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 A data catalog is a critical piece of infrastructure for any organization who wants to build analytics products, whether internal or external. While there are a number of platforms available for building that catalog, many of them are either difficult to deploy and integrate, or expensive to use at scale. In this episode Grant Seward explains how he built Tree Schema to be an easy to use and cost effective option for organizations to build their data catalogs. He also shares the internal architecture, how he approached the design to make it accessible and easy to use, and how it autodiscovers the schemas and metadata for your source systems.

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 you derive real results from your data at dataengineeringpodcast.com/immuta. Your host is Tobias Macey and today I’m interviewing Grant Seward about Tree Schema, a human friendly data catalog

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what you have built at Tree Schema?

What was your motivation for creating it?

At what stage of maturity should a team or organization

Summary Data lakes are gaining popularity due to their flexibility and reduced cost of storage. Along with the benefits there are some additional complexities to consider, including how to safely integrate new data sources or test out changes to existing pipelines. In order to address these challenges the team at Treeverse created LakeFS to introduce version control capabilities to your storage layer. In this episode Einat Orr and Oz Katz explain how they implemented branching and merging capabilities for object storage, best practices for how to use versioning primitives to introduce changes to your data lake, how LakeFS is architected, and how you can start using it for your own data platform.

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! 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. Your host is Tobias Macey and today I’m interviewing Einat Orr and Oz Katz about their work at Treeverse on the LakeFS system for versioning your data lakes the same way you version your code.

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what LakeFS is and why you built it?

There are a number of tools and platforms that support data virtualization and data versioning. How does LakeFS compare to the available options? (e.g. Alluxio, Denodo, Pachyderm, DVC, etc.)

What are the primary use cases that LakeFS enables? For someone who wants to use LakeFS what is involved in getting it set up? How is LakeFS implemented?

How has the design of the system changed or evolved since you began working on it? What assumptions did you have going into it which have since been invalidated or modified?

How does the workflow for an engineer or analyst change from working directly against S3 to running against the LakeFS interface? How do you handle merge conflicts and resolution?

What

Summary One of the most challenging aspects of building a data platform has nothing to do with pipelines and transformations. If you are putting your workflows into production, then you need to consider how you are going to implement data security, including access controls and auditing. Different databases and storage systems all have their own method of restricting access, and they are not all compatible with each other. In order to simplify the process of securing your data in the Cloud Manav Mital created Cyral to provide a way of enforcing security as code. In this episode he explains how the system is architected, how it can help you enforce compliance, and what is involved in getting it integrated with your existing systems. This was a good conversation about an aspect of data management that is too often left as an afterthought.

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!

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 Analytical workloads require a well engineered and well maintained data integration process to ensure that your information is reliable and up to date. Building a real-time pipeline for your data lakes and data warehouses is a non-trivial effort, requiring a substantial investment of time and energy. Meroxa is a new platform that aims to automate the heavy lifting of change data capture, monitoring, and data loading. In this episode founders DeVaris Brown and Ali Hamidi explain how their tenure at Heroku informed their approach to making data integration self service, how the platform is architected, and how they have designed their system to adapt to the continued evolution of the data ecosystem.

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 DeVaris Brown and Ali Hamidi about Meroxa, a new platform as a service for dat

Summary Kafka has become a de facto standard interface for building decoupled systems and working with streaming data. Despite its widespread popularity, there are numerous accounts of the difficulty that operators face in keeping it reliable and performant, or trying to scale an installation. To make the benefits of the Kafka ecosystem more accessible and reduce the operational burden, Alexander Gallego and his team at Vectorized created the Red Panda engine. In this episode he explains how they engineered a drop-in replacement for Kafka, replicating the numerous APIs, that can scale more easily and deliver consistently low latencies with a much lower hardware footprint. He also shares some of the areas of innovation that they have found to help foster the next wave of streaming applications while working within the constraints of the existing Kafka interfaces. This was a fascinating conversation with an energetic and enthusiastic engineer and founder about the challenges and opportunities in the realm of streaming 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 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. If you’re looking for a way to optimize your data engineering pipeline – with instant query performance – look no further than Qubz. Qubz is next-generation OLAP technology built for the scale of Big Data from UST Global, a renowned digital services provider. Qubz lets users and enterprises analyze data on the cloud and on-premise, with blazing speed, while eliminating the complex engineering required to operationalize analytics at scale. With an emphasis on visual data engineering, connectors for all major BI tools and data sources, Qubz allow users to query OLAP cubes with sub-second response times on hundreds of billions of rows. To learn more, and sign up for a free demo, visit dataengineeringpodcast.com/qubz. 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 s

Summary Data engineering is a constantly growing and evolving discipline. There are always new tools, systems, and design patterns to learn, which leads to a great deal of confusion for newcomers. Daniel Molnar has dedicated his time to helping data professionals get back to basics through presentations at conferences and meetups, and with his most recent endeavor of building the Pipeline Data Engineering Academy. In this episode he shares advice on how to cut through the noise, which principles are foundational to building a successful career as a data engineer, and his approach to educating the next generation of data practitioners. This was a useful conversation for anyone working with data who has found themselves spending too much time chasing the latest trends and wishes to develop a more focused approach to their work.

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. 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 Daniel Molnar about being a data janitor and how to cut through the hype to understand what to learn for the long run

Interview

Introduction How did you get involved in the area of data management? Can you start by describing your thoughts on the current state of the data management industry? What is your strategy for being effective in the face of so much complexity and conflicting needs for data? What are some of the common difficulties that you see data engineers contend with, whether technical or social/organizational? What are the core fundamentals that you thin

Summary Data warehouse technology has been around for decades and has gone through several generational shifts in that time. The current trends in data warehousing are oriented around cloud native architectures that take advantage of dynamic scaling and the separation of compute and storage. Firebolt is taking that a step further with a core focus on speed and interactivity. In this episode CEO and founder Eldad Farkash explains how the Firebolt platform is architected for high throughput, their simple and transparent pricing model to encourage widespread use, and the use cases that it unlocks through interactive query speeds.

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 Eldad Farkash about Firebolt, a cloud data warehouse optimized for speed and elasticity on structured and semi-structured data

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Firebolt is and your motivation for building it? How does Firebolt compare to other data warehouse technologies what unique features does it provide? The lines between a data warehouse and a data lake have been blurring in recent years. Where on that continuum does Firebolt lie? What are the unique use cases that Firebolt allows for? How do the performance characteristics of Firebolt change the ways that an engineer should think about data modeling? What technologies might someone replace with Firebolt? How is Firebolt architected and how has the design evolved since you first began working on it? What are some of the most challenging aspects of building a data warehouse platform that is optimized for speed? How do you ha

Summary In order to scale the use of data across an organization there are a number of challenges related to discovery, governance, and integration that need to be solved. The key to those solutions is a robust and flexible metadata management system. LinkedIn has gone through several iterations on the most maintainable and scalable approach to metadata, leading them to their current work on DataHub. In this episode Mars Lan and Pardhu Gunnam explain how they designed the platform, how it integrates into their data platforms, and how it is being used to power data discovery and analytics at LinkedIn.

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! If you’ve been exploring scalable, cost-effective and secure ways to collect and route data across your organization, RudderStack is the only solution that helps you turn your own warehouse into a state of the art customer data platform. Their mission is to empower data engineers to fully own their customer data infrastructure and easily push value to other parts of the organization, like marketing and product management. With their open-source foundation, fixed pricing, and unlimited volume, they are enterprise ready, but accessible to everyone. Go to dataengineeringpodcast.com/rudder to request a demo and get one free month of access to the hosted platform along with 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 Pardhu Gunnam and Mars Lan about DataHub, LinkedIn’s metadata management and data catalog platform

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what DataHub is and some of its back story?

What were you using at LinkedIn for metadata management prior to the introduction of DataHub? What was lacking in the previous solutions that motivated you to create a new platform?

There are a large number of other systems available for building data catalogs and tracking metadata, both open source and proprietary. What are the features of DataHub that would lead someone to use it in place of the other options? Who is the target audience for DataHub?

How do the needs of those end users influence or constrain your approach to the design and interfaces provided by DataHub?

Can you describe how DataHub is architected?

How has it evolved since yo

Summary Most databases are designed to work with textual data, with some special purpose engines that support domain specific formats. TileDB is a data engine that was built to support every type of data by using multi-dimensional arrays as the foundational primitive. In this episode the creator and founder of TileDB shares how he first started working on the underlying technology and the benefits of using a single engine for efficiently storing and querying any form of data. He also discusses the shifts in database architectures from vertically integrated monoliths to separately deployed layers, and the approach he is taking with TileDB cloud to embed the authorization into the storage engine, while providing a flexible interface for compute. This was a great conversation about a different approach to database architecture and how that enables a more flexible way to store and interact with data to power better data sharing and new opportunities for blending specialized domains.

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 Stavros Papadopoulos about TileDB, the universal storage engine

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what TileDB is and the problem that you are trying to solve with it?

What was your motivation for building it?

What are the main use cases or problem domains that you are trying to solve for?

What are the shortcomings of existing approaches to database design that prevent them from being useful for these applications?

What are the benefits of using matrices for data processing and domain modeling?

What are the challenges that you

Summary Event based data is a rich source of information for analytics, unless none of the event structures are consistent. The team at Iteratively are building a platform to manage the end to end flow of collaboration around what events are needed, how to structure the attributes, and how they are captured. In this episode founders Patrick Thompson and Ondrej Hrebicek discuss the problems that they have experienced as a result of inconsistent event schemas, how the Iteratively platform integrates the definition, development, and delivery of event data, and the benefits of elevating the visibility of event data for improving the effectiveness of the resulting analytics. If you are struggling with inconsistent implementations of event data collection, lack of clarity on what attributes are needed, and how it is being used then this is definitely a conversation worth following.

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! If you’ve been exploring scalable, cost-effective and secure ways to collect and route data across your organization, RudderStack is the only solution that helps you turn your own warehouse into a state of the art customer data platform. Their mission is to empower data engineers to fully own their customer data infrastructure and easily push value to other parts of the organization, like marketing and product management. With their open-source foundation, fixed pricing, and unlimited volume, they are enterprise ready, but accessible to everyone. Go to dataengineeringpodcast.com/rudder to request a demo and get one free month of access to the hosted platform along with 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 Patrick Thompson and Ondrej Hrebicek about Iteratively, a platform for enforcing consistent schemas for your event data

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Iteratively and your motivation for creating it? What are some of the ways that you have seen inconsistent message structures cause problems? What are some of the common anti-patterns that you have seen for managing the structure of event messages? What are the benefits that Iteratively provides for the different roles in an organization? Can you describe the workflow for a team using

Summary Finding connections between data and the entities that they represent is a complex problem. Graph data models and the applications built on top of them are perfect for representing relationships and finding emergent structures in your information. In this episode Denise Gosnell and Matthias Broecheler discuss their recent book, the Practitioner’s Guide To Graph Data, including the fundamental principles that you need to know about graph structures, the current state of graph support in database engines, tooling, and query languages, as well as useful tips on potential pitfalls when putting them into production. This was an informative and enlightening conversation with two experts on graph data applications that will help you start on the right track in your own projects.

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 Denise Gosnell and Matthias Broecheler about the recently published practitioner’s guide to graph data

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what your goals are for the Practitioner’s Guide To Graph Data?

What was your motivation for writing a book to address this topic?

What do you see as the driving force behind the growing popularity of graph technologies in recent years? What are some of the common use cases/applications of graph data and graph traversal algorithms?

What are the core elements of graph thinking that data teams need to be aware of to be effective in identifying those cases in their existing systems?

What are the fundamental principles of graph technologies that data engineers should be familiar with?

Wha

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 The first stage of every data pipeline is extracting the information from source systems. There are a number of platforms for managing data integration, but there is a notable lack of a robust and easy to use open source option. The Meltano project is aiming to provide a solution to that situation. In this episode, project lead Douwe Maan shares the history of how Meltano got started, the motivation for the recent shift in focus, and how it is implemented. The Singer ecosystem has laid the groundwork for a great option to empower teams of all sizes to unlock the value of their Data and Meltano is building the reamining structure to make it a fully featured contender for proprietary systems.

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 Douwe Maan about Meltano, an open source platform for building, running & orchestrating ELT pipelines.

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Meltano is and the story behind it? Who is the target audience?

How does the focus on small or early stage organizations constrain the architectural decisions that go into Meltano?

What have you found to be the complexities in trying to encapsulate the entirety of the data lifecycle in a single tool or platform?

What are the most painful transitions in that lifecycle and how does that pain manifest?

How and why has the focus of the project shifted from its original vision? With your current focus on the data integration/data transfer stage of the lifecycle, what are you seeing as the biggest barriers to entry with the current ecosystem?

What are the main elements of

Summary There are an increasing number of use cases for real time data, and the systems to power them are becoming more mature. Once you have a streaming platform up and running you need a way to keep an eye on it, including observability, discovery, and governance of your data. That’s what the Lenses.io DataOps platform is built for. In this episode CTO Andrew Stevenson discusses the challenges that arise from building decoupled systems, the benefits of using SQL as the common interface for your data, and the metrics that need to be tracked to keep the overall system healthy. Observability and governance of streaming data requires a different approach than batch oriented workflows, and this episode does an excellent job of outlining the complexities involved and how to address them.

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 Andrew Stevenson about Lenses.io, a platform to provide real-time data operations for engineers

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Lenses is and the story behind it? What is your working definition for what constitutes DataOps?

How does the Lenses platform support the cross-cutting concerns that arise when trying to bridge the different roles in an organization to deliver value with data?

What are the typical barriers to collaboration, and how does Lenses help with that?

Many different systems provide a SQL interface to streaming data on various substrates. What was your reason for building your own SQL engine and what is unique about it? What are the main challenges that you see engineers facing when working with s

Summary The majority of analytics platforms are focused on use internal to an organization by business stakeholders. As the availability of data increases and overall literacy in how to interpret it and take action improves there is a growing need to bring business intelligence use cases to a broader audience. GoodData is a platform focused on simplifying the work of bringing data to employees and end users. In this episode Sheila Jung and Philip Farr discuss how the GoodData platform is being used, how it is architected to provide scalable and performant analytics, and how it integrates into customer’s data platforms. This was an interesting conversation about a different approach to business intelligence and the importance of expanded access to 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 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! GoodData is revolutionizing the way in which companies provide analytics to their customers and partners. Start now with GoodData Free that makes our self-service analytics platform available to you at no cost. Register today at dataengineeringpodcast.com/gooddata Your host is Tobias Macey and today I’m interviewing Sheila Jung and Philip Farr about how GoodData is building a platform that lets you share your analytics outside the boundaries of your organization

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at GoodData and some of its origin story? The business intelligence market has been around for decades now and there are dozens of options with different areas of focus. What are the factors that might motivate me to choose GoodData over the other contenders in the space? What are the use cases and industries that you focus on supporting with GoodData? How has the market of business intelligence tools evolved in recent years?

What are the contributing trends in technology and business use cases that are driving that change?

What are some of the ways that your customers are embedding analytics into their own products? What are the differences in processing and serving capabilities between an internally used business intelligence tool, and one that is used for embedding into externally used systems?

What unique challenges are posed by the embedded analytics use case? How do you approach topics such as security, access control, and latency in a multitenant analytics platform?

What guidelines have you found to be most useful when addressing the concerns of accuracy and interpretability of the data being presented? How is the GoodData platform architected?

What are the complexities that you have had to design around in order to provide performant access to your customers’ data sources in an interactive use case? What are the off-the-shelf components that you have been able to integrate into the platform,

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