Summary The market for data warehouse platforms is large and varied, with options for every use case. ClickHouse is an open source, column-oriented database engine built for interactive analytics with linear scalability. In this episode Robert Hodges and Alexander Zaitsev explain how it is architected to provide these features, the various unique capabilities that it provides, and how to run it in production. It was interesting to learn about some of the custom data types and performance optimizations that are included.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Integrating data across the enterprise has been around for decades – so have the techniques to do it. But, a new way of integrating data and improving streams has evolved. By integrating each silo independently – data is able to integrate without any direct relation. At CluedIn they call it “eventual connectivity”. If you want to learn more on how to deliver fast access to your data across the enterprise leveraging this new method, and the technologies that make it possible, get a demo or presentation of the CluedIn Data Hub by visiting dataengineeringpodcast.com/cluedin. And don’t forget to thank them for supporting the show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Robert Hodges and Alexander Zaitsev about Clickhouse, an open source, column-oriented database for fast and scalable OLAP queries

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Clickhouse is and how you each got involved with it?

What are the primary use cases that Clickhouse is targeting? Where does it fit in the database market and how does it compare to other column stores, both open source and commercial?

Can you describe how Clickhouse is architected? Can you talk through the lifecycle of a given record or set of records from when they first get inserted into Clickhouse, through the engine an

Summary Building and maintaining a data lake is a choose your own adventure of tools, services, and evolving best practices. The flexibility and freedom that data lakes provide allows for generating significant value, but it can also lead to anti-patterns and inconsistent quality in your analytics. Delta Lake is an open source, opinionated framework built on top of Spark for interacting with and maintaining data lake platforms that incorporates the lessons learned at DataBricks from countless customer use cases. In this episode Michael Armbrust, the lead architect of Delta Lake, explains how the project is designed, how you can use it for building a maintainable data lake, and some useful patterns for progressively refining the data in your lake. This conversation was useful for getting a better idea of the challenges that exist in large scale data analytics, and the current state of the tradeoffs between data lakes and data warehouses in the cloud.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! And to keep track of how your team is progressing on building new pipelines and tuning their workflows, you need a project management system designed by engineers, for engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Data Engineering Podcast listeners get 2 months free on any plan by going to dataengineeringpodcast.com/clubhouse today and signing up for a free trial. Support the show and get your data projects in order! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Michael Armbrust about Delta Lake, an open source storage layer that brings ACID transactions to Apache Spark and big data workloads.

Interview

Introduction How did you get involved in the area of data m

Summary Some problems in data are well defined and benefit from a ready-made set of tools. For everything else, there’s Pachyderm, the platform for data science that is built to scale. In this episode Joe Doliner, CEO and co-founder, explains how Pachyderm started as an attempt to make data provenance easier to track, how the platform is architected and used today, and examples of how the underlying principles manifest in the workflows of data engineers and data scientists as they collaborate on data projects. In addition to all of that he also shares his thoughts on their recent round of fund-raising and where the future will take them. If you are looking for a set of tools for building your data science workflows then Pachyderm is a solid choice, featuring data versioning, first class tracking of data lineage, and language agnostic data pipelines.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave

Summary In recent years the traditional approach to building data warehouses has shifted from transforming records before loading, to transforming them afterwards. As a result, the tooling for those transformations needs to be reimagined. The data build tool (dbt) is designed to bring battle tested engineering practices to your analytics pipelines. By providing an opinionated set of best practices it simplifies collaboration and boosts confidence in your data teams. In this episode Drew Banin, creator of dbt, explains how it got started, how it is designed, and how you can start using it today to create reliable and well-tested reports in your favorite data warehouse.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Drew Banin about DBT, the Data Build Tool, a toolkit for building analytics the way that developers build applications

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what DBT is and your motivation for creating it? Where does it fit in the overall landscape of data tools and the lifecycle of data in an analytics pipeline? Can you talk through the workflow for someone using DBT? One of the useful features of DBT for stability of analytics is the ability to write and execute tests. Can you explain how those are implemented? The packaging capabilities are beneficial for enabling collaboration. Can you talk through how the packaging system is implemented?

Are these packages driven by Fishtown Analytics or the dbt community?

What are the limitations of modeling everything as a SELECT statement? Making SQL code reusable is notoriously difficult. How does the Jinja templating of DBT address this issue and what are the shortcomings?

What are your thoughts on higher level approaches to SQL that compile down to the specific statements?

Can you explain how DBT is implemented and how the design has evolved since you first began working on it? What are some of the features of DBT that are often overlooked which you find particularly useful? What are some of the most interesting/unexpected/innovative ways that you have seen DBT used? What are the additional features that the commercial version of DBT provides? What are some of the most useful or challenging lessons that you have learned in the process of building and maintaining DBT? When is it the wrong choice? What do you have planned for the future of DBT?

Contact Info

Email @drebanin on Twitter drebanin on GitHub

Parting Question

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

Links

DBT Fishtown Analytics 8Tracks Internet Radio Redshift Magento Stitch Data Fivetran Airflow Business Intelligence Jinja template language BigQuery Snowflake Version Control Git Continuous Integration Test Driven Development Snowplow Analytics

Podcast Episode

dbt-utils We Can Do Better Than SQL blog post from EdgeDB EdgeDB Looker LookML

Podcast Interview

Presto DB

Podcast Interview

Spark SQL Hive Azure SQL Data Warehouse Data Warehouse Data Lake Data Council Conference Slowly Changing Dimensions dbt Archival Mode Analytics Periscope BI dbt docs dbt repository

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

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Summary The database market continues to expand, offering systems that are suited to virtually every use case. But what happens if you need something customized to your application? FoundationDB is a distributed key-value store that provides the primitives that you need to build a custom database platform. In this episode Ryan Worl explains how it is architected, how to use it for your applications, and provides examples of system design patterns that can be built on top of it. If you need a foundation for your distributed systems, then FoundationDB is definitely worth a closer look.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Ryan Worl about FoundationDB, a distributed key/value store that gives you t

Summary Kubernetes is a driving force in the renaissance around deploying and running applications. However, managing the database layer is still a separate concern. The KubeDB project was created as a way of providing a simple mechanism for running your storage system in the same platform as your application. In this episode Tamal Saha explains how the KubeDB project got started, why you might want to run your database with Kubernetes, and how to get started. He also covers some of the challenges of managing stateful services in Kubernetes and how the fast pace of the community has contributed to the evolution of KubeDB. If you are at any stage of a Kubernetes implementation, or just thinking about it, this is definitely worth a listen to get some perspective on how to leverage it for your entire application stack.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your fri

Summary One of the biggest challenges for any business trying to grow and reach customers globally is how to scale their data storage. FaunaDB is a cloud native database built by the engineers behind Twitter’s infrastructure and designed to serve the needs of modern systems. Evan Weaver is the co-founder and CEO of Fauna and in this episode he explains the unique capabilities of Fauna, compares the consensus and transaction algorithm to that used in other NewSQL systems, and describes the ways that it allows for new application design patterns. One of the unique aspects of Fauna that is worth drawing attention to is the first class support for temporality that simplifies querying of historical states of the data. It is definitely worth a good look for anyone building a platform that needs a simple to manage data layer that will scale with your business.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Evan Weaver about FaunaDB, a modern operational data platform built for your cloud

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what FaunaDB is and how it got started? What are some of the main use cases that FaunaDB is targeting?

How does it compare to some of the other global scale databases that have been built in recent years such as CockroachDB?

Can you describe the architecture of FaunaDB and how it has evolved? The consensus and replication protocol in Fauna is intriguing. Can you talk through how it works?

What are some of the edge cases that users should be aware of? How are conflicts managed in Fauna?

What is the underlying storage layer?

How is the query layer designed to allow for different query patterns and model representations?

How does data modeling in Fauna compare to that of relational or document databases?

Can you describe the query format? What are some of the common difficulties or points of confusion around interacting with data in Fauna?

What are some application design patterns that are enabled by using Fauna as the storage layer? Given the ability to replicate globally, how do you mitigate latency when interacting with the database? What are some of the most interesting or unexpected ways that you have seen Fauna used? When is it the wrong choice? What have been some of the most interesting/unexpected/challenging aspects of building the Fauna database and company? What do you have in store for the future of Fauna?

Contact Info

@evan on Twitter LinkedIn

Parting Question

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

Links

Fauna Ruby on Rails CNET GitHub Twitter NoSQL Cassandra InnoDB Redis Memcached Timeseries Spanner Paper DynamoDB Paper Percolator ACID Calvin Protocol Daniel Abadi LINQ LSM Tree (Log-structured Merge-tree) Scala Change Data Capture GraphQL

Podcast.init Interview About Graphene

Fauna Query Language (FQL) CQL == Cassandra Query Language Object-Relational Databases LDAP == Lightweight Directory Access Protocol Auth0 OLAP == Online Analytical Processing Jepsen distributed systems safety research

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

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Summary Database indexes are critical to ensure fast lookups of your data, but they are inherently tied to the database engine. Pilosa is rewriting that equation by providing a flexible, scalable, performant engine for building an index of your data to enable high-speed aggregate analysis. In this episode Seebs explains how Pilosa fits in the broader data landscape, how it is architected, and how you can start using it for your own analysis. This was an interesting exploration of a different way to look at what a database can be.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Seebs about Pilosa, an open source, distributed bitmap index

Interview

Introduction How did you get involved in the area of data

Summary How much time do you spend maintaining your data pipeline? How much end user value does that provide? Raghu Murthy founded DataCoral as a way to abstract the low level details of ETL so that you can focus on the actual problem that you are trying to solve. In this episode he explains his motivation for building the DataCoral platform, how it is leveraging serverless computing, the challenges of delivering software as a service to customer environments, and the architecture that he has designed to make batch data management easier to work with. This was a fascinating conversation with someone who has spent his entire career working on simplifying complex data 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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Raghu Murthy about DataCoral, a platform that offers a fully managed and secure stack in your own cloud that delivers data to where you need it

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what DataCoral is and your motivation for founding it? How does the data-centric approa

Summary Analytics projects fail all the time, resulting in lost opportunities and wasted resources. There are a number of factors that contribute to that failure and not all of them are under our control. However, many of them are and as data engineers we can help to keep our projects on the path to success. Eugene Khazin is the CEO of PrimeTSR where he is tasked with rescuing floundering analytics efforts and ensuring that they provide value to the business. In this episode he reflects on the ways that data projects can be structured to provide a higher probability of success and utility, how data engineers can get throughout the project lifecycle, and how to salvage a failed project so that some value can be gained from the effort.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Eugene Khazin about the leading causes for failure in analytics projects

Interview

Introduction How did you get involved in the area of data management? The term "analytics" has grown to mean many different things to different people, so can you start by sharing your definition of what is in scope for an "analytics project" for the purposes of this discussion?

Wh

Summary Data integration is one of the most challenging aspects of any data platform, especially as the variety of data sources and formats grow. Enterprise organizations feel this acutely due to the silos that occur naturally across business units. The CluedIn team experienced this issue first-hand in their previous roles, leading them to build a business aimed at building a managed data fabric for the enterprise. In this episode Tim Ward, CEO of CluedIn, joins me to explain how their platform is architected, how they manage the task of integrating with third-party platforms, automating entity extraction and master data management, and the work of providing multiple views of the same data for different use cases. I highly recommend listening closely to his explanation of how they manage consistency of the data that they process across different storage backends.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Tim Ward about CluedIn, an integration platform for implementing your companies data fabric

Interview

Introduction

How did you get involved in t

Summary Delivering a data analytics project on time and with accurate information is critical to the success of any business. DataOps is a set of practices to increase the probability of success by creating value early and often, and using feedback loops to keep your project on course. In this episode Chris Bergh, head chef of Data Kitchen, explains how DataOps differs from DevOps, how the industry has begun adopting DataOps, and how to adopt an agile approach to building your 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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. "There aren’t enough data conferences out there that focus on the community, so that’s why these folks built a better one": Data Council is the premier community powered data platforms & engineering event for software engineers, data engineers, machine learning experts, deep learning researchers & artificial intelligence buffs who want to discover tools & insights to build new products. This year they will host over 50 speakers and 500 attendees (yeah that’s one of the best "Attendee:Speaker" ratios out there) in San Francisco on April 17-18th and are offering a $200 discount to listeners of the Data Engineering Podcast. Use code: DEP-200 at checkout You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Chris Bergh about the current state of DataOps and why it’s more than just DevOps for data

Interview

Introduction How did you get involved in the area of data management? We talked last year about what DataOps is, but can you give a quick overview of how the industry has changed or updated the definition since then?

It is easy to draw parallels between DataOps and DevOps, can you provide some clarity as to how they are different?

How has the conversat

Summary Customer analytics is a problem domain that has given rise to its own industry. In order to gain a full understanding of what your users are doing and how best to serve them you may need to send data to multiple services, each with their own tracking code or APIs. To simplify this process and allow your non-engineering employees to gain access to the information they need to do their jobs Segment provides a single interface for capturing data and routing it to all of the places that you need it. In this interview Segment CTO and co-founder Calvin French-Owen explains how the company got started, how it manages to multiplex data streams from multiple sources to multiple destinations, and how it can simplify your work of gaining visibility into how your customers are engaging with your business.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Your host is Tobias Macey and today I’m interviewing Calvin French-Owen about the data platform that Segment has built to handle multiplexing continuous streams of data from multiple sources to multiple destinations

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Segment is and how the business got started?

What are some of the primary ways that your customers are using the Segment platform? How have the capabilities and use cases of the Segment platform changed since it was first launched?

Layered on top of the data integration platform you have added the concepts of Protocols and Personas. Can you explain how each of those products fit into the over

Summary Deep learning is the latest class of technology that is gaining widespread interest. As data engineers we are responsible for building and managing the platforms that power these models. To help us understand what is involved, we are joined this week by Thomas Henson. In this episode he shares his experiences experimenting with deep learning, what data engineers need to know about the infrastructure and data requirements to power the models that your team is building, and how it can be used to supercharge our ETL pipelines.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th, both run by our friends at O’Reilly Media. Go to dataengineeringpodcast.com/stratacon and dataengineeringpodcast.com/aicon to register today and get 20% off Your host is Tobias Macey and today I’m interviewing Thomas Henson about what data engineers need to know about deep learning, including how to use it for their own projects

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what deep learning is for anyone who isn’t familiar with it? What has been your personal experience with deep learning and what set you down that path? What is involved in building a data pipeline and production infrastructure for a deep learning product?

How does that differ from other types of analytics projects such as data warehousing or traditional ML?

For anyone who is in the early stages of a deep learning project, what are some of the edge cases or gotchas that they should be aware of? What are your opinions on the level of involvement/understanding that data engineers should have with the analytical products that are being built with the information we collect and curate? What are some ways that we can use deep learning as part of the data management process?

How does that shift the infrastructure requirements for our platforms?

Cloud providers have b

Summary Distributed storage systems are the foundational layer of any big data stack. There are a variety of implementations which support different specialized use cases and come with associated tradeoffs. Alluxio is a distributed virtual filesystem which integrates with multiple persistent storage systems to provide a scalable, in-memory storage layer for scaling computational workloads independent of the size of your data. In this episode Bin Fan explains how he got involved with the project, how it is implemented, and the use cases that it is particularly well suited for. If your storage and compute layers are too tightly coupled and you want to scale them independently then Alluxio is the tool for the job.

Introduction

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 Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Bin Fan about Alluxio, a distributed virtual filesystem for unified access to disparate data sources

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Alluxio is and the history of the project?

What are some of the use cases that Alluxio enables?

How is Alluxio implemented and how has its architecture evolved over time?

What are some of the techniques that you use to mitigate the impact of latency, particularly when interfacing with storage systems across cloud providers and private data centers?

When dealing with large volumes of data over time it is often necessary to age out older records to cheaper storage. What capabilities does Alluxio provide for that lifecycle management? What are some of the most complex or challenging aspects of providing a unified abstraction across disparate storage platforms?

What are the tradeoffs that are made to provide a single API across systems with varying capabilities?

Testing and verification of distributed systems is a complex undertaking. Can you describe the approach that you use to ensure proper functionality of Alluxio as part of the development and release process?

In order to allow for this large scale testing with any regularity it must be straightforward to deploy and configure Alluxio. What are some of the mechanisms that you have built into the platform to simplify the operational aspects?

Can you describe a typical system topology that incorporates Alluxio? For someone planning a deployment of Alluxio, what should they be considering in terms of system requirements and deployment topologies?

What are some edge cases or operational complexities that they should be aware of?

What are some cases where Alluxio is the wrong choice?

What are some projects or products that provide a similar capability to Alluxio?

What do you have planned for the future of the Alluxio project and company?

Contact Info

LinkedIn @binfan on Twitter

Parting Question

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

Links

Alluxio

Project Company

Carnegie Me

Summary Building internal expertise around big data in a large organization is a major competitive advantage. However, it can be a difficult process due to compliance needs and the need to scale globally on day one. In this episode Jesper Søgaard and Keld Antonsen share the story of starting and growing the big data group at LEGO. They discuss the challenges of being at global scale from the start, hiring and training talented engineers, prototyping and deploying new systems in the cloud, and what they have learned in the process. This is a useful conversation for engineers, managers, and leadership who are interested in building enterprise big data systems.

Preamble

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 Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Keld Antonsen and Jesper Soegaard about the data infrastructure and analytics that powers LEGO

Interview

Introduction How did you get involved in the area of data management? My understanding is that the big data group at LEGO is a fairly recent development. Can you share the story of how it got started?

What kinds of data practices were in place prior to starting a dedicated group for managing the organization’s data? What was the transition process like, migrating data silos into a uniformly managed platform?

What are the biggest data challenges that you face at LEGO? What are some of the most critical sources and types of data that you are managing? What are the main components of the data infrastructure that you have built to support the organizations analytical needs?

What are some of the technologies that you have found to be most useful? Which have been the most problematic?

What does the team structure look like for the data services at LEGO?

Does that reflect in the types/numbers of systems that you support?

What types of testing, monitoring, and metrics do you use to ensure the health of the systems you support? What have been some of the most interesting, challenging, or useful lessons that you have learned while building and maintaining the data platforms at LEGO? How have the data systems at Lego evolved over recent years as new technologies and techniques have been developed? How does the global nature of the LEGO business influence the design strategies and technology choices for your platform? What are you most excited for in the coming year?

Contact Info

Jesper

LinkedIn

Keld

LinkedIn

Parting Question

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

Links

LEGO Group ERP (Enterprise Resource Planning) Predictive Analytics Prescriptive Analytics Hadoop Center Of Excellence Continuous Integration Spark

Podcast Episode

Apache NiFi

Podcast Episode

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

Support Data Engineering Podcast

Summary

The past year has been an active one for the timeseries market. New products have been launched, more businesses have moved to streaming analytics, and the team at Timescale has been keeping busy. In this episode the TimescaleDB CEO Ajay Kulkarni and CTO Michael Freedman stop by to talk about their 1.0 release, how the use cases for timeseries data have proliferated, and how they are continuing to simplify the task of processing your time oriented events.

Introduction

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 Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m welcoming Ajay Kulkarni and Mike Freedman back to talk about how TimescaleDB has grown and changed over the past year

Interview

Introduction How did you get involved in the area of data management? Can you refresh our memory about what TimescaleDB is? How has the market for timeseries databases changed since we last spoke? What has changed in the focus and features of the TimescaleDB project and company? Toward the end of 2018 you launched the 1.0 release of Timescale. What were your criteria for establishing that milestone?

What were the most challenging aspects of reaching that goal?

In terms of timeseries workloads, what are some of the factors that differ across varying use cases?

How do those differences impact the ways in which Timescale is used by the end user, and built by your team?

What are some of the initial assumptions that you made while first launching Timescale that have held true, and which have been disproven? How have the improvements and new features in the recent releases of PostgreSQL impacted the Timescale product?

Have you been able to leverage some of the native improvements to simplify your implementation? Are there any use cases for Timescale that would have been previously impractical in vanilla Postgres that would now be reasonable without the help of Timescale?

What is in store for the future of the Timescale product and organization?

Contact Info

Ajay

@acoustik on Twitter LinkedIn

Mike

LinkedIn Website @michaelfreedman on Twitter

Timescale

Website Documentation Careers timescaledb on GitHub @timescaledb on Twitter

Parting Question

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

Links

TimescaleDB Original Appearance on the Data Engineering Podcast 1.0 Release Blog Post PostgreSQL

Podcast Interview

RDS DB-Engines MongoDB IOT (Internet Of Things) AWS Timestream Kafka Pulsar

Podcast Episode

Spark

Podcast Episode

Flink

Podcast Episode

Hadoop DevOps PipelineDB

Podcast Interview

Grafana Tableau Prometheus OLTP (Online Transaction Processing) Oracle DB Data Lake

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

Summary

The Hadoop platform is purpose built for processing large, slow moving data in long-running batch jobs. As the ecosystem around it has grown, so has the need for fast data analytics on fast moving data. To fill this need the Kudu project was created with a column oriented table format that was tuned for high volumes of writes and rapid query execution across those tables. For a perfect pairing, they made it easy to connect to the Impala SQL engine. In this episode Brock Noland and Jordan Birdsell from PhData explain how Kudu is architected, how it compares to other storage systems in the Hadoop orbit, and how to start integrating it into you analytics pipeline.

Preamble

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 Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Brock Noland and Jordan Birdsell about Apache Kudu and how it is able to provide fast analytics on fast data in the Hadoop ecosystem

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Kudu is and the motivation for building it?

How does it fit into the Hadoop ecosystem? How does it compare to the work being done on the Iceberg table format?

What are some of the common application and system design patterns that Kudu supports? How is Kudu architected and how has it evolved over the life of the project? There are many projects in and around the Hadoop ecosystem that rely on Zookeeper as a building block for consensus. What was the reasoning for using Raft in Kudu? How does the storage layer in Kudu differ from what would be found in systems like Hive or HBase?

What are the implementation details in the Kudu storage interface that have had the greatest impact on its overall speed and performance?

A number of the projects built for large scale data processing were not initially built with a focus on operational simplicity. What are the features of Kudu that simplify deployment and management of production infrastructure? What was the motivation for using C++ as the language target for Kudu?

If you were to start the project over today what would you do differently?

What are some situations where you would advise against using Kudu? What have you found to be the most interesting/unexpected/challenging lessons learned in the process of building and maintaining Kudu? What are you most excited about for the future of Kudu?

Contact Info

Brock

LinkedIn @brocknoland on Twitter

Jordan

LinkedIn @jordanbirdsell jbirdsell on GitHub

PhData

Website phdata on GitHub @phdatainc on Twitter

Parting Question

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

Links

Kudu PhData Getting Started with Apache Kudu Thomson Reuters Hadoop Oracle Exadata Slowly Changing Dimensions HDFS S3 Azure Blob Storage State Farm Stanly Black & Decker ETL (Extract, Transform, Load) Parquet

Podcast Episode

ORC HBase Spark

Podcast Episode

Summary

Every business with a website needs some way to keep track of how much traffic they are getting, where it is coming from, and which actions are being taken. The default in most cases is Google Analytics, but this can be limiting when you wish to perform detailed analysis of the captured data. To address this problem, Alex Dean co-founded Snowplow Analytics to build an open source platform that gives you total control of your website traffic data. In this episode he explains how the project and company got started, how the platform is architected, and how you can start using it today to get a clearer view of how your customers are interacting with your web and mobile applications.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat This is your host Tobias Macey and today I’m interviewing Alexander Dean about Snowplow Analytics

Interview

Introductions How did you get involved in the area of data engineering and data management? What is Snowplow Analytics and what problem were you trying to solve when you started the company? What is unique about customer event data from an ingestion and processing perspective? Challenges with properly matching up data between sources Data collection is one of the more difficult aspects of an analytics pipeline because of the potential for inconsistency or incorrect information. How is the collection portion of the Snowplow stack designed and how do you validate the correctness of the data?

Cleanliness/accuracy

What kinds of metrics should be tracked in an ingestion pipeline and how do you monitor them to ensure that everything is operating properly? Can you describe the overall architecture of the ingest pipeline that Snowplow provides?

How has that architecture evolved from when you first started? What would you do differently if you were to start over today?

Ensuring appropriate use of enrichment sources What have been some of the biggest challenges encountered while building and evolving Snowplow? What are some of the most interesting uses of your platform that you are aware of?

Keep In Touch

Alex

@alexcrdean on Twitter LinkedIn

Snowplow

@snowplowdata on Twitter

Parting Question

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

Links

Snowplow

GitHub

Deloitte Consulting OpenX Hadoop AWS EMR (Elastic Map-Reduce) Business Intelligence Data Warehousing Google Analytics CRM (Customer Relationship Management) S3 GDPR (General Data Protection Regulation) Kinesis Kafka Google Cloud Pub-Sub JSON-Schema Iglu IAB Bots And Spiders List Heap Analytics

Podcast Interview

Redshift SnowflakeDB Snowplow Insights Googl

Summary

Web and mobile analytics are an important part of any business, and difficult to get right. The most frustrating part is when you realize that you haven’t been tracking a key interaction, having to write custom logic to add that event, and then waiting to collect data. Heap is a platform that automatically tracks every event so that you can retroactively decide which actions are important to your business and easily build reports with or without SQL. In this episode Dan Robinson, CTO of Heap, describes how they have architected their data infrastructure, how they build their tracking agents, and the data virtualization layer that enables users to define their own labels.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Dan Robinson about Heap and their approach to collecting, storing, and analyzing large volumes of data

Interview

Introduction How did you get involved in the area of data management? Can you start by giving a brief overview of Heap? One of your differentiating features is the fact that you capture every interaction on web and mobile platforms for your customers. How do you prevent the user experience from suffering as a result of network congestion, while ensuring the reliable delivery of that data? Can you walk through the lifecycle of a single event from source to destination and the infrastructure components that it traverses to get there? Data collected in a user’s browser can often be messy due to various browser plugins, variations in runtime capabilities, etc. How do you ensure the integrity and accuracy of that information?

What are some of the difficulties that you have faced in establishing a representation of events that allows for uniform processing and storage?

What is your approach for merging and enriching event data with the information that you retrieve from your supported integrations?

What challenges does that pose in your processing architecture?

What are some of the problems that you have had to deal with to allow for processing and storing such large volumes of data?

How has that architecture changed or evolved over the life of the company? What are some changes that you are anticipating in the near future?

Can you describe your approach for synchronizing customer data with their individual Redshift instances and the difficulties that entails? What are some of the most interesting challenges that you have faced while building the technical and business aspects of Heap? What changes have been necessary as a result of GDPR? What are your plans for the future of Heap?

Contact Info

@danlovesproofs on twitter [email protected] @drob on github heapanalytics.com / @heap on twitter https://heapanalytics.com/blog/category/engineering?utm_source=rss&utm_medium=rss

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data manageme

Summary

Most businesses end up with data in a myriad of places with varying levels of structure. This makes it difficult to gain insights from across departments, projects, or people. Presto is a distributed SQL engine that allows you to tie all of your information together without having to first aggregate it all into a data warehouse. Kamil Bajda-Pawlikowski co-founded Starburst Data to provide support and tooling for Presto, as well as contributing advanced features back to the project. In this episode he describes how Presto is architected, how you can use it for your analytics, and the work that he is doing at Starburst Data.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kamil Bajda-Pawlikowski about Presto and his experiences with supporting it at Starburst Data

Interview

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

What are some of the common use cases and deployment patterns for Presto?

How does Presto compare to Drill or Impala? What is it about Presto that led you to building a business around it? What are some of the most challenging aspects of running and scaling Presto? For someone who is using the Presto SQL interface, what are some of the considerations that they should keep in mind to avoid writing poorly performing queries?

How does Presto represent data for translating between its SQL dialect and the API of the data stores that it interfaces with?

What are some cases in which Presto is not the right solution? What types of support have you found to be the most commonly requested? What are some of the types of tooling or improvements that you have made to Presto in your distribution?

What are some of the notable changes that your team has contributed upstream to Presto?

Contact Info

Website E-mail Twitter – @starburstdata Twitter – @prestodb

Parting Question

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

Links

Starburst Data Presto Hadapt Hadoop Hive Teradata PrestoCare Cost Based Optimizer ANSI SQL Spill To Disk Tempto Benchto Geospatial Functions Cassandra Accumulo Kafka Redis PostGreSQL

The intro and outro music is from The Hug by The Freak Fandango Orchestra / {CC BY-SA](http://creativecommons.org/licenses/by-sa/3.0/)?utm_source=rss&utm_medium=rss Support Data Engineering Podcast

Summary

The rate of change in the data engineering industry is alternately exciting and exhausting. Joe Crobak found his way into the work of data management by accident as so many of us do. After being engrossed with researching the details of distributed systems and big data management for his work he began sharing his findings with friends. This led to his creation of the Hadoop Weekly newsletter, which he recently rebranded as the Data Engineering Weekly newsletter. In this episode he discusses his experiences working as a data engineer in industry and at the USDS, his motivations and methods for creating a newsleteter, and the insights that he has gleaned from it.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Joe Crobak about his work maintaining the Data Engineering Weekly newsletter, and the challenges of keeping up with the data engineering industry.

Interview

Introduction How did you get involved in the area of data management? What are some of the projects that you have been involved in that were most personally fulfilling?

As an engineer at the USDS working on the healthcare.gov and medicare systems, what were some of the approaches that you used to manage sensitive data? Healthcare.gov has a storied history, how did the systems for processing and managing the data get architected to handle the amount of load that it was subjected to?

What was your motivation for starting a newsletter about the Hadoop space?

Can you speak to your reasoning for the recent rebranding of the newsletter?

How much of the content that you surface in your newsletter is found during your day-to-day work, versus explicitly searching for it? After over 5 years of following the trends in data analytics and data infrastructure what are some of the most interesting or surprising developments?

What have you found to be the fundamental skills or areas of experience that have maintained relevance as new technologies in data engineering have emerged?

What is your workflow for finding and curating the content that goes into your newsletter? What is your personal algorithm for filtering which articles, tools, or commentary gets added to the final newsletter? How has your experience managing the newsletter influenced your areas of focus in your work and vice-versa? What are your plans going forward?

Contact Info

Data Eng Weekly Email Twitter – @joecrobak Twitter – @dataengweekly

Parting Question

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

Links

USDS National Labs Cray Amazon EMR (Elastic Map-Reduce) Recommendation Engine Netflix Prize Hadoop Cloudera Puppet healthcare.gov Medicare Quality Payment Program HIPAA NIST National Institute of Standards and Technology PII (Personally Identifiable Information) Threat Modeling Apache JBoss Apache Web Server MarkLogic JMS (Java Message Service) Load Balancer COBOL Hadoop Weekly Data Engineering Weekly Foursquare NiFi Kubernetes Spark Flink Stream Processing DataStax RSS The Flavors of Data Science and Engineering CQRS Change Data Capture Jay Kreps

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

Summary

Managing an analytics project can be difficult due to the number of systems involved and the need to ensure that new information can be delivered quickly and reliably. That challenge can be met by adopting practices and principles from lean manufacturing and agile software development, and the cross-functional collaboration, feedback loops, and focus on automation in the DevOps movement. In this episode Christopher Bergh discusses ways that you can start adding reliability and speed to your workflow to deliver results with confidence and consistency.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Christopher Bergh about DataKitchen and the rise of DataOps

Interview

Introduction How did you get involved in the area of data management? How do you define DataOps?

How does it compare to the practices encouraged by the DevOps movement? How does it relate to or influence the role of a data engineer?

How does a DataOps oriented workflow differ from other existing approaches for building data platforms? One of the aspects of DataOps that you call out is the practice of providing multiple environments to provide a platform for testing the various aspects of the analytics workflow in a non-production context. What are some of the techniques that are available for managing data in appropriate volumes across those deployments? The practice of testing logic as code is fairly well understood and has a large set of existing tools. What have you found to be some of the most effective methods for testing data as it flows through a system? One of the practices of DevOps is to create feedback loops that can be used to ensure that business needs are being met. What are the metrics that you track in your platform to define the value that is being created and how the various steps in the workflow are proceeding toward that goal?

In order to keep feedback loops fast it is necessary for tests to run quickly. How do you balance the need for larger quantities of data to be used for verifying scalability/performance against optimizing for cost and speed in non-production environments?

How does the DataKitchen platform simplify the process of operationalizing a data analytics workflow? As the need for rapid iteration and deployment of systems to capture, store, process, and analyze data becomes more prevalent how do you foresee that feeding back into the ways that the landscape of data tools are designed and developed?

Contact Info

LinkedIn @ChrisBergh on Twitter Email

Parting Question

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

Links

DataOps Manifesto DataKitchen 2017: The Year Of DataOps Air Traffic Control Chief Data Officer (CDO) Gartner W. Edwards Deming DevOps Total Quality Management (TQM) Informatica Talend Agile Development Cattle Not Pets IDE (Integrated Devel

Summary

PostGreSQL has become one of the most popular and widely used databases, and for good reason. The level of extensibility that it supports has allowed it to be used in virtually every environment. At Citus Data they have built an extension to support running it in a distributed fashion across large volumes of data with parallelized queries for improved performance. In this episode Ozgun Erdogan, the CTO of Citus, and Craig Kerstiens, Citus Product Manager, discuss how the company got started, the work that they are doing to scale out PostGreSQL, and how you can start using it in your environment.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Ozgun Erdogan and Craig Kerstiens about Citus, worry free PostGreSQL

Interview

Introduction How did you get involved in the area of data management? Can you describe what Citus is and how the project got started? Why did you start with Postgres vs. building something from the ground up? What was the reasoning behind converting Citus from a fork of PostGres to being an extension and releasing an open source version? How well does Citus work with other Postgres extensions, such as PostGIS, PipelineDB, or Timescale? How does Citus compare to options such as PostGres-XL or the Postgres compatible Aurora service from Amazon? How does Citus operate under the covers to enable clustering and replication across multiple hosts? What are the failure modes of Citus and how does it handle loss of nodes in the cluster? For someone who is interested in migrating to Citus, what is involved in getting it deployed and moving the data out of an existing system? How do the different options for leveraging Citus compare to each other and how do you determine which features to release or withhold in the open source version? Are there any use cases that Citus enables which would be impractical to attempt in native Postgres? What have been some of the most challenging aspects of building the Citus extension? What are the situations where you would advise against using Citus? What are some of the most interesting or impressive uses of Citus that you have seen? What are some of the features that you have planned for future releases of Citus?

Contact Info

Citus Data

citusdata.com @citusdata on Twitter citusdata on GitHub

Craig

Email Website @craigkerstiens on Twitter

Ozgun

Email ozgune on GitHub

Parting Question

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

Links

Citus Data PostGreSQL NoSQL Timescale SQL blog post PostGIS PostGreSQL Graph Database JSONB Data Type PipelineDB Timescale PostGres-XL Aurora PostGres Amazon RDS Streaming Replication CitusMX CTE (Common Table Expression) HipMunk Citus Sharding Blog Post Wal-e Wal-g Heap Analytics HyperLogLog C-Store

The intro and outro musi

Summary

Buzzfeed needs to be able to understand how its users are interacting with the myriad articles, videos, etc. that they are posting. This lets them produce new content that will continue to be well-received. To surface the insights that they need to grow their business they need a robust data infrastructure to reliably capture all of those interactions. Walter Menendez is a data engineer on their infrastructure team and in this episode he describes how they manage data ingestion from a wide array of sources and create an interface for their data scientists to produce valuable conclusions.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Walter Menendez about the data engineering platform at Buzzfeed

Interview

Introduction How did you get involved in the area of data management? How is the data engineering team at Buzzfeed structured and what kinds of projects are you responsible for? What are some of the types of data inputs and outputs that you work with at Buzzfeed? Is the core of your system using a real-time streaming approach or is it primarily batch-oriented and what are the business needs that drive that decision? What does the architecture of your data platform look like and what are some of the most significant areas of technical debt? Which platforms and languages are most widely leveraged in your team and what are some of the outliers? What are some of the most significant challenges that you face, both technically and organizationally? What are some of the dead ends that you have run into or failed projects that you have tried? What has been the most successful project that you have completed and how do you measure that success?

Contact Info

@hackwalter on Twitter walterm on GitHub

Links

Data Literacy MIT Media Lab Tumblr Data Capital Data Infrastructure Google Analytics Datadog Python Numpy SciPy NLTK Go Language NSQ Tornado PySpark AWS EMR Redshift Tracking Pixel Google Cloud Don’t try to be google Stop Hiring DevOps Engineers and Start Growing Them

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