Summary Most of the time when you think about a data pipeline or ETL job what comes to mind is a purely mechanistic progression of functions that move data from point A to point B. Sometimes, however, one of those transformations is actually a full-fledged machine learning project in its own right. In this episode Tal Galfsky explains how he and the team at Cherre tackled the problem of messy data for Addresses by building a natural language processing and entity resolution system that is served as an API to the rest of their pipelines. He discusses the myriad ways that addresses are incomplete, poorly formed, and just plain wrong, why it was a big enough pain point to invest in building an industrial strength solution for it, and how it actually works under the hood. After listening to this you’ll look at your data pipelines in a new light and start to wonder how you can bring more advanced strategies into the cleaning and transformation process.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Your host is Tobias Macey and today I’m interviewing Tal Galfsky about how Cherre is bringing order to the messy problem of physical addresses and entity resolution in their data pipelines.

Interview

Introduction How did you get involved in the area of data management? Started as physicist and evolved into Data Science Can you start by giving a brief recap of what Cherre is and the types of data that you deal with? Cherre is a company that connects data We’re not a data vendor, in that we don’t sell data, primarily We help companies connect and make sense of their data The real estate market is historically closed, gut let, behind on tech What are the biggest challenges that you deal with in your role when working with real estate data? Lack of a standard domain model in real estate. Ontology. What is a property? Each data source, thinks about properties in a very different way. Therefore, yielding similar, but completely different data. QUALITY (Even if the dataset are talking about the same thing, there are different levels of accuracy, freshness). HIREARCHY. When is one source better than another What are the teams and systems that rely on address information? Any company that needs to clean or organize (make sense) their data, need to identify, people, companies, and properties. Our clients use Address resolution in multiple ways. Via the UI or via an API. Our service is both external and internal so what I build has to be good enough for the demanding needs of our data science team, robust enough for our engineers, and simple enough that non-expert clients can use it. Can you give an example for the problems involved in entity resolution Known entity example. Empire state buidling. To resolve addresses in a way that makes sense for the client you need to capture the real world entities. Lots, buildings, units.

Identify the type of the object (lot, building, unit) Tag the object with all the relevant addresses Relations to other objects (lot, building, unit)

What are some examples of the kinds of edge cases or messiness that you encounter in addresses? First class is string problems. Second class component problems. third class is geocoding. I understand that you have developed a service for normalizing addresses and performing entity resolution to provide canonical references for downstream analyses. Can you give an overview of what is involved? What is the need for the service. The main requirement here is connecting an address to lot, building, unit with latitude and longitude coordinates

How were you satisfying this requirement previously? Before we built our model and dedicated service we had a basic prototype for pipeline only to handle NYC addresses. What were the motivations for designing and implementing this as a service? Need to expand nationwide and to deal with client queries in real time. What are some of the other data sources that you rely on to be able to perform this normalization and resolution? Lot data, building data, unit data, Footprints and address points datasets. What challenges do you face in managing these other sources of information? Accuracy, hirearchy, standardization, unified solution, persistant ids and primary keys

Digging into the specifics of your solution, can you talk through the full lifecycle of a request to resolve an address and the various manipulations that are performed on it? String cleaning, Parse and tokenize, standardize, Match What are some of the other pieces of information in your system that you would like to see addressed in a similar fashion? Our named entity solution with connection to knowledge graph and owner unmasking. What are some of the most interesting, unexpected, or challenging lessons that you learned while building this address resolution system? Scaling nyc geocode example. The NYC model was exploding a subset of the options for messing up an address. Flexibility. Dependencies. Client exposure. Now that you have this system running in production, if you were to start over today what would you do differently? a lot but at this point the module boundaries and client interface are defined in such way that we are able to make changes or completely replace any given part of it without breaking anything client facing What are some of the other projects that you are excited to work on going forward? Named entity resolution and Knowledge Graph

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today? BigQuery is huge asset and in particular UDFs but they don’t support API calls or python script

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Cherre

Podcast Episode

Photonics Knowledge Graph Entity Resolution BigQuery NLP == Natural Language Processing dbt

Podcast Episode

Airflow

Podcast.init Episode

Datadog

Podcast Episode

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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Summary A majority of the time spent in data engineering is copying data between systems to make the information available for different purposes. This introduces challenges such as keeping information synchronized, managing schema evolution, building transformations to match the expectations of the destination systems. H.O. Maycotte was faced with these same challenges but at a massive scale, leading him to question if there is a better way. After tasking some of his top engineers to consider the problem in a new light they created the Pilosa engine. In this episode H.O. explains how using Pilosa as the core he built the Molecula platform to eliminate the need to copy data between systems in able to make it accessible for analytical and machine learning purposes. He also discusses the challenges that he faces in helping potential users and customers understand the shift in thinking that this creates, and how the system is architected to make it possible. This is a fascinating conversation about what the future looks like when you revisit your assumptions about how systems are designed.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Your host is Tobias Macey and today I’m interviewing H.O. Maycotte about Molecula, a cloud based feature store based on the open source Pilosa project

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what you are building at Molecula and the story behind it?

What are the additional capabilities that Molecula offers on top of the open source Pilosa project?

What are the problems/use cases that Molecula solves for? What are some of the technologies or architectural patterns that Molecula might replace in a companies data platform? One of the use cases that is mentioned on the Molecula site is as a feature store for ML and AI. This is a category that has been seeing a lot of growth recently. Can you provide some context how Molecula fits in that market and how it compares to options such as Tecton, Iguazio, Feast, etc.?

What are the benefits of using a bitmap index for identifying and computing features?

Can you describe how the Molecula platform is architected?

How has the design and goal of Molecula changed or evolved since you first began working on it?

For someone who is using Molecula, can you describe the process of integrating it with their existing data sources? Can you describe the internal data model of Pilosa/Molecula?

How should users think about data modeling and architecture as they are loading information into the platform?

Once a user has data in Pilosa, what are the available mechanisms for performing analyses or feature engineering? What are some of the most underutilized or misunderstood capabilities of Molecula? What are some of the most interesting, unexpected, or innovative ways that you have seen the Molecula platform used? What are the most interesting, unexpected, or challenging lessons that you have learned from building and scaling Molecula? When is Molecula the wrong choice? What do you have planned for the future of the platform and business?

Contact Info

LinkedIn @maycotte on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Molecula Pilosa

Podcast Episode

The Social Dilemma Feature Store Cassandra Elasticsearch

Podcast Episode

Druid MongoDB SwimOS

Podcast Episode

Kafka Kafka Schema Registry

Podcast Episode

Homomorphic Encryption Lucene Solr

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Summary The process of building and deploying machine learning projects requires a staggering number of systems and stakeholders to work in concert. In this episode Yaron Haviv, co-founder of Iguazio, discusses the complexities inherent to the process, as well as how he has worked to democratize the technologies necessary to make machine learning operations maintainable.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Your host is Tobias Macey and today I’m interviewing Yaron Haviv about Iguazio, a platform for end to end automation of machine learning applications using MLOps principles.

Interview

Introduction How did you get involved in the area of data science & analytics? Can you start by giving an overview of what Iguazio is and the story of how it got started? How would you characterize your target or typical customer? What are the biggest challenges that you see around building production grade workflows for machine learning?

How does Iguazio help to address those complexities?

For customers who have already invested in the technical and organizational capacity for data science and data engineering, how does Iguazio integrate with their environments? What are the responsibilities of a data engineer throughout the different stages of the lifecycle for a machine learning application? Can you describe how the Iguazio platform is architected?

How has the design of the platform evolved since you first began working on it? How have the industry best practices around bringing machine learning to production changed?

How do you approach testing/validation of machine learning applications and releasing them to production environments? (e.g. CI/CD) Once a model is in

Summary As data professionals we have a number of tools available for storing, processing, and analyzing data. We also have tools for collaborating on software and analysis, but collaborating on data is still an underserved capability. Gavin Mendel-Gleason encountered this problem first hand while working on the Sesshat databank, leading him to create TerminusDB and TerminusHub. In this episode he explains how the TerminusDB system is architected to provide a versioned graph storage engine that allows for branching and merging of data sets, how that opens up new possibilities for individuals and teams to work together on building new data repositories. This is a fascinating conversation on the technical challenges involved, the opportunities that such as system provides, and the complexities inherent to building a successful business on open source.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Do you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to dataengineeringpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s dataengineeringpodcast.com/talkpython, and don’t forget to thank them for supporting the show. You invest so much in your data infrastructure – you simply can’t afford to settle for unreliable data. Fortunately, there’s hope: in the same way that New Relic, DataDog, and other Application Performance Management solutions ensure reliable software and keep application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo’s end-to-end Data Observability Platform monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact through lineage, and notify those who need to know before it impacts the business. By empowering data teams with end-to-end data reliability, Monte Carlo helps organizations save time, increase revenue, and restore trust in their data. Visit dataengineeringpodcast.com/montecarlo today to request a demo and see how Monte Carlo delivers data observability across your data infrastructure. The first 25 will receive a free, limited edition Monte Carlo hat! Your host is Tobias Macey and today I’m interviewing Gavin Mendel-Gleason about TerminusDB, an open source model driven graph database for knowledge graph representation

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what TerminusDB is and what motivated you to build it? What are the use cases that TerminusDB and TerminusHub are designed for? There are a number of different reasons and methods for versioning data, such as th

Summary As more organizations are gaining experience with data management and incorporating analytics into their decision making, their next move is to adopt machine learning. In order to make those efforts sustainable, the core capability they need is for data scientists and analysts to be able to build and deploy features in a self service manner. As a result the feature store is becoming a required piece of the data platform. To fill that need Kevin Stumpf and the team at Tecton are building an enterprise feature store as a service. In this episode he explains how his experience building the Michelanagelo platform at Uber has informed the design and architecture of Tecton, how it integrates with your existing data systems, and the elements that are required for well engineered feature store.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Do you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to dataengineeringpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s dataengineeringpodcast.com/talkpython, and don’t forget to thank them for supporting the show. You invest so much in your data infrastructure – you simply can’t afford to settle for unreliable data. Fortunately, there’s hope: in the same way that New Relic, DataDog, and other Application Performance Management solutions ensure reliable software and keep application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo’s end-to-end Data Observability Platform monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact through lineage, and notify those who need to know before it impacts the business. By empowering data teams with end-to-end data reliability, Monte Carlo helps organizations save time, increase revenue, and restore trust in their data. Visit dataengineeringpodcast.com/montecarlo today to request a demo and see how Monte Carlo delivers data observability across your data infrastructure. The first 25 will receive a free, limited edition Monte Carlo hat! Your host is Tobias Macey and today I’m interviewing Kevin Stumpf about Tecton and the role that the feature store plays in a modern MLOps platform

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Tecton and your motivation for starting the business? For anyone who isn’t familiar with the concept, what is an example of a feature? How do you define what a feature store is? What role does a feature store play in the overall lifecycle of a machine learning p

Summary Data governance is a term that encompasses a wide range of responsibilities, both technical and process oriented. One of the more complex aspects is that of access control to the data assets that an organization is responsible for managing. The team at Immuta has built a platform that aims to tackle that problem in a flexible and maintainable fashion so that data teams can easily integrate authorization, data masking, and privacy enhancing technologies into their data infrastructure. In this episode Steve Touw and Stephen Bailey share what they have built at Immuta, how it is implemented, and how it streamlines the workflow for everyone involved in working with sensitive data. If you are starting down the path of implementing a data governance strategy then this episode will provide a great overview of what is involved.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Feature flagging is a simple concept that enables you to ship faster, test in production, and do easy rollbacks without redeploying code. Teams using feature flags release new software with less risk, and release more often. ConfigCat is a feature flag service that lets you easily add flags to your Python code, and 9 other platforms. By adopting ConfigCat you and your manager can track and toggle your feature flags from their visual dashboard without redeploying any code or configuration, including granular targeting rules. You can roll out new features to a subset or your users for beta testing or canary deployments. With their simple API, clear documentation, and pricing that is independent of your team size you can get your first feature flags added in minutes without breaking the bank. Go to dataengineeringpodcast.com/configcat today to get 35% off any paid plan with code DATAENGINEERING or try out their free forever plan. You invest so much in your data infrastructure – you simply can’t afford to settle for unreliable data. Fortunately, there’s hope: in the same way that New Relic, DataDog, and other Application Performance Management solutions ensure reliable software and keep application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo’s end-to-end Data Observability Platform monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact through lineage, and notify those who need to know before it impacts the business. By empowering data teams with end-to-end data reliability, Monte Carlo helps organizations save time, increase revenue, and restore trust in their data. Visit dataengineeringpodcast.com/montecarlo today to request a demo and see how Monte Carlo delivers data observability across your data inf

Summary The core mission of data engineers is to provide the business with a way to ask and answer questions of their data. This often takes the form of business intelligence dashboards, machine learning models, or APIs on top of a cleaned and curated data set. Despite the rapid progression of impressive tools and products built to fulfill this mission, it is still an uphill battle to tie everything together into a cohesive and reliable platform. At Isima they decided to reimagine the entire ecosystem from the ground up and built a single unified platform to allow end-to-end self service workflows from data ingestion through to analysis. In this episode CEO and co-founder of Isima Darshan Rawal explains how the biOS platform is architected to enable ease of use, the challenges that were involved in building an entirely new system from scratch, and how it can integrate with the rest of your data platform to allow for incremental adoption. This was an interesting and contrarian take on the current state of the data management industry and is worth a listen to gain some additional perspective.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Follow go.datafold.com/dataengineeringpodcast to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. Are you bogged down by having to manually manage data access controls, repeatedly move and copy data, and create audit reports to prove compliance? How much time could you save if those tasks were automated across your cloud platforms? Immuta is an automated data governance solution that enables safe and easy data analytics in the cloud. Our comprehensive data-level security, auditing and de-identification features eliminate the need for time-consuming manual processes and our focus on data and compliance team collaboration empowers you to deliver quick and valuable data analytics on the most sensitive data to unlock the full potential of your cloud data platforms. Learn how we streamline and accelerate manual processes to help y

Summary In order for analytics and machine learning projects to be useful, they require a high degree of data quality. To ensure that your pipelines are healthy you need a way to make them observable. In this episode Barr Moses and Lior Gavish, co-founders of Monte Carlo, share the leading causes of what they refer to as data downtime and how it manifests. They also discuss methods for gaining visibility into the flow of data through your infrastructure, how to diagnose and prevent potential problems, and what they are building at Monte Carlo to help you maintain your data’s uptime.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Are you bogged down by having to manually manage data access controls, repeatedly move and copy data, and create audit reports to prove compliance? How much time could you save if those tasks were automated across your cloud platforms? Immuta is an automated data governance solution that enables safe and easy data analytics in the cloud. Our comprehensive data-level security, auditing and de-identification features eliminate the need for time-consuming manual processes and our focus on data and compliance team collaboration empowers you to deliver quick and valuable data analytics on the most sensitive data to unlock the full potential of your cloud data platforms. Learn how we streamline and accelerate manual processes to help you derive real results from your data at dataengineeringpodcast.com/immuta. Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Barr Moses and Lior Gavish about observability for your data pipelines and how they are addressing it at Monte Carlo.

Interview

Introduction How did you get involved in the area of data management? H

Summary Business intelligence efforts are only as useful as the outcomes that they inform. Power BI aims to reduce the time and effort required to go from information to action by providing an interface that encourages rapid iteration. In this episode Rob Collie shares his enthusiasm for the Power BI platform and how it stands out from other options. He explains how he helped to build the platform during his time at Microsoft, and how he continues to support users through his work at Power Pivot Pro. Rob shares some useful insights gained through his consulting work, and why he considers Power BI to be the best option on the market today for business analytics.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Are you bogged down by having to manually manage data access controls, repeatedly move and copy data, and create audit reports to prove compliance? How much time could you save if those tasks were automated across your cloud platforms? Immuta is an automated data governance solution that enables safe and easy data analytics in the cloud. Our comprehensive data-level security, auditing and de-identification features eliminate the need for time-consuming manual processes and our focus on data and compliance team collaboration empowers you to deliver quick and valuable data analytics on the most sensitive data to unlock the full potential of your cloud data platforms. Learn how we streamline and accelerate manual processes to help you derive real results from your data at dataengineeringpodcast.com/immuta. Equalum’s end to end data ingestion platform is relied upon by enterprises across industries to seamlessly stream data to operational, real-time analytics and machine learning environments. Equalum combines streaming Change Data Capture, replication, complex transformations, batch processing and full data management using a no-code UI. Equalum also leverages open source data frameworks by orchestrating Apache Spark, Kafka and others under the hood. Tool consolidation and linear scalability without the legacy platform price tag. Go to dataengineeringpodcast.com/equalum today to start a free 2 week test run of their platform, and don’t forget to tell them that we sent you. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Rob Collie about Microsoft’s Power BI platform and his

Summary Wind energy is an important component of an ecologically friendly power system, but there are a number of variables that can affect the overall efficiency of the turbines. Michael Tegtmeier founded Turbit Systems to help operators of wind farms identify and correct problems that contribute to suboptimal power outputs. In this episode he shares the story of how he got started working with wind energy, the system that he has built to collect data from the individual turbines, and how he is using machine learning to provide valuable insights to produce higher energy outputs. This was a great conversation about using data to improve the way the world works.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Michael Tegtmeier about Turbit, a machine learning powered platform for performance monitoring of wind farms

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Turbit and your motivation for creating the business? What are the most problematic factors that contribute to low performance in power generation with wind turbines? What is the current state of the art for accessing and analyzing data for wind farms? What information are you able to gather from the SCADA systems in the turbine?

How uniform is the availability and formatting of data from different manufacturers?

How are you handling data collection for the individual turbines?

How much information are you processing at the point of collection vs. sending to a centralized data store?

Can you describe the system architecture of Turbit and the lifecycle of turbine data as it propag

Summary We have machines that can listen to and process human speech in a variety of languages, but dealing with unstructured sounds in our environment is a much greater challenge. The team at Audio Analytic are working to impart a sense of hearing to our myriad devices with their sound recognition technology. In this episode Dr. Chris Mitchell and Dr. Thomas le Cornu describe the challenges that they are faced with in the collection and labelling of high quality data to make this possible, including the lack of a publicly available collection of audio samples to work from, the need for custom metadata throughout the processing pipeline, and the need for customized data processing tools for working with sound data. This was a great conversation about the complexities of working in a niche domain of data analysis and how to build a pipeline of high quality data from collection to analysis.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Dr. Chris Mitchell and Dr. Thomas le Cornu about Audio Analytic, a company that is building sound recognition technology that is giving machines a sense of hearing beyond speech and music

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Audio Analytic?

What was your motivation for building an AI platform for sound recognition?

What are some of the ways that your platform is being used? What are the unique challenges that you have faced in working with arbitrary sound data? How do you handle the collection and labelling of the source data that you rely on for building your models?

Beyond just collection and storage, what is your process for defining a taxonomy of the audio data that you are working with? How has the taxonomy had to evolve, and what assumptions have had to change, as you progressed in building the data set and the resulting models?

challenges of building an embeddable AI model

update cycle

difficulty of identifying relevant audio and dealing with literal noise in the input data rights and ownership challenges in collection of source data What was your design process for constructing a pipeline for the audio data that you need to process? Can you describe how your overall data management system is

Summary Machine learning is a process driven by iteration and experimentation which requires fast and easy access to relevant features of the data being processed. In order to reduce friction in the process of developing and delivering models there has been a recent trend toward building a dedicated feature. In this episode Simba Khadder discusses his work at StreamSQL building a feature store to make creation, discovery, and monitoring of features fast and easy to manage. He describes the architecture of the system, the benefits of streaming data for machine learning, and how a feature store provides a useful interface between data engineers and machine learning engineers to reduce communication overhead.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Your host is Tobias Macey and today I’m interviewing Simba Khadder about his views on the importance of ML feature stores, and his experience implementing one at StreamSQL

Interview

Introduction How did you get involved in the areas of machine learning and data management? What is StreamSQL and what motivated you to start the business? Can you describe what a machine learning feature is? What is the difference between generating features for training a model and generating features for serving? How is feature management typically handled today? What is a feature store and how is it different from the status quo? What is the overall lifecycle of identifying useful features, defining and generating them, using them for training, and then serving them in production? How does the usage of a feature store impact the workflow of ML engineers/data scientists and data engineers? What are the general requirements of a feature store? What additional capabilities or tangential services are necessary for providing a pleasant UX for a feature store?

How is discovery and documentation of features handled?

What is the current landscape of feature stores and how does StreamSQL compare? How is the StreamSQL feature store implemented?

How is the supporting infrastructure architected and how has it evolved since you first began working on it?

Why is streaming data such a focal point of feature stores? How do you generate features for training? How do you approach monitoring of features and what does remediation look like for a feature that is no longer valid? How do you handle versioning and deploying features? What’s the process for integrating data sources into StreamSQL for processing into features? How are the features materialized? What are the most challenging or complex aspects of working on or with a feature store? When is StreamSQL the wrong choice for a feature store? What are the most interesting, challenging, or unexpected lessons that you have learned in the process of building StreamSQL? What do you have planned for the future of the produ

Summary The landscape of data management and processing is rapidly changing and evolving. There are certain foundational elements that have remained steady, but as the industry matures new trends emerge and gain prominence. In this episode Astasia Myers of Redpoint Ventures shares her perspective as an investor on which categories she is paying particular attention to for the near to medium term. She discusses the work being done to address challenges in the areas of data quality, observability, discovery, and streaming. This is a useful conversation to gain a macro perspective on where businesses are looking to improve their capabilities to work with data.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar to get you up and running in no time. With simple pricing, fast networking, S3 compatible object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll. Your host is Tobias Macey and today I’m interviewing Astasia Myers about the trends in the data industry that she sees as an investor at Redpoint Ventures

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of Redpoint Ventures and your role there? From an investor perspective, what is most appealing about the category of data-oriented businesses? What are the main sources of information that you rely on to keep up to date with what is happening in the data industry?

What is your personal heuristic for determining the relevance of any given piece of information to decide whether it is worthy of further investigation?

As someone who works closely with a variety of companies across different industry verticals and different areas of focus, what are some of the common trends that you have identified in the data ecosystem? In your article that covers the trends you are keeping an eye on for 2020 you call out 4 in particular, data quality, data catalogs, observability of what influences critical business indicators, and streaming data. Taking those in turn:

What are the driving factors that influence data quality, and what elements of that problem space are being addressed by the companies you are watching?

What are the unsolved areas that you see as being viable for newcomers?

What are the challenges faced by businesses in establishing and maintaining data catalogs?

What approaches are being taken by the companies who are trying to solve this problem?

What shortcomings do you see in the available products?

For gaining visibility into the forces that impact the key performance indicators (KPI) of businesses, what is lacking in the current approaches?

What additional information needs to be tracked to provide the needed context for making informed decisions about what actions to take to improve KPIs? What challenges do businesses in this observability space face to provide useful access and analysis to this collected data?

Streaming is an area that has been growing rapidly over the past few years, with many open source and commercial options. What are the major business opportunities that you see to make streaming more accessible and effective?

What are the main factors that you see as driving this growth in the need for access to streaming data?

With your focus on these trends, how does that influence your investment decisions and where you spend your time? What are the unaddressed markets or product categories that you see which would be lucrative for new businesses? In most areas of technology now there is a mix of open source and commercial solutions to any given problem, with varying levels of maturity and polish between them. What are your views on the balance of this relationship in the data ecosystem?

For data in particular, there is a strong potential for vendor lock-in which can cause potential customers to avoid adoption of commercial solutions. What has been your experience in that regard with the companies that you work with?

Contact Info

@AstasiaMyers on Twitter @astasia on Medium LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Redpoint Ventures 4 Data Trends To Watch in 2020 Seagate Western Digital Pure Storage Cisco Cohesity Looker

Podcast Episode

DGraph

Podcast Episode

Dremio

Podcast Episode

SnowflakeDB

Podcast Episode

Thoughspot Tibco Elastic Splunk Informatica Data Council DataCoral Mattermost Bitwarden Snowplow

Podcast Interview Interview About Snowplow Infrastructure

CHAOSSEARCH

Podcast Episode

Kafka Streams Pulsar

Podcast Interview Followup Podcast Interview

Soda Toro Great Expectations Alation Collibra Amundsen DataHub Netflix Metacat Marquez

Podcast Episode

LDAP == Lightweight Directory Access Protocol Anodot Databricks Flink

a…

Summary Data lakes offer a great deal of flexibility and the potential for reduced cost for your analytics, but they also introduce a great deal of complexity. What used to be entirely managed by the database engine is now a composition of multiple systems that need to be properly configured to work in concert. In order to bring the DBA into the new era of data management the team at Upsolver added a SQL interface to their data lake platform. In this episode Upsolver CEO Ori Rafael and CTO Yoni Iny describe how they have grown their platform deliberately to allow for layering SQL on top of a robust foundation for creating and operating a data lake, how to bring more people on board to work with the data being collected, and the unique benefits that a data lake provides. This was an interesting look at the impact that the interface to your data can have on who is empowered to work with it.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll. Your host is Tobias Macey and today I’m interviewing Ori Rafael and Yoni Iny about building a data lake for the DBA at Upsolver

Interview

Introduction How did you get involved in the area of data management? Can you start by sharing your definition of what a data lake is and what it is comprised of? We talked last in November of 2018. How has the landscape of data lake technologies and adoption changed in that time?

How has Upsolver changed or evolved since we last spoke?

How has the evolution of the underlying technologies impacted your implementation and overall product strategy?

What are some of the common challenges that accompany a data lake implementation? How do those challenges influence the adoption or viability of a data lake? How does the introduction of a universal SQL layer change the staffing requirements for building and maintaining a data lake?

What are the advantages of a data lake over a data warehouse if everything is being managed via SQL anyway?

What are some of the underlying realities of the data systems that power the lake which will eventually need to be understood by the operators of the platform? How is the SQL layer in Upsolver implemented?

What are the most challenging or complex aspects of managing the underlying technologies to provide automated partitioning, indexing, etc.?

What are the main concepts that you need to educate your customers on? What are some of the pitfalls that users should be aware of? What features of your platform are often overlooked or underutilized which you think should be more widely adopted? What have you found to be the most interesting, unexpected, or challenging lessons learned while building the technical and business elements of Upsolver? What do you have planned for the future?

Contact Info

Ori

LinkedIn

Yoni

yoniiny on GitHub LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Upsolver

Podcast Episode

DBA == Database Administrator IDF == Israel Defense Forces Data Lake Eventual Consistency Apache Spark Redshift Spectrum Azure Synapse Analytics SnowflakeDB

Podcast Episode

BigQuery Presto

Podcast Episode

Apache Kafka Cartesian Product kSQLDB

Podcast Episode

Eventador

Podcast Episode

Materialize

Podcast Episode

Common Table Expressions Lambda Architecture Kappa Architecture Apache Flink

Podcast Episode

Reinforcement Learning Cloudformation GDPR

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Summary Gaining a complete view of the customer journey is especially difficult in B2B companies. This is due to the number of different individuals involved and the myriad ways that they interface with the business. Dreamdata integrates data from the multitude of platforms that are used by these organizations so that they can get a comprehensive view of their customer lifecycle. In this episode Ole Dallerup explains how Dreamdata was started, how their platform is architected, and the challenges inherent to data management in the B2B space. This conversation is a useful look into how data engineering and analytics can have a direct impact on the success of the business.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll. Your host is Tobias Macey and today I’m interviewing Ole Dallerup about Dreamdata, a platform for simplifying data integration for B2B companies

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Dreamata?

What was your inspiration for starting a company and what keeps you motivated?

How do the data requirements differ between B2C and B2B companies? What are the challenges that B2B companies face in gaining visibility across the lifecycle of their customers?

How does that lack of visibility impact the viability or growth potential of the business? What are the factors that contribute to silos in visibility of customer activity within a business?

What are the data sources that you are dealing with to generate meaningful analytics for your customers? What are some of the challenges that business face in either generating or collecting useful informati

Summary The PostgreSQL database is massively popular due to its flexibility and extensive ecosystem of extensions, but it is still not the first choice for high performance analytics. Swarm64 aims to change that by adding support for advanced hardware capabilities like FPGAs and optimized usage of modern SSDs. In this episode CEO and co-founder Thomas Richter discusses his motivation for creating an extension to optimize Postgres hardware usage, the benefits of running your analytics on the same platform as your application, and how it works under the hood. If you are trying to get more performance out of your database then this episode is for you!

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to dataengineeringpodcast.com/tidydata today and get started for free with no credit card required. Your host is Tobias Macey and today I’m interviewing Thomas Richter about Swarm64, a PostgreSQL extension to improve parallelism and add support for FPGAs

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Swarm64 is?

How did the business get started and what keeps you motivated?

What are some of the common bottlenecks that users of postgres run into? What are the use cases and workloads that gain the most benefit from increased parallelism in the database engine? By increasing the processing throughput of the database, how does that impact disk I/O and what are some options for avoiding bottlenecks in the persistence layer? Can you describe how Swarm64 is implemented?

How has the product evolved since you first began working on it?

How has the evolution of postgres impacted your product direction?

What are some of the notable challenges that you have dealt with as a result of upstream changes in postgres?

How has the hardware landscape evolved and how does that affect your prioritization of features and improvements? What are some of the other extensions in the postgres ecosystem that are most commonly used alongside Swarm64?

Which extensions conflict with yours and how does that impact potential adoption?

In addition to your work to optimize performance of the postres engine, you also provide support for using an FPGA as a co-processor. What are the benefits that an FPGA provides over and above a CPU or GPU architecture?

What are the available options for provisioning hardware in a datacenter or the cloud that has access to an FPGA? Most people are familiar with the relevant attributes for selecting a CPU or GPU, what are the specifications that they should be looking at when selecting an FPGA?

For users who are adopting Swarm64, how does it impact the way they should be thinking of their data models? What is involved in migrating an existing database to use Swarm64? What are some of the most interesting, unexpected, or

Summary There have been several generations of platforms for managing streaming data, each with their own strengths and weaknesses, and different areas of focus. Pulsar is one of the recent entrants which has quickly gained adoption and an impressive set of capabilities. In this episode Sijie Guo discusses his motivations for spending so much of his time and energy on contributing to the project and growing the community. His most recent endeavor at StreamNative is focused on combining the capabilities of Pulsar with the cloud native movement to make it easier to build and scale real time messaging systems with built in event processing capabilities. This was a great conversation about the strengths of the Pulsar project, how it has evolved in recent years, and some of the innovative ways that it is being used. Pulsar is a well engineered and robust platform for building the core of any system that relies on durable access to easily scalable streams of data.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to dataengineeringpodcast.com/tidydata today and get started for free with no credit card required. Your host is Tobias Macey and today I’m interviewing Sijie Guo about the current state of the Pulsar framework for stream processing and his experiences building a managed offering for it at StreamNative

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what Pulsar is?

How did you get involved with the project?

What is Pulsar’s role in the lifecycle of data and where does it fit in the overall ecosystem of data tools? How has the Pulsar project evolved or changed over the past 2 years?

How has the overall state of the ecosystem influenced the direction that Pulsar has taken?

One of the critical elements in the success of a piece of technology is the ecosystem that grows around it. How has the community responded to Pulsar, and what are some of the barriers to adoption?

How are you and other project leaders addressing those barriers?

You were a co-founder at Streamlio, which was built on top of Pulsar, and now you have founded StreamNative to offer Pulsar as a service. What did you learned from your time at Streamlio that has been most helpful in your current endeavor?

How would you characterize your relationship with the project and community in each role?

What motivates you to dedicate so much of your time and enery to Pulsar in particular, and the streaming data ecosystem in general?

Why is streaming data such an important capability? How have projects such as Kafka and Pulsar impacted the broader software and data landscape?

What are some of the most interesting, innovative, or unexpected ways that you have seen Pulsar used? When is Pulsar the wrong choice? What do you have planned for the future of S

Summary Data management is hard at any scale, but working in the context of an enterprise organization adds even greater complexity. Infoworks is a platform built to provide a unified set of tooling for managing the full lifecycle of data in large businesses. By reducing the barrier to entry with a graphical interface for defining data transformations and analysis, it makes it easier to bring the domain experts into the process. In this interview co-founder and CTO of Infoworks Amar Arsikere explains the unique challenges faced by enterprise organizations, how the platform is architected to provide the needed flexibility and scale, and how a unified platform for data improves the outcomes of the organizations using it.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Free yourself from maintaining brittle data pipelines that require excessive coding and don’t operationally scale. With the Ascend Unified Data Engineering Platform, you and your team can easily build autonomous data pipelines that dynamically adapt to changes in data, code, and environment — enabling 10x faster build velocity and automated maintenance. On Ascend, data engineers can ingest, build, integrate, run, and govern advanced data pipelines with 95% less code. Go to dataengineeringpodcast.com/ascend to start building with a free 30-day trial. You’ll partner with a dedicated data engineer at Ascend to help you get started and accelerate your journey from prototype to production. Your host is Tobias Macey and today I’m interviewing Amar Arsikere about the Infoworks platform for enterprise data operations and orchestration

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you have built at Infoworks and the story of how it got started? What are the fundamental challenges that often plague organizations dealing with "big data"?

How do those challenges change or compound in the context of an enterprise organization? What are some of the unique needs that enterprise organizations have of their data?

What are the design or technical limitations of existing big data technologies that contribute to the overall difficulty of using or integrating them effectively? What are some of the tools or platforms that InfoWorks replaces in the overall data lifecycle?

How do you identify and prioritize the integrations that you build?

How is Infoworks itself architected and how has it evolved since you first built it? Discoverability and reuse of data is one of the biggest challenges facing organizations of all sizes. How do you address that in your platform? What are the roles that use InfoWorks in their day-to-day?

What does the workflow look like for each of those roles?

Can you talk through the overall lifecycle of a unit of data in InfoWorks and the different subsystems that it interacts with at each stage? What are some of the design challenges that you face in building a UI oriented workflow while providing the necessary level of control for these systems?

How do you handle versioning of pipelines and validation of new iterations prior to production release? What are the cases where the no code, graphical paradigm for data orchestration breaks down

Summary Data is a critical element to every role in an organization, which is also what makes managing it so challenging. With so many different opinions about which pieces of information are most important, how it needs to be accessed, and what to do with it, many data projects are doomed to failure. In this episode Chris Bergh explains how taking an agile approach to delivering value can drive down the complexity that grows out of the varied needs of the business. Building a DataOps workflow that incorporates fast delivery of well defined projects, continuous testing, and open lines of communication is a proven path to success.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! If DataOps sounds like the perfect antidote to your pipeline woes, DataKitchen is here to help. DataKitchen’s DataOps Platform automates and coordinates all the people, tools, and environments in your entire data analytics organization – everything from orchestration, testing and monitoring to development and deployment. In no time, you’ll reclaim control of your data pipelines so you can start delivering business value instantly, without errors. Go to dataengineeringpodcast.com/datakitchen today to learn more and thank them for supporting the show! Your host is Tobias Macey and today I’m welcoming back Chris Bergh to talk about ways that DataOps principles can help to reduce organizational complexity

Interview

Introduction How did you get involved in the area of data management? How are typical data and analytic teams organized? What are their roles and structure? Can you start by giving an outline of the ways that complexity can manifest in a data organization?

What are some of the contributing factors that generate this complexity? How does the size or scale of an organization and their data needs impact the segmentation of responsibilities and roles?

How does this organizational complexity play out within a single team? For example between data engineers, data scientists, and production/operations? How do you approach the definition of useful interfaces between different roles or groups within an organization?

What are your thoughts on the relationship between the multivariate complexities of data and analytics workflows and the software trend toward microservices as a means of addressing the challenges of organizational communication patterns in the software lifecycle?

How does this organizational complexity play out between multiple teams? For example between centralized data team and line of business self service teams? Isn’t organizational complexity just ‘the way it is’? Is there any how in getting out of meetings and inter team conflict? What are some of the technical elements that are most impactful in reducing the time to delivery for different roles? What are some strategies that you have found to be useful for maintaining a connection to the business need throughout the different stages of the data lifecycle? What are some of the signs or symptoms of problematic complexity that individuals and organizations should keep an eye out for? What role can automated testing play in improving this process? How do the current set of tools contribute to the fragmentation of data wor

Summary Modern applications frequently require access to real-time data, but building and maintaining the systems that make that possible is a complex and time consuming endeavor. Eventador is a managed platform designed to let you focus on using the data that you collect, without worrying about how to make it reliable. In this episode Eventador Founder and CEO Kenny Gorman describes how the platform is architected, the challenges inherent to managing reliable streams of data, the simplicity offered by a SQL interface, and the interesting projects that his customers have built on top of it. This was an interesting inside look at building a business on top of open source stream processing frameworks and how to reduce the burden on end users.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Your host is Tobias Macey and today I’m interviewing Kenny Gorman about the Eventador streaming SQL platform

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what the Eventador platform is and the story behind it?

How has your experience at ObjectRocket influenced your approach to streaming SQL? How do the capabilities and developer experience of Eventador compare to other streaming SQL engines such as ksqlDB, Pulsar SQL, or Materialize?

What are the main use cases that you are seeing people use for streaming SQL?

How does it fit into an application architecture? What are some of the design changes in the different layers that are necessary to take advantage of the real time capabilities?

Can you describe how the Eventador platform is architected?

How has the system design evolved since you first began working on it? How has the overall landscape of streaming systems changed since you first began working on Eventador? If you were to start over today what would you do differently?

What are some of the most interesting and challenging operational aspects of running your platform? What are some of the ways that you have modified or augmented the SQL dialect that you support?

What is the tipping point for when SQL is insufficient for a given task and a user might want to leverage Flink?

What is the workflow for developing and deploying different SQL jobs?

How do you handle versioning of the queries and integration with the software development lifecycle?

What are some data modeling considerations that users should be aware of?

What are some of the sharp edges or design pitfalls that users should be aware of?

What are some of the most interesting, innovative, or unexpected ways that you have seen your customers use your platform? What are some of the most interesting, unexpected, or challenging lessons that you have learned in the process of building and scaling Eventador? What do you have planned for the future of the platform?

Contact Info

LinkedIn Blog @kennygorman on Twitter kgorman on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit t

Summary The software applications that we build for our businesses are a rich source of data, but accessing and extracting that data is often a slow and error-prone process. Rookout has built a platform to separate the data collection process from the lifecycle of your code. In this episode, CTO Liran Haimovitch discusses the benefits of shortening the iteration cycle and bringing non-engineers into the process of identifying useful data. This was a great conversation about the importance of democratizing the work of data collection.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Your host is Tobias Macey and today I’m interviewing Liran Haimovitch, CTO of Rookout, about the business value of operations metrics and other dark data in your organization

Interview

Introduction How did you get involved in the area of data management? Can you start by describing the types of data that we typically collect for the systems operations context?

What are some of the business questions that can be answered from these data sources?

What are some of the considerations that developers and operations engineers need to be aware of when they are defining the collection points for system metrics and log messages?

What are some effective strategies that you have found for including business stake holders in the process of defining these collection points?

One of the difficulties in building useful analyses from any source of data is maintaining the appropriate context. What are some of the necessary metadata that should be maintained along with operational metrics?

What are some of the shortcomings in the systems we design and use for operational data stores in terms of making the collected data useful for other purposes?

How does the existing tooling need to be changed or augmented to simplify the collaboration between engineers and stake holders for defining and collecting the needed information? The types of systems that we use for collecting and analyzing operations metrics are often designed and optimized for different access patterns and data formats than those used for analytical and exploratory purposes. What are your thoughts on how to incorporate the collected metrics with behavioral data? What are some of the other sources of dark data that we should keep an eye out for in our organizations?

Contact Info

LinkedIn @Liran_Last on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Rookout Cybersecurity DevOps DataDog Graphite Elasticsearch Logz.io Kafka

The intro and o

Summary Knowledge graphs are a data resource that can answer questions beyond the scope of traditional data analytics. By organizing and storing data to emphasize the relationship between entities, we can discover the complex connections between multiple sources of information. In this episode John Maiden talks about how Cherre builds knowledge graphs that provide powerful insights for their customers and the engineering challenges of building a scalable graph. If you’re wondering how to extract additional business value from existing data, this episode will provide a way to expand your data resources.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on great conferences. We have partnered with organizations such as ODSC, and Data Council. Upcoming events include ODSC East which has gone virtual starting April 16th. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing John Maiden about how Cherre is building and using a knowledge graph of commercial real estate information

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Cherre is and the role that data plays in the business? What are the benefits of a knowledge graph for making real estate investment decisions? What are the main ways that you and your customers are using the knowledge graph?

What are some of the challenges that you face in providing a usable interface for end-users to query the graph?

What technology are you using for storing and processing the graph?

What challenges do you face in scaling the complexity and analysis of the graph?

What are the main sources of data for the knowledge graph? What are some of the ways that messiness manifests in the data that you are using to populate the graph?

How are you managing cleaning of the data and how do you identify and process records that can’t be coerced into the desired structure? How do you handle missing attributes or extra attributes in a given record?

How did you approach the process of determining an effective taxonomy for records in the graph? What is involved in performing entity extraction on your data? What are some of the most interesting or unexpected questions that you have been able to ask and answer with the graph? What are some of the most interesting/unexpected/challenging lessons that you have learned in the process of working with this data? What are some of the near and medium term improvements that you have planned for your knowledge graph? What advice do you have for anyone who is interested in building a knowledge graph of their own?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for liste

Summary Building and maintaining a system that integrates and analyzes all of the data for your organization is a complex endeavor. Operating on a shoe-string budget makes it even more challenging. In this episode Tyler Colby shares his experiences working as a data professional in the non-profit sector. From managing Salesforce data models to wrangling a multitude of data sources and compliance challenges, he describes the biggest challenges that he is facing.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on great conferences. We have partnered with organizations such as ODSC, and Data Council. Upcoming events include the Observe 20/20 virtual conference and ODSC East which has also gone virtual. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Tyler Colby about his experiences working as a data professional in the non-profit arena, most recently at the Natural Resources Defense Council

Interview

Introduction How did you get involved in the area of data management? Can you start by describing your responsibilities as the director of data infrastructure at the NRDC? What specific challenges are you facing at the NRDC? Can you describe some of the types of data that you are working with at the NRDC?

What types of systems are you relying on for the source of your data?

What kinds of systems have you put in place to manage the data needs of the NRDC?

What are your biggest influences in the build vs. buy decisions that you make? What heuristics or guidelines do you rely on for aligning your work with the business value that it will produce and the broader mission of the organization?

Have you found there to be any extra scrutiny of your work as a member of a non-profit in terms of regulations or compliance questions? Your career has involved a significant focus on the Salesforce platform. For anyone not familiar with it, what benefits does it provide in managing information flows and analysis capabilities?

What are some of the most challenging or complex aspects of working with Saleseforce?

In light of the current global crisis posed by COVID-19 you have established a new non-profit entity to organize the efforts of various technical professionals. Can you describe the nature of that mission?

What are some of the unique data challenges that you anticipate or have already encountered? How do the data challenges of this new organization compare to your past experiences?

What have you found to be most useful or beneficial in the current landscape of data management systems and practices in your career with non-profit organizations?

What are the areas that need to be addressed or improved for workers in the non-profit sector?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap

Summary There are a number of platforms available for object storage, including self-managed open source projects. But what goes on behind the scenes of the companies that run these systems at scale so you don’t have to? In this episode Will Smith shares the journey that he and his team at Linode recently completed to bring a fast and reliable S3 compatible object storage to production for your benefit. He discusses the challenges of running object storage for public usage, some of the interesting ways that it was stress tested internally, and the lessons that he learned along the way.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Will Smith about his work on building object storage for the Linode cloud platform

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of the current state of your object storage product?

What was the motivating factor for building and managing your own object storage system rather than building an integration with another offering such as Wasabi or Backblaze?

What is the scale and scope of usage that you had to design for? Can you describe how your platform is implemented?

What was your criteria for deciding whether to use an available platform such as Ceph or MinIO vs building your own from scratch? How have your initial assumptions about the operability and maintainability of your installation been challenged or updated since it has been released to the public?

What have been the biggest challenges that you have faced in designing and deploying a system that can meet the scale and reliability requirements of Linode? What are the most important capabilities for the underlying hardware that you are running on? What supporting systems and tools are you using to manage the availability and durability of your object storage? How did you approach the rollout of Linode’s object storage to gain the confidence that you needed to feel comfortable with full scale usage? What are some of the benefits that you have gained internally at Linode from having an object storage system available to your product teams? What are your thoughts on the state of the S3 API as a de facto standard for object storage? What is your main focus now that object storage is being rolled out to more data centers?

Contact Info

Dorthu on GitHub dorthu22 on Twitter LinkedIn Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Linode Object Storage Xen Hypervisor KVM (Linux K

Summary CouchDB is a distributed document database built for scale and ease of operation. With a built-in synchronization protocol and a HTTP interface it has become popular as a backend for web and mobile applications. Created 15 years ago, it has accrued some technical debt which is being addressed with a refactored architecture based on FoundationDB. In this episode Adam Kocoloski shares the history of the project, how it works under the hood, and how the new design will improve the project for our new era of computation. This was an interesting conversation about the challenges of maintaining a large and mission critical project and the work being done to evolve it.

Announcements

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Interview

Introduction How did you get involved in the area of data management? Can you starty by describing what CouchDB is?

How did you get involved in the CouchDB project and what is your current role in the community?

What are the use cases that it is well suited for? Can you share some of the history of CouchDB and its role in the NoSQL movement? How is CouchDB currently architected and how has it evolved since it was first introduced? What have been the benefits and challenges of Erlang as the runtime for CouchDB? How is the current storage engine implemented and what are its shortcomings? What problems are you trying to solve by replatforming on a new storage layer?

What were the selection criteria for the new storage engine and how did you structure the decision making process? What was the motivation for choosing FoundationDB as opposed to other options such as rocksDB, levelDB, etc.?

How is the adoption of FoundationDB going to impact the overall architecture and implementation of CouchDB? How will the use of FoundationDB impact the way that the current capabilities are implemented, such as data replication? What will the migration path be for people running an existing installation? What are some of the biggest challenges that you are facing in rearchitecting the codebase? What new capabilities will the FoundationDB storage layer enable? What are some of the most interesting/unexpected/innovative ways that you have seen CouchDB used?

What new capabilities or use cases do you anticipate once this migration is complete?

What are some of the most interesting/unexpected/challenging lessons that you have learned while working with the CouchDB project and community? What is in store for the future of CouchDB?

Contact Info

LinkedIn @kocolosk on Twitter kocolosk on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Apache CouchDB FoundationDB

Podcast Episode

IBM Cloudant Experimental Particle Physics FPGA == Field Programmable Gate Array Apache Software Foundation CRDT == Conflict-free Replicated Data Type

Podcast Episode

Erlang Riak RabbitMQ Heisenbug Kubernetes Property Based Testing

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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