Summary This has been an active year for the data ecosystem, with a number of new product categories and substantial growth in existing areas. In an attempt to capture the zeitgeist Maura Church, David Wallace, Benn Stancil, and Gleb Mezhanskiy join the show to reflect on the past year and share their thought son the year to come.

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 Maura Church, David Wallace, Benn Stancil, and Gleb Mezhanskiy about the key themes of 2021 in the data ecosystem and what to expect for next year

Interview

Introduction

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

What were the main themes that you saw data practitioners and vendors focused on this year?

What is the major bottleneck for Data teams in 2021? Will it be the same in 2022? One of the ways to reason about progress in any domain is to look at what was the primary bottleneck of further progress (data adoption for decision making) at different points in time. In the data domain, we have seen a number of bottlenecks, for example, scaling data platforms, the answer to which was Hadoop and on-prem columnar stores and then cloud data warehouses such as Snowflake & BigQuery. Then the problem was data integration and transformation which was solved by data integration vendors and frameworks such as Fivetran / Airbyte, modern orchestration frameworks such as Dagster & dbt and “reverse-ETL” Hightouch. What is the main challenge now?

Will SQL be challenged as a primary interface to analytical data? In 2020 we’ve seen a few launches of post-SQL languages such as Malloy, Preql, metric layer query languages from Transform and Supergrain.

To what extent does speed matter? Over the past

Summary The data mesh is a thesis that was presented to address the technical and organizational challenges that businesses face in managing their analytical workflows at scale. Zhamak Dehghani introduced the concepts behind this architectural patterns in 2019, and since then it has been gaining popularity with many companies adopting some version of it in their systems. In this episode Zhamak re-joins the show to discuss the real world benefits that have been seen, the lessons that she has learned while working with her clients and the community, and her vision for the future of the data mesh.

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 welcoming back Zhamak Dehghani to talk about her work on the data mesh book and the lessons learned over the past 2 years

Interview

Introduction How did you get involved in the area of data management? Can you start by giving a brief recap of the principles of the data mesh and the story behind it? How has your view of the principles of the data mesh changed since our conversation in July of 2019? What are some of the ways that your work on the data mesh book influenced your thinking on the practical elements of implementing a data mesh? What do you view as the as-yet-unknown elements of the technical and social design constructs that are needed for a sustainable data mesh implementation? In the opening of your book you state that "Data Mesh is a new approach in sourcing, managing, and accessing data for analytical use cases at scale". As with everything, scale is subjective, but what are some of the heuristics that you rely on for determining when a data mesh is an appropriate solution? What are some of the ways that data mesh concepts manifest at the boundaries of organizations? While the idea of federated access to data product quanta reduces the amount of coordination necessary at the organizational level, it raises the spectre of more complex logic required for consumers of multiple quanta. How can data mesh implementations mitigate the impact of this problem? What are some of the technical components that you have found to be best suited to the implementation of data elements within a mesh? What are the technological components that are still missing for a mesh-native data platform? How should an organization that wishes to implement a mesh style architecture think about the roles and skills that they will need on staff?

How can vendors factor into the solution?

What is the role of application developers in a data mesh ecosystem and how do they need to change their thinking around the interfaces that they provide in their products? What are the most interesting, innovative, or unexpected ways that you have seen data mesh principles used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data mesh implementations? When is a data mesh the wrong approach? What do you think the future of the data mesh will look like?

Contact Info

LinkedIn @zhamakd on Twitter

Parting Question

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

Links

Data Engineering Podcast Data Mesh Interview Data Mesh Book Thoughtworks Expert Systems OpenLineage

Podcast Episode

Data Mesh Learning

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

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Summary One of the perennial challenges of data analytics is having a consistent set of definitions, along with a flexible and performant API endpoint for querying them. In this episode Artom Keydunov and Pavel Tiunov share their work on Cube.js and the various ways that it is being used in the open source 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! 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 Artyom Keydunov and Pavel Tiunov about Cube.js a framework for building analytics APIs to power your applications and BI dashboards

Interview

Introduction How did you get involved in the area of data management? Can you describe what Cube is and the story behind it? What are the main use cases and platform architectures that you are focused on?

Who are the target personas that will be using and managing Cube.js?

The name comes from the concept of an OLAP cube. Can you discuss the applications of OLAP cubes and their role in the current state of the data ecosystem?

How does the idea of an OLAP cube compare to the recent focus on a dedicated metrics layer?

What are the pieces of a data platform that might be replaced by Cube.js? Can you describe the design and architecture of the Cube platform?

How has the focus and target use case for the Cube platform evolved since you first started working on it?

One of the perpetually hard problems in computer science is cache management. How have you approached that challenge in the pre-aggregation layer of the Cube framework? What is your overarching design philosophy for the API of the Cube system? Can you talk through the workflow of someone building a cube and querying it from a downstream system?

What do the iteration cycles look like as you go from initial proof of concept to a more sophisticated usage of Cube.js

Understand the essentials of the Snowflake Database and the overall Snowflake Data Cloud. This book covers how Snowflake’s architecture is different from prior on-premises and cloud databases. The authors also discuss, from an insider perspective, how Snowflake grew so fast to become the largest software IPO of all time. Snowflake was the first database made specifically to be optimized with a cloud architecture. This book helps you get started using Snowflake by first understanding its architecture and what separates it from other database platforms you may have used. You will learn about setting up users and accounts, and then creating database objects. You will know how to load data into Snowflake and query and analyze that data, including unstructured data such as data in XML and JSON formats. You will also learn about Snowflake’s compute platform and the different data sharing options that are available. What YouWill Learn Run analytics in the Snowflake Data Cloud Create users and roles in Snowflake Set up security in Snowflake Set up resource monitors in Snowflake Set up and optimize Snowflake Compute Load, unload, and query structured and unstructured data (JSON, XML) within Snowflake Use Snowflake Data Sharing to share data Set up a Snowflake Data Exchange Use the Snowflake Data Marketplace Who This Book Is For Database professionals or information technology professionals who want to move beyond traditional database technologies by learning Snowflake, a new and massively scalable cloud-based database solution

Summary Spark is a powerful and battle tested framework for building highly scalable data pipelines. Because of its proven ability to handle large volumes of data Capital One has invested in it for their business needs. In this episode Gokul Prabagaren shares his use for it in calculating your rewards points, including the auditing requirements and how he designed his pipeline to maintain all of the necessary information through a pattern of data enrichment.

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 Gokul Prabagaren about how he is using Spark for real-world workflows at Capital One

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of the types of data and workflows that you are responsible for at Capital one?

In terms of the three "V"s (Volume, Variety, Velocity), what is the magnitude of the data that you are working with?

What are some of the business and regulatory requirements that have to be factored into the solutions that you design? Who are the consumers of the data assets that you are producing? Can you describe the technical elements of the platform that you use for managing your data pipelines? What are the various ways that you are using Spark at Capital One? You wrote a post and presented at the Databricks conference about your experience moving from a data filtering to a data enrichment pattern for segmenting transactions. Can you give some context as to the use case and what your design process was for the initial implementation?

What were the shortcomings to that approach/business requirements which led you to refactoring the approach to one that maintained all of the data through the different processing stages?

What are some of t

Where does Snowflake go from here? What meta trends and technologies play into that vision? How does that impact the world of data analytics? Christian and Tristan have no shortage of opinions or ideas. This is your chance to hear some of them, live and unfiltered. Register to catch the rest of Coalesce, the Analytics Engineering Conference, at https://coalesce.getdbt.com. The Analytics Engineering Podcast is brought to you by dbt Labs.

Summary A/B testing and experimentation are the most reliable way to determine whether a change to your product will have the desired effect on your business. Unfortunately, being able to design, deploy, and validate experiments is a complex process that requires a mix of technical capacity and organizational involvement which is hard to come by. Chetan Sharma founded Eppo to provide a system that organizations of every scale can use to reduce the burden of managing experiments so that you can focus on improving your business. In this episode he digs into the technical, statistical, and design requirements for running effective experiments and how he has architected the Eppo platform to make the process more accessible to business and data professionals.

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 Chetan Sharma about Eppo, a platform for building A/B experiments that are easier to manage

Interview

Introduction How did you get involved in the area of data management? Can you describe what Eppo is and the story behind it? What are some examples of the kinds of experiments that teams and organizations might want to conduct? What are the points of friction that What are the steps involved in designing, deploying, and analyzing the outcomes of an A/B experiment?

What are some of the statistical errors that are common when conducting an experiment?

What are the design and UX principles that you have focused on in Eppo to improve the workflow of building and analyzing experiments? Can you describe the system design of the Eppo platform?

What are the services or capabilities external to Eppo that are required for it to be effective? What are the integration points for adding Eppo to an organization’s existing platform?

B

Summary The modern data stack has been gaining a lot of attention recently with a rapidly growing set of managed services for different stages of the data lifecycle. With all of the available options it is possible to run a scalable, production grade data platform with a small team, but there are still sharp edges and integration challenges to work through. Peter Fishman and Dan Silberman experienced these difficulties firsthand and created Mozart Data to provide a single, easy to use option for getting started with the modern data stack. In this episode they explain how they designed a user experience to make working with data more accessibly by organizations without a data team, while allowing for more advanced users to build out more complex workflows. They also share their thoughts on the modern data ecosystem and how it improves the availability of analytics for companies of all sizes.

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 Peter Fishman and Dan Silberman about Mozart Data and how they are building a unified experience for the modern data stack

Interview

Introduction How did you get involved in the area of data management? Can you describe what Mozart Data is and the story behind it? The promise of the "modern data stack" is that it’s all delivered as a service to make it easier to set up. What are the missing pieces that make something like Mozart necessary? What are the main workflows or industries that you are focusing on? Who are the main personas that you are building Mozart for?

How has that combination of user persona and industry focus informed your decisions around feature priorities and user experience?

Can you describe how you have architected the Mozart platform?

How have you approached the bu

Summary One of the perennial challenges posed by data lakes is how to keep them up to date as new data is collected. With the improvements in streaming engines it is now possible to perform all of your data integration in near real time, but it can be challenging to understand the proper processing patterns to make that performant. In this episode Ori Rafael shares his experiences from Upsolver and building scalable stream processing for integrating and analyzing data, and what the tradeoffs are when coming from a batch oriented mindset.

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 Ori Rafael about strategies for building stream and batch processing patterns for data lake analytics

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of the state of the market for data lakes today?

What are the prevailing architectural and technological patterns that are being used to manage these systems?

Batch and streaming systems have been used in various combinations since the early days of Hadoop. The Lambda architecture has largely been abandoned, so what is the answer for today’s data lakes? What are the challenges presented by streaming approaches to data transformations?

The batch model for processing is intuitive despite its latency problems. What are the benefits that it provides?

The core concept for data orchestration is the DAG. How does that manifest in a streaming context? In batch processing idempotent/immutable datasets are created by re-running the entire pipeline when logic changes need to be made. Given that there is no definitive start or end of a stream, what are the options for amending logical errors in transformations? What are some of the da

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 Business intelligence is often equated with a collection of dashboards that show various charts and graphs representing data for an organization. What is overlooked in that characterization is the level of complexity and effort that are required to collect and present that information, and the opportunities for providing those insights in other contexts. In this episode Telmo Silva explains how he co-founded ClicData to bring full featured business intelligence and reporting to every organization without having to build and maintain that capability on their own. This is a great conversation about the technical and organizational operations involved in building a comprehensive business intelligence system and the current state of the market.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. 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 Telmo Silva about ClicData,

Interview

Introduction How did you get involved in the area of data management? Can you describe what ClicData is and the story behind it? How would you characterize the current state of the market for business intelligence?

What are the systems/capabilities that are required to run a full-featured BI system?

What are the challenges that businesses face in developing in-house capacity for business intelligence? Can you describe how the ClicData platform is architected?

How has it changed or evolved since you first began working on it?

How are you approaching schema design and evolution in the storage layer? How do you handle questions of data security/privacy/regulations given that you are storing the information on behalf of the business? In your work with clients what are some of the challenges that businesses are facing when attempting to answer questions and gain insights from their data in a rep

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 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 Organizations of all sizes are striving to become data driven, starting in earnest with the rise of big data a decade ago. With the never-ending growth in data sources and methods for aggregating and analyzing them, the use of data to direct the business has become a requirement. Randy Bean has been helping enterprise organizations define and execute their data strategies since before the age of big data. In this episode he discusses his experiences and how he approached the work of distilling them for his book "Fail Fast, Learn Faster". This is an entertaining and enlightening exploration of the business side of data with an industry veteran.

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 Randy Bean about his recent book focusing on the use of big data and AI for informing data driven business leadership

Interview

Introduction How did you get involved in the area of data management? Can you start by discussing the focus of the book and what motivated you to write it?

Who is the intended audience, and how did that inform the tone and content?

Businesses and their officers have been aiming to be "data driven" for years. In your experience, what are the concrete goals that are implied by that term?

What are the barriers that organizations encounter in the pursuit of those goals? How have the success rates (real and imagined) shifted in recent years as the level of sophisticatio

Summary The key to making data valuable to business users is the ability to calculate meaningful metrics and explore them along useful dimensions. Business intelligence tools have provided this capability for years, but they don’t offer a means of exposing those metrics to other systems. Metriql is an open source project that provides a headless BI system where you can define your metrics and share them with all of your other processes. In this episode Burak Kabakcı shares the story behind the project, how you can use it to create your metrics definitions, and the benefits of treating the semantic layer as a dedicated component of your 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 $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. 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 Burak Emre Kabakcı about Metriql, a headless BI and metrics layer for your data stack

Interview

Introduction How did you get involved in the area of data management? Can you describe what Metriql is and the story behind it? What are the characteristics and benefits of a "headless BI" system? What was your motivation to create and open-source Metriql as an independent project outside of your business?

How are you approaching governance and sustainability of the project?

How does Metriql compare to projects such as AirBnB’s Minerva or Transform’s platform? How does the industry/vertical of a business impact their ability to benefit from a metrics layer/headless BI?

What are the limitations to the logical complexity that can be applied to the calculation of a given metric/set of metrics?

Can you describe how Metriql is implemented?

How have the design and goals of the project changed or evolved since you began worki

Summary Transactions are a necessary feature for ensuring that a set of actions are all performed as a single unit of work. In streaming systems this is necessary to ensure that a set of messages or transformations are all executed together across different queues. In this episode Denis Rystsov explains how he added support for transactions to the Redpanda streaming engine. He discusses the use cases for transactions, the different strategies, semantics, and guarantees that they might need to support, and how his implementation ended up improving the performance of bulk write operations. This is an interesting deep dive into the internals of a high performance streaming engine and the details that are involved in building distributed 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! 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 Denis Rystsov about implementing transactions in the RedPanda streaming engine

Interview

Introduction How did you get involved in the area of data management? Can you quickly recap what RedPanda is and the goals of the project? What are the use cases for transactions in a pub/sub messaging system?

What are the elements of streaming systems that make atomic transactions a complex problem?

What was the motivation for starting down the path of adding transactions to the RedPanda engine?

How did the constraint of supporting the Kafka API influence your implementation strategy for transaction semantics?

This book is your complete guide to Snowflake security, covering account security, authentication, data access control, logging and monitoring, and more. It will help you make sure that you are using the security controls in a right way, are on top of access control, and making the most of the security features in Snowflake. Snowflake is the fastest growing cloud data warehouse in the world, and having the right methodology to protect the data is important both to data engineers and security teams. It allows for faster data enablement for organizations, as well as reducing security risks, meeting compliance requirements, and solving data privacy challenges. There are currently tens of thousands of people who are either data engineers/data ops in Snowflake-using organizations, or security people in such organizations. This book provides guidance when you want to apply certain capabilities, such as data masking, row-level security, column-level security, tackling rolehierarchy, building monitoring dashboards, etc., to your organizations. What You Will Learn Implement security best practices for Snowflake Set up user provisioning, MFA, OAuth, and SSO Set up a Snowflake security model Design roles architecture Use advanced access control such as row-based security and dynamic masking Audit and monitor your Snowflake Data Cloud Who This Book Is For Data engineers, data privacy professionals, and security teams either with security knowledge (preferably some data security knowledge) or with data engineering knowledge; in other words, either “Snowflake people” or “data people” who want to get security right, or “security people” who want to make sure that Snowflake gets handled right in terms of security

Summary Aerospike is a database engine that is designed to provide millisecond response times for queries across terabytes or petabytes. In this episode Chief Strategy Officer, Lenley Hensarling, explains how the ability to process these large volumes of information in real-time allows businesses to unlock entirely new capabilities. He also discusses the technical implementation that allows for such extreme performance and how the data model contributes to the scalability of the system. If you need to deal with massive data, at high velocities, in milliseconds, then Aerospike is definitely worth learning about.

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 or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold’s proactive approach to data quality 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. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. Your host is Tobias Macey and today I’m interviewing Lenley Hensarling about Aerospike and building real-time data platforms

Interview

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

What are the use cases that it is uniquely well suited for? What are the use cases that you and the Aerospike team are focusing on and how does that influence your focus on priorities of feature development and user experience?

What are the driving factors for building a real-time data platform? How is Aerospike being incorporated in application and data architectures? Can you describe how the Aerospike engine is architected?

How have the design and architecture changed or evolved since it was first created? How have market forces influenced the product priorities and focus?

What are the challenges that end users face when determining how to model their data given a key/value storage interface?

What are the abstrac

Summary The promise of online services is that they will make your life easier in exchange for collecting data about you. The reality is that they use more information than you realize for purposes that are not what you intended. There have been many attempts to harness all of the data that you generate for gaining useful insights about yourself, but they are generally difficult to set up and manage or require software development experience. The team at Prifina have built a platform that allows users to create their own personal data cloud and install applications built by developers that power useful experiences while keeping you in full control. In this episode Markus Lampinen shares the goals and vision of the company, the technical aspects of making it a reality, and the future vision for how services can be designed to respect user’s privacy while still providing compelling experiences.

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 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! Your host is Tobias Macey and today I’m interviewing Markus Lampinen about Prifina, a platform for building applications powered by personal data that is under the user’s control

Interview

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

What are the primary goals of Prifina?

There has been a lof of interest in the "quantified self" and different projects (many that are open source) which aim to aggregate all of a user

Summary The accuracy and availability of data has become critically important to the day-to-day operation of businesses. Similar to the practice of site reliability engineering as a means of ensuring consistent uptime of web services, there has been a new trend of building data reliability engineering practices in companies that rely heavily on their data. In this episode Egor Gryaznov explains how this practice manifests from a technical and organizational perspective and how you can start adopting it in your own 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! 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 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. Your host is Tobias Macey and today I’m interviewing Egor Gryaznov, co-founder and CTO of Bigeye, about the ideas and practices of data reliability engineering and how to integrate it into your systems

Interview

Introduction How did you get involved in the area of data management? What does the term "Data Reliability Engineering" mean? What is encompassed under the umbrella of Data Reliability Engineering?

How does it compare to the concepts from site reliability engineering? Is DRE just a repackaged version of DataOps?

Why is Data Reliability Engineering particularly important now? Who is responsible for the practice of DRE in an organization? What are some areas of innovation that teams are focusing on to support a DRE practice? What are the tools that teams are using to improve the reliability of their data operations? What are the organizational systems that need to be in place to support a DRE practice?

What are some potential roadblocks that teams might have to address when planning and implementing a DRE strategy?

What are the most interesting, innovative, or unexpected approaches/solutions to DRE that you have seen? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Data Reliability Engineering? Is Data Reliability Engi