Summary In this episode of the Data Engineering Podcast Viktor Kessler, co-founder of Vakmo, talks about the architectural patterns in the lake house enabled by a fast and feature-rich Iceberg catalog. Viktor shares his journey from data warehouses to developing the open-source project, Lakekeeper, an Apache Iceberg REST catalog written in Rust that facilitates building lake houses with essential components like storage, compute, and catalog management. He discusses the importance of metadata in making data actionable, the evolution of data catalogs, and the challenges and innovations in the space, including integration with OpenFGA for fine-grained access control and managing data across formats and compute engines.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Viktor Kessler about architectural patterns in the lakehouse that are unlocked by a fast and feature-rich Iceberg catalogInterview IntroductionHow did you get involved in the area of data management?Can you describe what LakeKeeper is and the story behind it? What is the core of the problem that you are addressing?There has been a lot of activity in the catalog space recently. What are the driving forces that have highlighted the need for a better metadata catalog in the data lake/distributed data ecosystem?How would you characterize the feature sets/problem spaces that different entrants are focused on addressing?Iceberg as a table format has gained a lot of attention and adoption across the data ecosystem. The REST catalog format has opened the door for numerous implementations. What are the opportunities for innovation and improving user experience in that space?What is the role of the catalog in managing security and governance? (AuthZ, auditing, etc.)What are the channels for propagating identity and permissions to compute engines? (how do you avoid head-scratching about permission denied situations)Can you describe how LakeKeeper is implemented?How have the design and goals of the project changed since you first started working on it?For someone who has an existing set of Iceberg tables and catalog, what does the migration process look like?What new workflows or capabilities does LakeKeeper enable for data teams using Iceberg tables across one or more compute frameworks?What are the most interesting, innovative, or unexpected ways that you have seen LakeKeeper used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on LakeKeeper?When is LakeKeeper the wrong choice?What do you have planned for the future of LakeKeeper?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links LakeKeeperSAPMicrosoft AccessMicrosoft ExcelApache IcebergPodcast EpisodeIceberg REST CatalogPyIcebergSparkTrinoDremioHive MetastoreHadoopNATSPolarsDuckDBPodcast EpisodeDataFusionAtlanPodcast EpisodeOpen MetadataPodcast EpisodeApache AtlasOpenFGAHudiPodcast EpisodeDelta LakePodcast EpisodeLance Table FormatPodcast EpisodeUnity CatalogPolaris CatalogApache GravitinoPodcast Episode KeycloakOpen Policy Agent (OPA)Apache RangerApache NiFiThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary Transactional databases used in applications are optimized for fast reads and writes with relatively simple queries on a small number of records. Data warehouses are optimized for batched writes and complex analytical queries. Between those use cases there are varying levels of support for fast reads on quickly changing data. To address that need more completely the team at Materialize has created an engine that allows for building queryable views of your data as it is continually updated from the stream of changes being generated by your applications. In this episode Frank McSherry, chief scientist of Materialize, explains why it was created, what use cases it enables, and how it works to provide fast queries on continually updated data.

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! 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, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Frank McSherry about Materialize, an engine for maintaining materialized views on incrementally updated data from change data captures

Interview

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

What was your motivation for creating it?

What use cases does Materialize enable?

What are some of the existing tools or systems that you have seen employed to address those needs which can be replaced by Materialize? How does it fit into the broader ecosystem of data tools and platforms?

What are some of the use cases that Materialize is uniquely able to support? How is Materialize architected and how has the design evolved since you first began working on it? Materialize is based on your timely-dataflow project, which itself is based on the work you did on Naiad. What was your reasoning for using Rust as the implementation target and what benefits has it provided?

What are some of the components or primitives that were missing in the Rust ecosystem as compared to what is available in Java or C/C++, which have been the dominant languages for distributed data systems?

In the list of features, you highlight full support for ANSI SQL 92. What were some of the edge cases that you faced in complying with that standard given the distributed execution context for Materialize?

A majority of SQL oriented platforms define custom extensions or built-in functions that are specific to their problem domain. What are some of the existing or

Summary The first stage in every data project is collecting information and routing it to a storage system for later analysis. For operational data this typically means collecting log messages and system metrics. Often a different tool is used for each class of data, increasing the overall complexity and number of moving parts. The engineers at Timber.io decided to build a new tool in the form of Vector that allows for processing both of these data types in a single framework that is reliable and performant. In this episode Ben Johnson and Luke Steensen explain how the project got started, how it compares to other tools in this space, and how you can get involved in making it even better.

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! 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, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Ben Johnson and Luke Steensen about Vector, a high-performance, open-source observability data router

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what the Vector project is and your reason for creating it?

What are some of the comparable tools that are available and what were they lacking that prompted you to start a new project?

What strategy are you using for project governance and sustainability? What are the main use cases that Vector enables? Can you explain how Vector is implemented and how the system design has evolved since you began working on it?

How did your experience building the business and products for Timber influence and inform your work on Vector? When you were planning the implementation, what were your criteria for the runtime implementation and why did you decide to use Rust? What led you to choose Lua as the embedded scripting environment?

What data format does Vector use internally?

Is there any support for defining and enforcing schemas?

In the event of a malformed message is there any capacity for a dead letter queue?

What are some strategies for formatting source data to improve the effectiveness of the information that is gathered and the ability of Vector to parse it into useful data? When designing an event flow in Vector what are the available mechanisms for testing the overall delivery and any transformations? What options are available to operators to support visibility into the running system? In terms of deployment topologies, what ca

Summary

With the increased ease of gaining access to servers in data centers across the world has come the need for supporting globally distributed data storage. With the first wave of cloud era databases the ability to replicate information geographically came at the expense of transactions and familiar query languages. To address these shortcomings the engineers at Cockroach Labs have built a globally distributed SQL database with full ACID semantics in Cockroach DB. In this episode Peter Mattis, the co-founder and VP of Engineering at Cockroach Labs, describes the architecture that underlies the database, the challenges they have faced along the way, and the ways that you can use it in your own environments today.

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 Peter Mattis about CockroachDB, the SQL database for global cloud services

Interview

Introduction How did you get involved in the area of data management? What was the motivation for creating CockroachDB and building a business around it? Can you describe the architecture of CockroachDB and how it supports distributed ACID transactions?

What are some of the tradeoffs that are necessary to allow for georeplicated data with distributed transactions? What are some of the problems that you have had to work around in the RAFT protocol to provide reliable operation of the clustering mechanism?

Go is an unconventional language for building a database. What are the pros and cons of that choice? What are some of the common points of confusion that users of CockroachDB have when operating or interacting with it?

What are the edge cases and failure modes that users should be aware of?

I know that your SQL syntax is PostGreSQL compatible, so is it possible to use existing ORMs unmodified with CockroachDB?

What are some examples of extensions that are specific to CockroachDB?

What are some of the most interesting uses of CockroachDB that you have seen? When is CockroachDB the wrong choice? What do you have planned for the future of CockroachDB?

Contact Info

Peter

LinkedIn petermattis on GitHub @petermattis on Twitter

Cockroach Labs

@CockroackDB on Twitter Website cockroachdb on GitHub

Parting Question

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

Links

CockroachDB Cockroach Labs SQL Google Bigtable Spanner NoSQL RDBMS (Relational Database Management System) “Big Iron” (colloquial term for mainframe computers) RAFT Consensus Algorithm Consensus MVCC (Multiversion Concurrency Control) Isolation Etcd GDPR Golang C++ Garbage Collection Metaprogramming Rust Static Linking Docker Kubernetes CAP Theorem PostGreSQL ORM (Object Relational Mapping) Information Schema PG Catalog Interleaved Tables Vertica Spark Change Data Capture

The intro and outro music is from The Hug by The Freak Fandan

Summary Sharing data across multiple computers, particularly when it is large and changing, is a difficult problem to solve. In order to provide a simpler way to distribute and version data sets among collaborators the Dat Project was created. In this episode Danielle Robinson and Joe Hand explain how the project got started, how it functions, and some of the many ways that it can be used. They also explain the plans that the team has for upcoming features and uses that you can watch out for in future releases.

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 A few announcements:

There is still time to register for the O’Reilly Strata Conference in San Jose, CA March 5th-8th. Use the link dataengineeringpodcast.com/strata-san-jose to register and save 20% The O’Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20% If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register.

Your host is Tobias Macey and today I’m interviewing Danielle Robinson and Joe Hand about Dat Project, a distributed data sharing protocol for building applications of the future

Interview

Introduction How did you get involved in the area of data management? What is the Dat project and how did it get started? How have the grants to the Dat project influenced the focus and pace of development that was possible?

Now that you have established a non-profit organization around Dat, what are your plans to support future sustainability and growth of the project?

Can you explain how the Dat protocol is designed and how it has evolved since it was first started? How does Dat manage conflict resolution and data versioning when replicating between multiple machines? One of the primary use cases that is mentioned in the documentation and website for Dat is that of hosting and distributing open data sets, with a focus on researchers. How does Dat help with that effort and what improvements does it offer over other existing solutions? One of the difficult aspects of building a peer-to-peer protocol is that of establishing a critical mass of users to add value to the network. How have you approached that effort and how much progress do you feel that you have made? How does the peer-to-peer nature of the platform affect the architectural patterns for people wanting to build applications that are delivered via dat, vs the common three-tier architecture oriented around persistent databases? What mechanisms are available for content discovery, given the fact that Dat URLs are private and unguessable by default? For someone who wants to start using Dat today, what is involved in creating and/or consuming content that is available on the network? What have been the most challenging aspects of building and promoting Dat? What are some of the most interesting or inspiring uses of the Dat protocol that you are aware of?

Contact Info

Dat

datproject.org Email @dat_project on Twitter Dat Chat

Danielle

Email @daniellecrobins

Joe

Email @joeahand on Twitter

Parting Question

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

Links

Dat Project Code For Science and Society Neuroscience Cell Biology OpenCon Mozilla Science Open Education Open Access Open Data Fortune 500 Data Warehouse Knight Foundation Alfred P. Sloan Foundation Gordon and Betty Moore Foundation Dat In The Lab Dat in the Lab blog posts California Digital Library IPFS Dat on Open Collective – COMING SOON! ScienceFair Stencila eLIFE Git BitTorrent Dat Whitepaper Merkle Tree Certificate Transparency Dat Protocol Working Group Dat Multiwriter Development – Hyperdb Beaker Browser WebRTC IndexedDB Rust C Keybase PGP Wire Zenodo Dryad Data Sharing Dataverse RSync FTP Globus Fritter Fritter Demo Rotonde how to Joe’s website on Dat Dat Tutorial Data Rescue – NYTimes Coverage Data.gov Libraries+ Network UC Conservation Genomics Consortium Fair Data principles hypervision hypervision in browser

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

Click here to read the unedited transcript… Tobias Macey 00:13…