Summary The database is the core of any system because it holds the data that drives your entire experience. We spend countless hours designing the data model, updating engine versions, and tuning performance. But how confident are you that you have configured it to be as performant as possible, given the dozens of parameters and how they interact with each other? Andy Pavlo researches autonomous database systems, and out of that research he created OtterTune to find the optimal set of parameters to use for your specific workload. In this episode he explains how the system works, the challenge of scaling it to work across different database engines, and his hopes for the future of database systems.

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! 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. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. Your host is Tobias Macey and today I’m interviewing Andy Pavlo about OtterTune, a system to continuously monitor and improve database performance via machine learning

Interview

Introduction How did you get involved in the area of data management? Can you describe what OtterTune is and the story behind it?

How does it relate to your work with NoisePage?

What are the challenges that database administrators, operators, and users run into when working with, configuring, and tuning transactional systems?

What are some of the contributing factors to the sprawling complexity of the configurable parameters for these databases?

Can you describe how OtterTune is implemented?

What are some of the aggregate benefits that OtterTune can gain by running as a centralized service and learning from all of the systems that it connects to? What are some of the assumptions that you made when starting the commercialization of this technology that have been challenged or invalidated as you began working with initial customers? How have the design and goals of the system changed or evolved since you first began working on it?

What is involved in adding support for a new database engine?

How applicable are the OtterTune capabilities to analyti

Summary Working with unstructured data has typically been a motivation for a data lake. The challenge is imposing enough order on the platform to make it useful. Kirk Marple has spent years working with data systems and the media industry, which inspired him to build a platform for automatically organizing your unstructured assets to make them more valuable. In this episode he shares the goals of the Unstruk Data Warehouse, how it is architected to extract asset metadata and build a searchable knowledge graph from the information, and the myriad ways that the system can be used. If you are wondering how to deal with all of the information that doesn’t fit in your databases or data warehouses, 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 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! Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Kirk Marple about Unstruk Data, a company that is building a data warehouse for unstructured data that ofers automated data preparation via metadata enrichment, integrated compute, and graph-based search

Interview

Introduction How did you get involved in the area of data management? Can you describe what Unstruk Data is and the story behind it? What would you classify as "unstructured data"?

What are some examples of industries that rely on large or varied sets of unstructured data? What are the challenges for analytics that are posed by the different categories of unstructured data?

What is the current state of the industry for working with unstructured data?

What are the unique capabilities that Unstruk provides and how does it integrate with the rest of the ecosystem? Where does it sit in the overall landscape of data tools?

Can you describe how the Unstruk data warehouse is implemented?

What are the assumptions that you had at the start of this project that have been challenged as you started working through the technical implementation and customer trials? How has the design and architecture evolved or changed since you began working on it?

How do you handle versioning of data, give

Summary When you build a machine learning model, the first step is always to load your data. Typically this means downloading files from object storage, or querying a database. To speed up the process, why not build the model inside the database so that you don’t have to move the information? In this episode Paige Roberts explains the benefits of pushing the machine learning processing into the database layer and the approach that Vertica has taken for their implementation. If you are looking for a way to speed up your experimentation, or an easy way to apply AutoML then this conversation 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 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! 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. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. Your host is Tobias Macey and today I’m interviewing Paige Roberts about machine learning workflows inside the database

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 the market for databases that support in-process machine learning?

What are the motivating factors for running a machine learning workflow inside the database?

What styles of ML are feasible to do inside the database? (e.g. bayesian inference, deep learning, etc.) What are the performance implications of running a model training pipeline within the database runtime? (both in terms of training performance boosts, and database performance impacts) Can you describe the architecture of how the machine learning process is managed by the database engine? How do you manage interacting with Python/R/Jupyter/etc. when working within the database? What is the impact on data pipeline and MLOps architectures when using the database to manage the machine learning workflow? What are the most interesting, innovative, or unexpected ways that you have seen in-database ML used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on machine learning inside the database? When is in-database ML the wrong choice? What are the recent trends/

Summary Google pioneered an impressive number of the architectural underpinnings of the broader big data ecosystem. Now they offer the technologies that they run internally to external users of their cloud platform. In this episode Lak Lakshmanan enumerates the variety of services that are available for building your various data processing and analytical systems. He shares some of the common patterns for building pipelines to power business intelligence dashboards, machine learning applications, and data warehouses. If you’ve ever been overwhelmed or confused by the array of services available in the Google Cloud Platform 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 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! Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Lak Lakshmanan about the suite of services for data and analytics in Google Cloud Platform.

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of the tools and products that are offered as part of Google Cloud for data and analytics?

How do the various systems relate to each other for building a full workflow? How do you balance the need for clean integration between services with the need to make them useful in isolation when used as a single component of a data platform?

What have you found to be the primary motivators for customers who are adopting GCP for some or all of their data workloads? What are some of the challenges that new users of GCP encounter when working with the data and analytics products that it offers? What are the systems that you have found to be easiest to work with?

Which are the most challenging to work with, whether due to the kinds of problems that they are solving for, or due to their user experience design?

How has your work with customers fed back into the products that you are building on top of? What are some examples of architectural or software patterns that are unique to the GCP product suite? What are the most interesting, innovative, or unexpected ways that y

Summary The way to build maintainable software and systems is through composition of individual pieces. By making those pieces high quality and flexible they can be used in surprising ways that the original creators couldn’t have imagined. One such component that has gone above and beyond its originally envisioned use case is BookKeeper, a distributed storage system that is optimized for durability and speed. In this episode Matteo Merli shares the story behind the creation of BookKeeper, the various ways that it is being used today, and the architectural aspects that make it such a strong building block for projects such as Pulsar. He also shares some of the other interesting systems that have been built on top of it and an amusing war story of running it at scale in its early years.

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! 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. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. Your host is Tobias Macey and today I’m interviewing Matteo Merli about Apache BookKeeper, a scalable, fault-tolerant, and low-latency storage service optimized for real-time workloads

Interview

Introduction How did you get involved in the area of data management? Can you describe what BookKeeper is and the story behind it? What are the most notable features/capabilities of BookKeeper? What are some of the ways that BookKeeper is being used? How has your work on Pulsar influenced the features and product direction of BookKeeper? Can you describe the architecture of a BookKeeper cluster?

How have the design and goals of BookKeeper changed or evolved over time?

What is the impact of record-oriented storage on data distribution/allocation within the cluster when working with variable record sizes? What are some of the operational considerations that users should be aware of? What are some of the most interesting/compelling features from your perspective? What are some of the most often overlooked or misunderstood capabilities of BookKeeper? What are the most interesting, innovative, or unexpected ways that you have seen BookKeeper used? What

Summary SQL is the most widely used language for working with data, and yet the tools available for writing and collaborating on it are still clunky and inefficient. Frustrated with the lack of a modern IDE and collaborative workflow for managing the SQL queries and analysis of their big data environments, the team at Pinterest created Querybook. In this episode Justin Mejorada-Pier and Charlie Gu share the story of how the initial prototype for a data catalog ended up as one of their most widely used interfaces to their analytical data. They also discuss the unique combination of features that it offers, how it is implemented, and the path to releasing it as open source. Querybook is an impressive and unique piece of technology that is well worth exploring, so listen and try it out today.

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! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Justin Mejorada-Pier and Charlie Gu about Querybook, an open source IDE for your big data projects

Interview

Introduction How did you get involved in the area of data management? Can you describe what Querybook is and the story behind it? What are the main use cases or workflows that Querybook is designed for?

What are the shortcomings of dashboarding/BI tools that make something like Querybook necessary?

The tag line calls out the fact that Querybook is an IDE for "big data". What are the manifestations of that focus in the feature set and user experience? Who are the target users of Querybook and how does that inform the feature priorities and user experience? Can you describe how Querybook is architected?

How have the goals and design changed or evolved since you first began working on it? What were some of the assumptions or design choices that you had to unwind in the process of open sourcing it?

What is the workflow for someone building a DataDoc with Querybook?

What is the experience of working as a collaborator on an analysis?

How do you handle lifecycle management of query results? What are your thoughts on the potential for extending Querybook beyond SQL-oriented analysis and integrating something like Jupyter kernels? What are the most interesting, innovative, or unexpected ways that you have seen Querybook used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Querybook? When is Querybook the wrong choice? What do you have planned for the future of Querybook?

Contact Info

Justin

Link

Summary Every part of the business relies on data, yet only a small team has the context and expertise to build and maintain workflows and data pipelines to transform, clean, and integrate it. In order for the true value of your data to be realized without burning out your engineers you need a way for everyone to get access to the information they care about. To help make that a more tractable problem Blake Burch co-founded Shipyard. In this episode he explains the utility of a low code solution that lets non engineers create their own self-serve pipelines, how the Shipyard platform is designed to make that possible, and how it allows engineers to create reusable tasks to satisfy the specific needs of the business. This is an interesting conversation about how to make data more accessible and more useful by improving the user experience of the tools that we create.

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! 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. When it comes to serving data for AI and ML projects, do you feel like you have to rebuild the plane while you’re flying it across the ocean? Molecula is an enterprise feature store that operationalizes advanced analytics and AI in a format designed for massive machine-scale projects without having to manage endless one-off information requests. With Molecula, data engineers manage one single feature store that serves the entire organization with millisecond query performance whether in the cloud or at your data center. And since it is implemented as an overlay, Molecula doesn’t disrupt legacy systems. High-growth startups use Molecula’s feature store because of its unprecedented speed, cost savings, and simplified access to all enterprise data. From feature extraction to model training to production, the Molecula feature store provides continuously updated feature access, reuse, and sharing without the need to pre-process data. If you need to deliver unprecedented speed, cost savings, and simplified access to large scale, real-time data, visit dataengineeringpodcast.com/molecula and request a demo. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt. Your host is Tobias Macey and today I’m interviewing Blake Burch about Shipyard, and his mission to create the easiest way for data teams to launch, monitor, and share resilient pipelines with less engineering

Interview

Introduction How did you get involved in the area of data management? Can you describe what you are building at Shipyard and the story behind it? What are the main goals that you have for Shipyard?

How does it compare to other data orchestration frameworks in the market?

Who are

Summary The data warehouse has become the focal point of the modern data platform. With increased usage of data across businesses, and a diversity of locations and environments where data needs to be managed, the warehouse engine needs to be fast and easy to manage. Yellowbrick is a data warehouse platform that was built from the ground up for speed, and can work across clouds and all the way to the edge. In this episode CTO Mark Cusack explains how the engine is architected, the benefits that speed and predictable pricing has for the organization, and how you can simplify your platform by putting the warehouse close to the data, instead of the other way around.

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! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Mark Cusack about Yellowbrick, a data warehouse designed for distributed clouds

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Yellowbrick is and some of the story behind it? What does the term "distributed cloud" signify and what challenges are associated with it? How would you characterize Yellowbrick’s position in the database/DWH market? How is Yellowbrick architected?

How have the goals and design of the platform changed or evolved over time?

How does Yellowbrick maintain visibility across the different data locations that it is responsible for?

What capabilities does it offer for being able to join across the disparate "clouds"?

What are some data modeling strategies that users should consider when designing their deployment of Yellowbrick? What are some of the capabilities of Yellowbrick that you find most useful or technically interesting? For someone who is adopting Yellowbrick, what is the process for getting it integrated into their data systems? What are the most underutilized, overlooked, or misunderstood features of Yellowbrick? What are the most interesting, innovative, or unexpected ways that you have seen Yellowbrick used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on and with Yellowbrick? When is Yellowbrick the wrong choice? What do you have planned for the future of the product?

Contact Info

LinkedIn @markcusack on Twitter

Parting Question

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

Links

Yellowbrick Teradata Rainstor Distributed Cloud Hybrid Cloud SwimOS

Podcast Episode

K

Summary Machine learning models use vectors as the natural mechanism for representing their internal state. The problem is that in order for the models to integrate with external systems their internal state has to be translated into a lower dimension. To eliminate this impedance mismatch Edo Liberty founded Pinecone to build database that works natively with vectors. In this episode he explains how this technology will allow teams to accelerate the speed of innovation, how vectors make it possible to build more advanced search functionality, and how Pinecone is architected. This is an interesting conversation about how reconsidering the architecture of your systems can unlock impressive new capabilities.

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! 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. When it comes to serving data for AI and ML projects, do you feel like you have to rebuild the plane while you’re flying it across the ocean? Molecula is an enterprise feature store that operationalizes advanced analytics and AI in a format designed for massive machine-scale projects without having to manage endless one-off information requests. With Molecula, data engineers manage one single feature store that serves the entire organization with millisecond query performance whether in the cloud or at your data center. And since it is implemented as an overlay, Molecula doesn’t disrupt legacy systems. High-growth startups use Molecula’s feature store because of its unprecedented speed, cost savings, and simplified access to all enterprise data. From feature extraction to model training to production, the Molecula feature store provides continuously updated feature access, reuse, and sharing without the need to pre-process data. If you need to deliver unprecedented speed, cost savings, and simplified access to large scale, real-time data, visit dataengineeringpodcast.com/molecula and request a demo. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt. Your host is Tobias Macey and today I’m interviewing Edo Liberty about Pinecone, a vector database for powering machine learning and similarity search

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Pinecone is and the story behind it? What are some of the contexts where someone would want to perform a similarity search?

What are the considerations that someone should be aware of when deciding between Pinecone and Solr/Lucene for a search oriented use case?

What are some of the other use cases that Pinecone enables? In the absence of Pinecone, what kinds of systems and solutions are people b

Summary Data governance is a phrase that means many different things to many different people. This is because it is actually a concept that encompasses the entire lifecycle of data, across all of the people in an organization who interact with it. Stijn Christiaens co-founded Collibra with the goal of addressing the wide variety of technological aspects that are necessary to realize such an important and expansive process. In this episode he shares his thoughts on the balance between human and technological processes that are necessary for a well-managed data governance strategy, how Collibra is designed to aid in that endeavor, and his experiences using the platform that his company is building to help power the company. This is an excellent conversation that spans the engineering and philosophical complexities of an important and ever-present aspect of working with 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 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!

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 Stijn Christiaens about data governance in the enterprise and how Collibra applies the lessons learned from their customers to their own business

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Collibra and the story behind the company? Wat does "data governance" mean to you, and how does that definition inform your work at Collibra?

How would you characterize the current landscape of "data governance" offerings and Collibra’s position within it?

What are the elements of governance that are often ignored in small/medium businesses but which are essential for the enterprise? (e.g. data stewards, business glossaries, etc.) One of the most important tasks as a data professional is to establish and maintain trust in the information you are curating. What are the biggest obstacles to overcome in that mission? What are some of the data problems that you will only find at large or complex organizations?

How does Collibra help to tame that complexity?

Who are the end users of Collibra within an organization? Can you talk through the workflow and various interactions that your customers have as it relates to the overall flow of data through an organization? Can you describe how the Collibra platform is implemented?

How has the scope and design of the system evolved since you first began working on it?

You are currently leading a team that uses Collibra to manage the operations of the business. What are some of the most notable surprises that you have learned from being your own customer?

What are some of the weak points that you have be

Summary Data lineage is the common thread that ties together all of your data pipelines, workflows, and systems. In order to get a holistic understanding of your data quality, where errors are occurring, or how a report was constructed you need to track the lineage of the data from beginning to end. The complicating factor is that every framework, platform, and product has its own concepts of how to store, represent, and expose that information. In order to eliminate the wasted effort of building custom integrations every time you want to combine lineage information across systems Julien Le Dem introduced the OpenLineage specification. In this episode he explains his motivations for starting the effort, the far-reaching benefits that it can provide to the industry, and how you can start integrating it into your data platform today. This is an excellent conversation about how competing companies can still find mutual benefit in co-operating on open standards.

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! 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. When it comes to serving data for AI and ML projects, do you feel like you have to rebuild the plane while you’re flying it across the ocean? Molecula is an enterprise feature store that operationalizes advanced analytics and AI in a format designed for massive machine-scale projects without having to manage endless one-off information requests. With Molecula, data engineers manage one single feature store that serves the entire organization with millisecond query performance whether in the cloud or at your data center. And since it is implemented as an overlay, Molecula doesn’t disrupt legacy systems. High-growth startups use Molecula’s feature store because of its unprecedented speed, cost savings, and simplified access to all enterprise data. From feature extraction to model training to production, the Molecula feature store provides continuously updated feature access, reuse, and sharing without the need to pre-process data. If you need to deliver unprecedented speed, cost savings, and simplified access to large scale, real-time data, visit dataengineeringpodcast.com/molecula and request a demo. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt. Your host is Tobias Macey and today I’m interviewing Julien Le Dem about Open Lineage, a new standard for structuring metadata to enable interoperability across the ecosystem of data management tools.

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what the Open Lineage project is and the story behind it? What is the current state of t

Summary There is a lot of attention on the database market and cloud data warehouses. While they provide a measure of convenience, they also require you to sacrifice a certain amount of control over your data. If you want to build a warehouse that gives you both control and flexibility then you might consider building on top of the venerable PostgreSQL project. In this episode Thomas Richter and Joshua Drake share their advice on how to build a production ready data warehouse with Postgres.

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! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Thomas Richter and Joshua Drake about using Postgres as your data warehouse

Interview

Introduction How did you get involved in the area of data management? Can you start by establishing a working definition of what constitutes a data warehouse for the purpose of this discussion?

What are the limitations for out-of-the-box Postgres when trying to use it for these workloads?

There are a large and growing number of options for data warehouse style workloads. How would you categorize the different systems and what is PostgreSQL’s position in that ecosystem?

What do you see as the motivating factors for a team or organization to select from among those categories?

Why would someone want to use Postgres as their data warehouse platform rather than using a purpose-built engine? What is the cost/performance equation for Postgres as compared to other data warehouse solutions? For someone who wants to turn Postgres into a data warehouse engine, what are their options?

What are the relative tradeoffs of the different open source and commercial offerings? (e.g. Citus, cstore_fdw, zedstore, Swarm64, Greenplum, etc.)

One of the biggest areas of growth right now is in the "cloud data warehouse" market where storage and compute are decoupled. What are the options for making that possible with Postgres? (e.g. using foreign data wrappers for interacting with data lake storage (S3, HDFS, Alluxio, etc.)) What areas of work are happening in the Postgres community for upcoming releases to make it more easily suited to data warehouse/analytical workloads? What are some of the most interesting, innovative, or unexpected ways that you have seen Postgres used in analytical contexts? What are the most interesting, unexpected, or challenging lessons that you have learned from your own experiences of building analytical systems with Postgres? When is Postgres the wrong choice fo

Summary Building an API for real-time data is a challenging project. Making it robust, scalable, and fast is a full time job. The team at Tinybird wants to make it easy to turn a continuous stream of data into a production ready API or data product. In this episode CEO Jorge Sancha explains how they have architected their system to handle high data throughput and fast response times, and why they have invested heavily in Clickhouse as the core of their platform. This is a great conversation about the challenges of building a maintainable business from a technical and product perspective.

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! 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. Ascend.io — recognized as a 2021 Gartner Cool Vendor in Enterprise AI Operationalization and Engineering—empowers data teams to to build, scale, and operate declarative data pipelines with 95% less code and zero maintenance. Connect to any data source using Ascend’s new flex code data connectors, rapidly iterate on transformations and send data to any destination in a fraction of the time it traditionally takes—just ask companies like Harry’s, HNI, and Mayvenn. Sound exciting? Come join the team! We’re hiring data engineers, so head on over to dataengineeringpodcast.com/ascend and check out our careers page to learn more. Your host is Tobias Macey and today I’m interviewing Jorge Sancha about Tinybird, a platform to easily build analytical APIs for real-time data

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Tinybird and the story behind it? What are some of the types of use cases that your customers are focused on? What are the areas of complexity that come up when building analytical APIs that are often overlooked when first designing a system to operate on and expose real-time data?

What are the supporting systems that are necessary and useful for operating this kind of system which contribute to the overall time and engineering cost beyond the baseline functionality?

How is the Tinybird platform architected?

How have the goals and implementation of Tinybird changed or evolved since you first began building it?

What was your criteria for selecting the core building block of your platform, and how did that lead to your choice to build on top of Clickhouse? What are some of the sharp edges that you have run into while operating Clickhouse?

What are some of the custom tools or systems that you have built to help deal with them?

What are some of the performance challenges that an API built with Tinybird might run into?

What are the considerations that users should be

Summary Spark is one of the most well-known frameworks for data processing, whether for batch or streaming, ETL or ML, and at any scale. Because of its popularity it has been deployed on every kind of platform you can think of. In this episode Jean-Yves Stephan shares the work that he is doing at Data Mechanics to make it sing on Kubernetes. He explains how operating in a cloud-native context simplifies some aspects of running the system while complicating others, how it simplifies the development and experimentation cycle, and how you can get a head start using their pre-built Spark container. This is a great conversation for understanding how new ways of operating systems can have broader impacts on how they are being used.

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! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Jean-Yves Stephan about Data Mechanics, a cloud-native Spark platform for data engineers

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 Data Mechanics and the story behind it? What are the operational characteristics of Spark that make it difficult to run in a cloud-optimized environment? How do you handle retries, state redistribution, etc. when instances get pre-empted during the middle of a job execution?

What are some of the tactics that you have found useful when designing jobs to make them more resilient to interruptions?

What are the customizations that you have had to make to Spark itself? What are some of the supporting tools that you have built to allow for running Spark in a Kubernetes environment? How is the Data Mechanics platform implemented?

How have the goals and design of the platform changed or evolved since you first began working on it?

How does running Spark in a container/Kubernetes environment change the ways that you and your customers think about how and where to use it?

How does it impact the development workflow for data engineers and data scientists?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while building the Data Mechanics product? When is Spark/Data Mechanics the wrong choice? What do you have planned for the future of the platform?

Contact Info

LinkedIn

Parting Question

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

Links

Data Mechanics Databricks Stanford Andrew Ng Mining Massive Datasets Spark Kubernetes Spot Instances Infiniband Data Mechanics Spark Container Image Delight – Spark monitoring utility Terraform Blue/Green Deployment Spark Operator for Kubernetes JupyterHub Jupyter Enterprise Gateway

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

Support Data Engineering Podcast

Summary The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. More than just a collection of tools, there are a number of organizational and conceptual changes that a proper DataOps approach depends on. In this episode Kevin Stumpf, CTO of Tecton, Maxime Beauchemin, CEO of Preset, and Lior Gavish, CTO of Monte Carlo, discuss the grand vision and present realities of DataOps. They explain how to think about your data systems in a holistic and maintainable fashion, the security challenges that threaten to derail your efforts, and the power of using metadata as the foundation of everything that you do. If you are wondering how to get control of your data platforms and bring all of your stakeholders onto the same page then this conversation 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 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 Max Beauchemin, Lior Gavish, and Kevin Stumpf about the real world challenges of embracing DataOps practices and systems, and how to keep things secure as you scale

Interview

Introduction How did you get involved in the area of data management? Before we get started, can you each give your definition of what "DataOps" means to you?

How does this differ from "business as usual" in the data industry? What are some of the things that DataOps isn’t (despite what marketers might say)?

What are the biggest difficulties that you have faced in going from concept to production with a workflow or system intended to power self-serve access to other membe

Summary The reason for collecting, cleaning, and organizing data is to make it usable by the organization. One of the most common and widely used methods of access is through a business intelligence dashboard. Superset is an open source option that has been gaining popularity due to its flexibility and extensible feature set. In this episode Maxime Beauchemin discusses how data engineers can use Superset to provide self service access to data and deliver analytics. He digs into how it integrates with your data stack, how you can extend it to fit your use case, and why open source systems are a good choice for your business intelligence. If you haven’t already tried out Superset then this conversation is well worth your time. Give it a listen and then take it for a test drive today.

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 Max Beauchemin about Superset, an open source platform for data exploration, dashboards, and business intelligence

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Superset is? Superset is becoming part of the reference architecture for a modern data stack. What are the factors that have contributed to its popularity over other tools such as Redash, Metabase, Looker, etc.? Where do dashboarding and exploration tools like Superset fit in the responsibilities and workflow of a data engineer? What are some of the challenges that Superset faces in being performant when working with large data sources?

Which data sources have you found to be the most challenging to work with?

What are some anti-patterns that users of Superset mig

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

Support Data Engineering Podcast

Summary "Business as usual" is changing, with more companies investing in data as a first class concern. As a result, the data team is growing and introducing more specialized roles. In this episode Josh Benamram, CEO and co-founder of Databand, describes the motivations for these emerging roles, how these positions affect the team dynamics, and the types of visibility that they need into the data platform to do their jobs effectively. He also talks about how his experience working with these teams informs his work at Databand. If you are wondering how to apply your talents and interests to working with data then this episode is a must listen.

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 Josh Benamram about the continued evolution of roles and responsibilities in data teams and their varied requirements for visibility into the data stack

Interview

Introduction How did you get involved in the area of data management? Can you start by discussing the set of roles that you see in a majority of data teams? What new roles do you see emerging, and what are the motivating factors?

Which of the more established positions are fracturing or merging to create these new responsibilities?

What are the contexts in which you are seeing these role definitions used? (e.g. small teams, large orgs, etc.) How do the increased granularity/specialization of responsibilities across data teams change the ways that data and platform architects need to think about technology investment?

What are the organizational impacts of these new types of data work?

How do these shifts in role definition change the ways that the individuals in th

Summary One of the biggest obstacles to success in delivering data products is cross-team collaboration. Part of the problem is the difference in the information that each role requires to do their job and where they expect to find it. This introduces a barrier to communication that is difficult to overcome, particularly in teams that have not reached a significant level of maturity in their data journey. In this episode Prukalpa Sankar shares her experiences across multiple attempts at building a system that brings everyone onto the same page, ultimately bringing her to found Atlan. She explains how the design of the platform is informed by the needs of managing data projects for large and small teams across her previous roles, how it integrates with your existing systems, and how it can work to bring everyone onto the same page.

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 Prukalpa Sankar about Atlan, a modern data workspace that makes collaboration among data stakeholders easier, increasing efficiency and agility in data projects

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 Atlan and some of the story behind it? Who are the target users of Atlan? What portions of the data workflow is Atlan responsible for?

What components of the data stack might Atlan replace?

How would you characterize Atlan’s position in the current data ecosystem?

What makes Atlan stand out from other systems for data cataloguing, metadata management, or data governance? What types of data assets (e.g. structured vs unstructured, textual

Summary Data quality is on the top of everyone’s mind recently, but getting it right is as challenging as ever. One of the contributing factors is the number of people who are involved in the process and the potential impact on the business if something goes wrong. In this episode Maarten Masschelein and Tom Baeyens share the work they are doing at Soda to bring everyone on board to make your data clean and reliable. They explain how they started down the path of building a solution for managing data quality, their philosophy of how to empower data engineers with well engineered open source tools that integrate with the rest of the platform, and how to bring all of the stakeholders onto the same page to make your data great. There are many aspects of data quality management and it’s always a treat to learn from people who are dedicating their time and energy to solving it for everyone.

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 Maarten Masschelein and Tom Baeyens about the work are doing at Soda to power data quality management

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 Soda? What problem are you trying to solve? And how are you solving that problem?

What motivated you to start a business focused on data monitoring and data quality?

The data monitoring and broader data quality space is a segment of the industry that is seeing a huge increase in attention recently. Can you share your perspective on the current state of the ecosystem and how your approach compares to other tools and products? who have you cr