Segment

Data Lineage For Your Pipelines - Episode 82

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 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 Joe Doliner about Pachyderm, a platform that lets you deploy and manage multi-stage, language-agnostic data pipelines while maintaining complete reproducibility and provenance

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what Pachyderm is and how it got started?
    • What is new in the last two years since I talked to Dan Whitenack in episode 1?
    • How have the changes and additional features in Kubernetes impacted your work on Pachyderm?
  • A recent development in the Kubernetes space is the Kubeflow project. How do its capabilities compare with or complement what you are doing in Pachyderm?
  • Can you walk through the overall workflow for someone building an analysis pipeline in Pachyderm?
    • How does that break down across different roles and responsibilities (e.g. data scientist vs data engineer)?
  • There are a lot of concepts and moving parts in Pachyderm, from getting a Kubernetes cluster set up, to understanding the file system and processing pipeline, to understanding best practices. What are some of the common challenges or points of confusion that new users encounter?
  • Data provenance is critical for understanding the end results of an analysis or ML model. Can you explain how the tracking in Pachyderm is implemented?
    • What is the interface for exposing and exploring that provenance data?
  • What are some of the advanced capabilities of Pachyderm that you would like to call out?
  • With your recent round of fundraising I’m assuming there is new pressure to grow and scale your product and business. How are you approaching that and what are some of the challenges you are facing?
  • What have been some of the most challenging/useful/unexpected lessons that you have learned in the process of building, maintaining, and growing the Pachyderm project and company?
  • What do you have planned for the future of Pachyderm?

Contact Info

Parting Question

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

Links

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

Build Your Data Analytics Like An Engineer - Episode 81

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

Parting Question

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

Links

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

Using FoundationDB As The Bedrock For Your Distributed Systems - Episode 80

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 the power of ACID transactions in a NoSQL database

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you explain what FoundationDB is and how you got involved with the project?
  • What are some of the unique use cases that FoundationDB enables?
  • Can you describe how FoundationDB is architected?
    • How is the ACID compliance implemented at the cluster level?
  • What are some of the mechanisms built into FoundationDB that contribute to its fault tolerance?
    • How are conflicts managed?
  • FoundationDB has an interesting feature in the form of Layers that provide different semantics on the underlying storage. Can you describe how that is implemented and some of the interesting layers that are available?
    • Is it possible to apply different layers, such as relational and document, to the same underlying objects in storage?
  • One of the aspects of FoundationDB that is called out in the documentation and which I have heard about elsewhere is the performance that it provides. Can you describe some of the implementation mechanics of FoundationDB that allow it to provide such high throughput?
  • For someone who wants to run FoundationDB can you describe a typical deployment topology?
    • What are the scaling factors for the underlying storage and for the Layers that are operating on the cluster?
  • Once you have a cluster deployed, what are some of the edge cases that users should watch out for?
    • How are version upgrades managed in a cluster?
  • What are some of the ways that FoundationDB impacts the way that an application developer or data engineer would architect their software as compared to working with something like Postgres or MongoDB?
  • What are some of the more interesting/unusual/unexpected ways that you have seen FoundationDB used?
  • When is FoundationDB the wrong choice?
  • What is in store for the future of FoundationDB?

Contact Info

Parting Question

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

Links

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

Running Your Database On Kubernetes With KubeDB - Episode 79

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 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 Tamal Saha about KubeDB, a project focused on making running production-grade databases easy on Kubernetes

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what KubeDB is and how the project got started?
  • What are the main challenges associated with running a stateful system on top of Kubernetes?
    • Why would someone want to run their database on a container platform rather than on a dedicated instance or with a hosted service?
  • Can you describe how KubeDB is implemented and how that has evolved since you first started working on it?
  • Can you talk through how KubeDB simplifies the process of deploying and maintaining databases?
  • What is involved in adding support for a new database?
    • How do the requirements change for systems that are natively clustered?
  • How does KubeDB help with maintenance processes around upgrading existing databases to newer versions?
  • How does the work that you are doing on KubeDB compare to what is available in StorageOS?
    • Are there any other projects that are targeting similar goals?
  • What have you found to be the most interesting/challenging/unexpected aspects of building KubeDB?
  • What do you have planned for the future of the project?

Contact Info

Parting Question

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

Links

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

Unpacking Fauna: A Global Scale Cloud Native Database - Episode 78

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

Parting Question

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

Links

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

Index Your Big Data With Pilosa For Faster Analytics - Episode 77

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 management?
  • Can you start by describing what Pilosa is and how the project got started?
  • Where does Pilosa fit into the overall data ecosystem and how does it integrate into an existing stack?
  • What types of use cases is Pilosa uniquely well suited for?
  • The Pilosa data model is fairly unique. Can you talk through how it is represented and implemented?
  • What are some approaches to modeling data that might be coming from a relational database or some structured flat files?
    • How do you handle highly dimensional data?
  • What are some of the decisions that need to be made early in the modeling process which could have ramifications later on in the lifecycle of the project?
  • What are the scaling factors of Pilosa?
  • What are some of the most interesting/challenging/unexpected lessons that you have learned in the process of building Pilosa?
  • What is in store for the future of Pilosa?

Contact Info

Parting Question

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

Links

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