Summary The data that you have access to affects the questions that you can answer. By using external data sources you can drastically increase the range of analysis that is available to your organization. The challenge comes in all of the operational aspects of finding, accessing, organizing, and serving that data. In this episode Mark Hookey discusses how he and his team at Demyst do all of the DataOps for external data sources so that you don’t have to, including the systems necessary to organize and catalog the various collections that they host, the various serving layers to provide query interfaces that match your platform, and the utility of having a single place to access a multitude of information. If you are having trouble answering questions for your business with the data that you generate and collect internally, then it is definitely worthwhile to explore the information available from external sources.

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! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/montecarlo to learn more. The first 10 people to request a personalized product tour will receive an exclusive Monte Carlo Swag box. 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. Your host is Tobias Macey and today I’m interviewing Mark Hookey about Demyst Data, a platform for operationalizing external data

Interview

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

What are the services and systems that you provide for organizations to incorporate external sources in their data workflows? Who are your target customers?

What are some examples of data sets that an organization might want to use in their analytics?

How are these different from SaaS data that an organization might integrate with tools such as Stitcher and Fivetran?

What are some of the challenges that are introduced by working with these external data sets?

If an organization isn’t using Demyst what are some

Summary The technology for scaling storage and processing of data has gone through massive evolution over the past decade, leaving us with the ability to work with massive datasets at the cost of massive complexity. Nick Schrock created the Dagster framework to help tame that complexity and scale the organizational capacity for working with data. In this episode he shares the journey that he and his team at Elementl have taken to understand the state of the ecosystem and how they can provide a foundational layer for a holistic 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 and blazing fast NVMe storage there’s nothing slowing you down. 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! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/montecarlo to learn more. The first 10 people to request a personalized product tour will receive an exclusive Monte Carlo Swag box. 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. Your host is Tobias Macey and today I’m interviewing Nick Schrock about the evolution of Dagster and its path forward

Interview

Introduction How did you get involved in the area of data management? Can you describe what Dagster is and the story behind it? How has the project and community changed/evolved since we last spoke 2 years ago?

How has the experience of the past 2 years clarified the challenges and opportunities that exist in the data ecosystem?

What do you see as the foundational vs transient complexities that are germane to the industry?

One of the emerging ideas in Dagster is the "software defined data asset" as the central entity in the framework. How has that shifted the way that engineers approach pipeline design and composition?

How did that conceptual shift inform the accompanying refactor of the core principles in the framework? (jobs, ops, graphs)

One of the powerful elements of the Dagster framework is the investment in rich metadata as a foundational principle. What are the opportunities for integrating and extending that context throughout the rest of an organizations data platform?

What do you see as the potential for efforts such as OpenLineage and OpenMetadata to allow for other compone

Summary The most important gauge of success for a data platform is the level of trust in the accuracy of the information that it provides. In order to build and maintain that trust it is necessary to invest in defining, monitoring, and enforcing data quality metrics. In this episode Michael Harper advocates for proactive data quality and starting with the source, rather than being reactive and having to work backwards from when a problem is found.

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! 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 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. Your host is Tobias Macey and today I’m interviewing Michael Harper about definitions of data quality and where to define and enforce it in the data platform

Interview

Introduction How did you get involved in the area of data management? What is your definition for the term "data quality" and what are the implied goals that it embodies?

What are some ways that different stakeholders and participants in the data lifecycle might disagree about the definitions and manifestations of data quality?

The market for "data quality tools" has been growing and gaining attention recently. How would you categorize the different approaches taken by open source and commercial options in the ecosystem?

What are the tradeoffs that you see in each approach? (e.g. data warehouse as a chokepoint vs quality checks on extract)

What are the difficulties that engineers and stakeholders encounter when identifying and defining information that is necessary to identify issues in their workflows? Can you describe some examples of adding data quality checks to the beginning stages of a data workflow and the kinds of issues that can be identified?

What are some ways that quality and observability metrics can be aggregated across multiple pipeline stages to identify more complex issues?

In application observa

Summary A significant source of friction and wasted effort in building and integrating data management systems is the fragmentation of metadata across various tools. After experiencing the impacts of fragmented metadata and previous attempts at building a solution Suresh Srinivas and Sriharsha Chintalapani created the OpenMetadata project. In this episode they share the lessons that they have learned through their previous attempts and the positive impact that a unified metadata layer had during their time at Uber. They also explain how the OpenMetadat project is aiming to be a common standard for defining and storing metadata for every use case in data platforms and the ways that they are architecting the reference implementation to simplify its adoption. This is an ambitious and exciting project, 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! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/impact today to save your spot at IMPACT: The Data Observability Summit a half-day virtual event featuring the first U.S. Chief Data Scientist, founder of the Data Mesh, Creator of Apache Airflow, and more data pioneers spearheading some of the biggest movements in data. The first 50 to RSVP with this link will be entered to win an Oculus Quest 2 — Advanced All-In-One Virtual Reality Headset. RSVP today – you don’t want to miss it! 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. Your host is Tobias Macey and today I’m interviewing Sriharsha Chintalapani and Suresh Srinivas about OpenMetadata, an open standard for metadata and a reference implementation for a central metadata store

Interview

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

What are the goals of the project?

What are the common challenges faced by engineers and data practitioners in organizing the metadata for their systems? What are the capabilities that a centralized and holis

Summary The precursor to widespread adoption of cloud data warehouses was the creation of customer data platforms. Acting as a centralized repository of information about how your customers interact with your organization they drove a wave of analytics about how to improve products based on actual usage data. A natural outgrowth of that capability is the more recent growth of reverse ETL systems that use those analytics to feed back into the operational systems used to engage with the customer. In this episode Tejas Manohar and Rachel Bradley-Haas share the story of their own careers and experiences coinciding with these trends. They also discuss the current state of the market for these technological patterns and how to take advantage of them in your own work.

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! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. Go to dataengineeringpodcast.com/montecarlo and start trusting your data with Monte Carlo today! 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. Your host is Tobias Macey and today I’m interviewing Rachel Bradley-Haas and Tejas Manohar about the combination of operational analytics and the customer data platform

Interview

Introduction How did you get involved in the area of data management? Can we start by discussing what it means to have a "customer data platform"? What are the challenges that organizations face in establishing a unified view of their customer interactions?

How do the presence of multiple product lines impact the ability to understand the relationship with the customer?

We have been building data warehouses and business intelligence systems for decades. How does the idea of a CDP differ from the approaches of those previous generations? A recent outgrowth of the focus on creating a CDP is the introduction of "operational analytics", which was initially termed "reverse ETL". What are your opinions on the semantics and importance of these names?

What is the relationship between a CDP and operational analytics? (can you have one without the other?)

How have the capabilities

Summary The perennial question of data warehousing is how to model the information that you are storing. This has given rise to methods as varied as star and snowflake schemas, data vault modeling, and wide tables. The challenge with many of those approaches is that they are optimized for answering known questions but brittle and cumbersome when exploring unknowns. In this episode Ahmed Elsamadisi shares his journey to find a more flexible and universal data model in the form of the "activity schema" that is powering the Narrator platform, and how it has allowed his customers to perform self-service exploration of their business domains without being blocked by schema evolution in the data warehouse. This is a fascinating exploration of what can be done when you challenge your assumptions about what is possible.

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! 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 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. Your host is Tobias Macey and today I’m interviewing Ahmed Elsamadisi about Narrator, a platform to enable anyone to go from question to data-driven decision in minutes

Interview

Introduction How did you get involved in the area of data management? Can you describe what Narrator is and the story behind it? What are the challenges that you have seen organizations encounter when attempting to make analytics a self-serve capability? What are the use cases that you are focused on? How does Narrator fit within the data workflows of an organization? How is the Narrator platform implemented?

How has the design and focus of the technology evolved since you first started working on Narrator?

The core element of the analyses that you are building is the "activity schema". Can you describe the design process that led you to that format?

What are the challenges that are posed by more widely used modeling techniques such as star/s

Summary Streaming data systems have been growing more capable and flexible over the past few years. Despite this, it is still challenging to build reliable pipelines for stream processing. In this episode Eric Sammer discusses the shortcomings of the current set of streaming engines and how they force engineers to work at an extremely low level of abstraction. He also explains why he started Decodable to address that limitation and the work that he and his team have done to let data engineers build streaming pipelines entirely in SQL.

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! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/impact today to save your spot at IMPACT: The Data Observability Summit a half-day virtual event featuring the first U.S. Chief Data Scientist, founder of the Data Mesh, Creator of Apache Airflow, and more data pioneers spearheading some of the biggest movements in data. The first 50 to RSVP with this link will be entered to win an Oculus Quest 2 — Advanced All-In-One Virtual Reality Headset. RSVP today – you don’t want to miss it! 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. Your host is Tobias Macey and today I’m interviewing Eric Sammer about Decodable, a platform for simplifying the work of building real-time data pipelines

Interview

Introduction How did you get involved in the area of data management? Can you describe what Decodable is and the story behind it? Who are the target users, and how has that focus informed your prioritization of features at launch? What are the complexities that data engineers encounter when building pipelines on streaming systems? What are the distributed systems concepts and design optimizations that are often skipped over or misunderstood by engineers who are using them? (e.g. backpressure, exactly once semantics, isolation levels, etc.)

How do those mismatches in understanding and expectation impact the correctness and reliability of the workflows that they are building?

Can you describe how y

Summary The market for business intelligence has been going through an evolutionary shift in recent years. One of the driving forces for that change has been the rise of analytics engineering powered by dbt. Lightdash has fully embraced that shift by building an entire open source business intelligence framework that is powered by dbt models. In this episode Oliver Laslett describes why dashboards aren’t sufficient for business analytics, how Lightdash promotes the work that you are already doing in your data warehouse modeling with dbt, and how they are focusing on bridging the divide between data teams and business teams and the requirements that they have for data workflows.

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. 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. Your host is Tobias Macey and today I’m interviewing Oliver Laslett about Lightdash, an open source business intelligence system powered by your dbt models

Interview

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

What are the main goals of the project? Who are the target users, and how has that profile informed your feature priorities?

Business intelligence is a market that has gone through several generational shifts, with products targeting numerous personas and purposes. What are the capabilities that make Lightdash stand out from the other options? Can you describe how Lightdash is architected?

How have the design and goals of the system changed or evolved since you first began working on it? What have been the most challenging engineering problems that you have dealt with?

How does the approach that you are taking with Lightdash compare to systems such as Transform and Metriql that aim to provide a dedicated metrics layer? Can you describe the workflow for som

Summary The focus of the past few years has been to consolidate all of the organization’s data into a cloud data warehouse. As a result there have been a number of trends in data that take advantage of the warehouse as a single focal point. Among those trends is the advent of operational analytics, which completes the cycle of data from collection, through analysis, to driving further action. In this episode Boris Jabes, CEO of Census, explains how the work of synchronizing cleaned and consolidated data about your customers back into the systems that you use to interact with those customers allows for a powerful feedback loop that has been missing in data systems until now. He also discusses how Census makes that synchronization easy to manage, how it fits with the growth of data quality tooling, and how you can start using it 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! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/impact today to save your spot at IMPACT: The Data Observability Summit a half-day virtual event featuring the first U.S. Chief Data Scientist, founder of the Data Mesh, Creator of Apache Airflow, and more data pioneers spearheading some of the biggest movements in data. The first 50 to RSVP with this link will be entered to win an Oculus Quest 2 — Advanced All-In-One Virtual Reality Headset. RSVP today – you don’t want to miss it! 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 Boris Jabes about Census and the growing category of operational analytics

Interview

Introduction How did you get involved in the area of data management? Can you describe what Census is and the story behind it? The terms "reverse ETL" and "operational analytics" have started being used for similar, and often interchangeable, purposes. What are your thoughts on the semantic and concrete differences between these phrases? What are the motivating factors for adding operational analytics or "data activation" to a

Summary The binding element of all data work is the metadata graph that is generated by all of the workflows that produce the assets used by teams across the organization. The DataHub project was created as a way to bring order to the scale of LinkedIn’s data needs. It was also designed to be able to work for small scale systems that are just starting to develop in complexity. In order to support the project and make it even easier to use for organizations of every size Shirshanka Das and Swaroop Jagadish founded Acryl Data. In this episode they discuss the recent work that has been done by the community, how their work is building on top of that foundation, and how you can get started with DataHub for your own work to manage data discovery today. They also share their ambitions for the near future of adding data observability and data quality management features.

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. 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. Your host is Tobias Macey and today I’m interviewing Shirshanka Das and Swaroop Jagadish about Acryl Data, the company driving the open source metadata project DataHub for powering data discovery, data observability and federated data governance.

Interview

Introduction How did you get involved in the area of data management? Can you describe what Acryl Data is and the story behind it? How has your experience of building and running DataHub at LinkedIn informed your product direction for Acryl?

What are some lessons that your co-founder Swaroop has contributed from his experience at AirBnB?

The data catalog/discovery/quality market has become very active over the past year. What is your perspective on the market, and what are the gaps that are not yet bei

Summary Building, scaling, and maintaining the operational components of a machine learning workflow are all hard problems. Add the work of creating the model itself, and it’s not surprising that a majority of companies that could greatly benefit from machine learning have yet to either put it into production or see the value. Tristan Zajonc recognized the complexity that acts as a barrier to adoption and created the Continual platform in response. In this episode he shares his perspective on the benefits of declarative machine learning workflows as a means of accelerating adoption in businesses that don’t have the time, money, or ambition to build everything from scratch. He also discusses the technical underpinnings of what he is building and how using the data warehouse as a shared resource drastically shortens the time required to see value. This is a fascinating episode and Tristan’s work at Continual is likely to be the catalyst for a new stage in the machine learning community.

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! Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today. 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 Tristan Zajonc about Continual, a platform for automating the creation and application of operational AI on top of your data warehouse

Interview

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

What is your definition for "operational AI" and how does it differ from other applications of ML/AI?

What are some example use cases for AI in an operational capacity?

What are the barriers to adoption for organizations that want to take advantage of predictive analytics?

Who are the target users of Continual? Can you describe how the Continual platform is implemented?

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

What is the workflow for

Summary The term "data platform" gets thrown around a lot, but have you stopped to think about what it actually means for you and your organization? In this episode Lior Gavish, Lior Solomon, and Atul Gupte share their view of what it means to have a data platform, discuss their experiences building them at various companies, and provide advice on how to treat them like a software product. This is a valuable conversation about how to approach the work of selecting the tools that you use to power your data systems and considerations for how they can be woven together for a unified experience across your various stakeholders.

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 Lior Gavish, Lior Solomon, and Atul Gupte about the technical, social, and architectural aspects of building your data platform as a product for your internal customers

Interview

Introduction How did you get involved in the area of data management? – all Can we start by establishing a definition of "data platform" for the purpose of this conversation? Who are the stakeholders in a data platform?

Where does the responsibility lie for creating and maintaining ("owning") the platform?

What are some of the technical and organizational constraints that are likely to factor into the design and execution of the platform? What are the minimum set of requirements necessary to qualify as a platform? (as opposed to a collection of discrete components)

What are the additional capabilities that should be in place to simplify the use and maintenance of the platform?

How are data platforms managed? Are they managed by technical teams, product managers, etc.? What is the profile for a data product manager? – Atul G. How do you set SLIs / SLOs with your data platform team when you don’t have clear metrics you’re tracking? – Lior S. There has been a lot of conversation recently about different interpretations of the "modern data stack". For a team who is just starting to build out their platform, h

Summary The reason that so much time and energy is spent on data integration is because of how our applications are designed. By making the software be the owner of the data that it generates, we have to go through the trouble of extracting the information to then be used elsewhere. The team at Cinchy are working to bring about a new paradigm of software architecture that puts the data as the central element. In this episode Dan DeMers, Cinchy’s CEO, explains how their concept of a "Dataware" platform eliminates the need for costly and error prone integration processes and the benefits that it can provide for transactional and analytical application design. This is a fascinating and unconventional approach to working with data, so definitely give this a listen to expand your thinking about how to build your 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! 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. Have you ever had to develop ad-hoc solutions for security, privacy, and compliance requirements? Are you spending too much of your engineering resources on creating database views, configuring database permissions, and manually granting and revoking access to sensitive data? Satori has built the first DataSecOps Platform that streamlines data access and security. Satori’s DataSecOps automates data access controls, permissions, and masking for all major data platforms such as Snowflake, Redshift and SQL Server and even delegates data access management to business users, helping you move your organization from default data access to need-to-know access. Go to dataengineeringpodcast.com/satori today and get a $5K credit for your next Satori subscription. Your host is Tobias Macey and today I’m interviewing Dan DeMers about Cinchy, a dataware platform aiming to simplify the work of data integration by eliminating ETL/ELT

Interview

Introduction How did you get involved in the area of data management? Can you describe what Cinchy is and the story behind it? In your experience working in data and building complex enterprise-grade systems, what are the shortcomings and negative externalities of an ETL/ELT approach to data integration? How is a Dataware platform from a data lake or data warehouses? What is it used for? What is Zero-Copy Integration? How does that work? Can you describe how customers start their Cinchy journey? What are the main use case patterns that you’re seeing with Dataware? Your platform offers unlimited users, including business users. What are some of the challenges that you face in building a user experience that doesn’t become overwhelming as an organization scales the number of data sources and processing flows? Wh

Summary Data lakes have been gaining popularity alongside an increase in their sophistication and usability. Despite improvements in performance and data architecture they still require significant knowledge and experience to deploy and manage. In this episode Vikrant Dubey discusses his work on the Cuelake project which allows data analysts to build a lakehouse with SQL queries. By building on top of Zeppelin, Spark, and Iceberg he and his team at Cuebook have built an autoscaled cloud native system that abstracts the underlying complexity.

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. Have you ever had to develop ad-hoc solutions for security, privacy, and compliance requirements? Are you spending too much of your engineering resources on creating database views, configuring database permissions, and manually granting and revoking access to sensitive data? Satori has built the first DataSecOps Platform that streamlines data access and security. Satori’s DataSecOps automates data access controls, permissions, and masking for all major data platforms such as Snowflake, Redshift and SQL Server and even delegates data access management to business users, helping you move your organization from default data access to need-to-know access. Go to dataengineeringpodcast.com/satori today and get a $5K credit for your next Satori subscription. Your host is Tobias Macey and today I’m interviewing Vikrant Dubey about Cuebook and their Cuelake project for building ELT pipelines for your data lakehouse entirely in SQL

Interview

Introduction How did you get involved in the area of data management? Can you describe what Cuelake is and the story behind it? There are a number of platforms and projects for running SQL workloads and transformations on a data lake. What was lacking in those systems that you are addressing with Cuelake? Who are the target users of Cuelake and how has that influenced the features and design of the system? Can you describe how Cuelake is implemented?

What was your selection process for the various components?

What are some of the sharp edges that you have had to work around when integrating these components? What involved in getting Cuelake deployed? How are you using Cuelake in your work at Cuebook? Given your focus on machine learning for anomaly detection of business metrics, what are the challenges that you faced in using a data warehouse for those workloads?

What are the advantages that a data lake/lakehouse architecture maintains over a warehouse? What are the shortcomings of the lake/lakehouse approach that are solved by using a warehouse?

What are the most interesting, in

Summary The vast majority of data tools and platforms that you hear about are designed for working with structured, text-based data. What do you do when you need to manage unstructured information, or build a computer vision model? Activeloop was created for exactly that purpose. In this episode Davit Buniatyan, founder and CEO of Activeloop, explains why he is spending his time and energy on building a platform to simplify the work of getting your unstructured data ready for machine learning. He discusses the inefficiencies that teams run into from having to reprocess data multiple times, his work on the open source Hub library to solve this problem for everyone, and his thoughts on the vast potential that exists for using computer vision to solve hard and meaningful problems.

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. Have you ever had to develop ad-hoc solutions for security, privacy, and compliance requirements? Are you spending too much of your engineering resources on creating database views, configuring database permissions, and manually granting and revoking access to sensitive data? Satori has built the first DataSecOps Platform that streamlines data access and security. Satori’s DataSecOps automates data access controls, permissions, and masking for all major data platforms such as Snowflake, Redshift and SQL Server and even delegates data access management to business users, helping you move your organization from default data access to need-to-know access. Go to dataengineeringpodcast.com/satori today and get a $5K credit for your next Satori subscription. Your host is Tobias Macey and today I’m interviewing Davit Buniatyan about Activeloop, a platform for hosting and delivering datasets optimized for machine learning

Interview

Introduction How did you get involved in the area of data management? Can you describe what Activeloop is and the story behind it? How does the form and function of data storage introduce friction in the development and deployment of machine learning projects? How does the work that you are doing at Activeloop compare to vector databases such as Pinecone? You have a focus on image oriented data and computer vision projects. How does the specific applications of ML/DL influence the format and interactions with the data? Can you describe how the Activeloop platform is architected?

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

What are the feature and performance tradeoffs between self-managed storage locations (e.g. S3, GCS) and the Activeloop platform? What is the process for sourcing, processing, and storing

Summary All of the fancy data platform tools and shiny dashboards that you use are pointless if the consumers of your analysis don’t have trust in the answers. Stemma helps you establish and maintain that trust by giving visibility into who is using what data, annotating the reports with useful context, and understanding who is responsible for keeping it up to date. In this episode Mark Grover explains what he is building at Stemma, how it expands on the success of the Amundsen project, and why trust is the most important asset for data teams.

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 Mark Grover about his work at Stemma to bring the Amundsen project to a wider audience and increase trust in their data.

Interview

Introduction Can you describe what Stemma is and the story behind it? Can you give me more context into how and why Stemma fits into the current data engineering world? Among the popular tools of today for data warehousing and other products that stitch data together – what is Stemma’s place? Where does it fit into the workflow? How has the explosion in options for data cataloging and discovery influenced your thinking on the necessary feature set for that class of tools? How do you compare to your competitors With how long we have been using data and building systems to analyze it, why do you think that trust in the results is still such a momentous problem? Tell me more about Stemma and how it compares to Amundsen? Can you tell me more about the impact of Stemma/Amundsen to companies that use it? What are the opportunities for innovating on top of Stemma to help organizations streamline communication between data producers and consumers? Beyond the technological capabilities of a data platform, the bigger question is usually the social/organizational patterns around data. How have the "best practices" around the people side of data changed in the recent past?

What are the points of friction that

Summary Every organization needs to be able to use data to answer questions about their business. The trouble is that the data is usually spread across a wide and shifting array of systems, from databases to dashboards. The other challenge is that even if you do find the information you are seeking, there might not be enough context available to determine how to use it or what it means. Castor is building a data discovery platform aimed at solving this problem, allowing you to search for and document details about everything from a database column to a business intelligence dashboard. In this episode CTO Amaury Dumoulin shares his perspective on the complexity of letting everyone in the company find answers to their questions and how Castor is designed to help.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy! 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. Have you ever had to develop ad-hoc solutions for security, privacy, and compliance requirements? Are you spending too much of your engineering resources on creating database views, configuring database permissions, and manually granting and revoking access to sensitive data? Satori has built the first DataSecOps Platform that streamlines data access and security. Satori’s DataSecOps automates data access controls, permissions, and masking for all major data platforms such as Snowflake, Redshift and SQL Server and even delegates data access management to business users, helping you move your organization from default data access to need-to-know access. Go to dataengineeringpodcast.com/satori today and get a $5K credit for your next Satori subscription. Your host is Tobias Macey and today I’m interviewing Amaury Dumoulin about Castor, a managed platform for easy data cataloging and discovery

Interview

Introduction How did you get involved in the area of data management? Can you describe what Castor is and the story behind it? The market for data catalogues is nascent but growing fast. What are the broad categories for the different products and projects in the space? What do you see as the core features that are required to be competitive?

In what ways has that changed in

Summary Data lake architectures have largely been biased toward batch processing workflows due to the volume of data that they are designed for. With more real-time requirements and the increasing use of streaming data there has been a struggle to merge fast, incremental updates with large, historical analysis. Vinoth Chandar helped to create the Hudi project while at Uber to address this challenge. By adding support for small, incremental inserts into large table structures, and building support for arbitrary update and delete operations the Hudi project brings the best of both worlds together. In this episode Vinoth shares the history of the project, how its architecture allows for building more frequently updated analytical queries, and the work being done to add a more polished experience to the data lake paradigm.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy! 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 Vinoth Chandar about Apache Hudi, a data lake management layer for supporting fast and incremental updates to your tables.

Interview

Introduction How did you get involved in the area of data management? Can you describe what Hudi is and the story behind it? What are the use cases that it is focused on supporting? There have been a number of alternative table formats introduced for data lakes recently. How does Hudi compare to projects like Iceberg, Delta Lake, Hive, etc.? Can you describe how Hudi is architected?

How have the goals and design of Hudi changed or evolved since you first began working on it? If you were to start the whole project over today, what would you do differently?

Can you talk through the lifecycle of a data record as it is ingested, compacted, and queried in a Hudi deployment? One of the capabilities that is interesting to explore is support for arbitrary record deletion. Can you talk through why this is a challenging operation in data lake architectures?

How does Hudi make that a tractable problem?

What are the data platform components that are needed to support an installation of Hudi? What is involved in migrating an existing data lake to use Hudi?

How would someone approach supporting heterogeneous table formats in their lake?

As someone who has invested a lot of time in technologies for supporting data lakes, what are your thoughts on the tradeoffs of data lake vs data warehouse and the current trajectory of the ecosystem? What are the most interesting, innovative, or unexpected ways that you have seen Hudi used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Hudi? When is Hudi the wrong choice? What do you have planned for the future of Hudi?

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Parting Question

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

Closing Announcements

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

Links

Hudi Docs Hudi Design & Architecture Incremental Processing CDC == Change Data Capture

Podcast Episodes

Oracle GoldenGate Voldemort Kafka Hadoop Spark HBase Parquet Iceberg Table Format

Data Engineering Episode

Hive ACID Apache Kudu

Podcast Episode

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

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Summary Companies of all sizes and industries are trying to use the data that they and their customers generate to survive and thrive in the modern economy. As a result, they are relying on a constantly growing number of data sources being accessed by an increasingly varied set of users. In order to help data consumers find and understand the data is available, and help the data producers understand how to prioritize their work, SelectStar has built a data discovery platform that brings everyone together. In this episode Shinji Kim shares her experience as a data professional struggling to collaborate with her colleagues and how that led her to founding a company to address that problem. She also discusses the combination of technical and social challenges that need to be solved for everyone to gain context and comprehension around their most valuable asset.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy! 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 Shinji Kim about SelectStar, an intelligent data discovery platform that helps you understand your data

Interview

Introduction How did you get involved in the area of data management? Can you describe what SelectStar is and the story behind it? What are the core challenges that organizations are facing around data cataloging and discovery? There has been a surge in tools and services for metadata collection, data catalogs, and data collaboration. How would you characterize the current state of the ecosystem?

What is SelectStar’s role in

Summary Everyone expects data to be transmitted, processed, and updated instantly as more and more products integrate streaming data. The technology to make that possible has been around for a number of years, but the barriers to adoption have still been high due to the level of technical understanding and operational capacity that have been required to run at scale. Datastax has recently introduced a new managed offering for Pulsar workloads in the form of Astra Streaming that lowers those barriers and make stremaing workloads accessible to a wider audience. In this episode Prabhat Jha and Jonathan Ellis share the work that they have been doing to integrate streaming data into their managed Cassandra service. They explain how Pulsar is being used by their customers, the work that they have done to scale the administrative workload for multi-tenant environments, and the challenges of operating such a data intensive service at large scale. This is a fascinating conversation with a lot of useful lessons for anyone who wants to understand the operational aspects of Pulsar and the benefits that it can provide to data workloads.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy! 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 Prabhat Jha and Jonathan Ellis about Astra Streaming, a cloud-native streaming platform built on Apache Pulsar

Interview

Introduction

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

Can you describe what the Astra platform is and the story behind it?

How does streaming fit into your overall product vision and the needs of your customers?

What was your selection process/criteria for adopting a streaming engine to complement your existing technology investment?

What are the core use cases that you are aiming to support with Astra Streaming?

Can you describe the architecture and automation of your hosted platform for Pulsar?

What are the integration points that you have built to make it work well with Cassandra?

What are some of the additional tools that you have added to your distribution of Pulsar to simplify operation and use?

What are some of the sharp edges that you have had to sand down as you have scaled up your usage of Pulsar?

What is the process for someone to adopt and integrate with your Astra Streaming service?

How do you handle migrating existing projects, particularly if they are using Kafka currently?

One of the capabilities that you highlight on the product page for Astra Streaming is the ability to execute machine learning workflows on data in flight. What are some of the supporting systems that are necessary to power that workflow?

What are the capabilities that are built into Pulsar that simplify the operational aspects of streaming ML?

What are the ways that you are engaging with and supporting the Pulsar community?

What are the near to medium term elements of the Pulsar roadmap that you are working toward and excited to incorporate into Astra?

What are the most interesting, innovative, or unexpected ways that you have seen Astra used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Astra?

When is Astra the wrong choice?

What do you have planned for the future of Astra?

Contact Info

Prabhat

LinkedIn @prabhatja on Twitter prabhatja on GitHub

Jonathan

LinkedIn @spyced on Twitter

Parting Question

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

Links

Pulsar

Podcast Episode Streamnative Episode

Datastax Astra Streaming Datastax Astra DB Luna Streaming Distribution Datastax Cassandra Kesque (formerly Kafkaesque) Kafka RabbitMQ Prometheus Grafana Pulsar Heartbeat Pulsar Summit Pulsar Summit Presentation on Kafka Connectors Replicated Chaos Engineering Fallout chaos engineering tools Jepsen

Podcast Episode

Jack VanLightly

BookKeeper TLA+ Model

Change Data Capture

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

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