Data Engineering Podcast

• Exploring NATS: A Multi-Paradigm Connectivity Layer for Distributed Applications

  SummaryIn this episode of the Data Engineering Podcast Derek Collison, creator of NATS and CEO of Synadia, talks about the evolution and capabilities of NATS as a multi-paradigm connectivity layer for distributed applications. Derek discusses the challenges and solutions in building distributed systems, and highlights the unique features of NATS that differentiate it from other messaging systems. He delves into the architectural decisions behind NATS, including its ability to handle high-speed global microservices, support for edge computing, and integration with Jetstream for data persistence, and explores the role of NATS in modern data management and its use cases in industries like manufacturing and connected vehicles.AnnouncementsHello 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 Derek Collison about NATS, a multi-paradigm connectivity layer for distributed applications.InterviewIntroductionHow did you get involved in the area of data management?Can you describe what NATS is and the story behind it?How have your experiences in past roles (cloud foundry, TIBCO messaging systems) informed the core principles of NATS?What other sources of inspiration have you drawn on in the design and evolution of NATS? (e.g. Kafka, RabbitMQ, etc.)There are several patterns and abstractions that NATS can support, many of which overlap with other well-regarded technologies. When designing a system or service, what are the heuristics that should be used to determine whether NATS should act as a replacement or addition to those capabilities? (e.g. considerations of scale, speed, ecosystem compatibility, etc.)There is often a divide in the technologies and architecture used between operational/user-facing applications and data systems. How does the unification of multiple messaging patterns in NATS shift the ways that teams think about the relationship between these use cases?How does the shared communication layer of NATS with multiple protocol and pattern adaptaters reduce the need to replicate data and logic across application and data layers?Can you describe how the core NATS system is architected?How have the design and goals of NATS evolved since you first started working on it?In the time since you first began writing NATS (~2012) there have been several evolutionary stages in both application and data implementation patterns. How have those shifts influenced the direction of the NATS project and its ecosystem?For teams who have an existing architecture, what are some of the patterns for adoption of NATS that allow them to augment or migrate their capabilities?What are some of the ecosystem investments that you and your team have made to ease the adoption and integration of NATS?What are the most interesting, innovative, or unexpected ways that you have seen NATS used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on NATS?When is NATS the wrong choice?What do you have planned for the future of NATS?Contact InfoGitHubLinkedInParting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?Closing AnnouncementsThank 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.LinksNATSNATS JetStreamSynadiaCloud FoundryTIBCOApplied Physics Lab - Johns Hopkins UniversityCray SupercomputerRVCM Certified MessagingTIBCO ZMSIBM MQJMS == Java Message ServiceRabbitMQMongoDBNodeJSRedisAMQP == Advanced Message Queueing ProtocolPub/Sub PatternCircuit Breaker PatternZero MQAkamaiFastlyCDN == Content Delivery NetworkAt Most OnceAt Least OnceExactly OnceAWS KinesisMemcachedSQSSegmentRudderstackPodcast EpisodeDLQ == Dead Letter QueueMQTT == Message Queueing Telemetry TransportNATS Kafka Bridge10BaseT NetworkWeb AssemblyRedPandaPodcast EpisodePulsar FunctionsmTLSAuthZ (Authorization)AuthN (Authentication)NATS Auth CalloutsOPA == Open Policy AgentRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodeHome AssistantPodcast.__init__ EpisodeTailscaleOllamaCDC == Change Data CapturegRPCThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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• Advanced Lakehouse Management With The LakeKeeper Iceberg REST Catalog

  SummaryIn 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.AnnouncementsHello 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 catalogInterviewIntroductionHow 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 InfoLinkedInParting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?Closing AnnouncementsThank 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.LinksLakeKeeperSAPMicrosoft 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

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• Simplifying Data Pipelines with Durable Execution

  SummaryIn this episode of the Data Engineering Podcast Jeremy Edberg, CEO of DBOS, about durable execution and its impact on designing and implementing business logic for data systems. Jeremy explains how DBOS's serverless platform and orchestrator provide local resilience and reduce operational overhead, ensuring exactly-once execution in distributed systems through the use of the Transact library. He discusses the importance of version management in long-running workflows and how DBOS simplifies system design by reducing infrastructure needs like queues and CI pipelines, making it beneficial for data pipelines, AI workloads, and agentic AI.AnnouncementsHello 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 Jeremy Edberg about durable execution and how it influences the design and implementation of business logicInterviewIntroductionHow did you get involved in the area of data management?Can you describe what DBOS is and the story behind it?What is durable execution?What are some of the notable ways that inclusion of durable execution in an application architecture changes the ways that the rest of the application is implemented? (e.g. error handling, logic flow, etc.)Many data pipelines involve complex, multi-step workflows. How does DBOS simplify the creation and management of resilient data pipelines? How does durable execution impact the operational complexity of data management systems?One of the complexities in durable execution is managing code/data changes to workflows while existing executions are still processing. What are some of the useful patterns for addressing that challenge and how does DBOS help?Can you describe how DBOS is architected?How have the design and goals of the system changed since you first started working on it?What are the characteristics of Postgres that make it suitable for the persistence mechanism of DBOS?What are the guiding principles that you rely on to determine the boundaries between the open source and commercial elements of DBOS?What are the most interesting, innovative, or unexpected ways that you have seen DBOS used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on DBOS?When is DBOS the wrong choice?What do you have planned for the future of DBOS?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?Closing AnnouncementsThank 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.LinksDBOSExactly Once SemanticsTemporalSempahorePostgresDBOS TransactPython Typescript Idempotency KeysAgentic AIState MachineYugabyteDBPodcast EpisodeCockroachDBSupabaseNeonPodcast EpisodeAirflowThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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• Overcoming Redis Limitations: The Dragonfly DB Approach

  SummaryIn this episode of the Data Engineering Podcast Roman Gershman, CTO and founder of Dragonfly DB, explores the development and impact of high-speed in-memory databases. Roman shares his experience creating a more efficient alternative to Redis, focusing on performance gains, scalability, and cost efficiency, while addressing limitations such as high throughput and low latency scenarios. He explains how Dragonfly DB solves operational complexities for users and delves into its technical aspects, including maintaining compatibility with Redis while innovating on memory efficiency. Roman discusses the importance of cost efficiency and operational simplicity in driving adoption and shares insights on the broader ecosystem of in-memory data stores, future directions like SSD tiering and vector search capabilities, and the lessons learned from building a new database engine.AnnouncementsHello 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 Roman Gershman about building a high-speed in-memory database and the impact of the performance gains on data applicationsInterviewIntroductionHow did you get involved in the area of data management?Can you describe what DragonflyDB is and the story behind it?What is the core problem/use case that is solved by making a "faster Redis"?The other major player in the high performance key/value database space is Aerospike. What are the heuristics that an engineer should use to determine whether to use that vs. Dragonfly/Redis?Common use cases for Redis involve application caches and queueing (e.g. Celery/RQ). What are some of the other applications that you have seen Redis/Dragonfly used for, particularly in data engineering use cases?There is a piece of tribal wisdom that it takes 10 years for a database to iron out all of the kinks. At the same time, there have been substantial investments in commoditizing the underlying components of database engines. Can you describe how you approached the implementation of DragonflyDB to arive at a functional and reliable implementation?What are the architectural elements that contribute to the performance and scalability benefits of Dragonfly?How have the design and goals of the system changed since you first started working on it?For teams who migrate from Redis to Dragonfly, beyond the cost savings what are some of the ways that it changes the ways that they think about their overall system design?What are the most interesting, innovative, or unexpected ways that you have seen Dragonfly used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on DragonflyDB?When is DragonflyDB the wrong choice?What do you have planned for the future of DragonflyDB?Contact InfoGitHubLinkedInParting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?Closing AnnouncementsThank 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.LinksDragonflyDBRedisElasticacheValKeyAerospikeLaravelSidekiqCelerySeastar FrameworkShared-Nothing Architectureio_uringmidi-redisDunning-Kruger EffectRustThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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  43:58
• Bringing AI Into The Inner Loop of Data Engineering With Ascend

  SummaryIn this episode of the Data Engineering Podcast Sean Knapp, CEO of Ascend.io, explores the intersection of AI and data engineering. He discusses the evolution of data engineering and the role of AI in automating processes, alleviating burdens on data engineers, and enabling them to focus on complex tasks and innovation. The conversation covers the challenges and opportunities presented by AI, including the need for intelligent tooling and its potential to streamline data engineering processes. Sean and Tobias also delve into the impact of generative AI on data engineering, highlighting its ability to accelerate development, improve governance, and enhance productivity, while also noting the current limitations and future potential of AI in the field.AnnouncementsHello 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 Sean Knapp about how Ascend is incorporating AI into their platform to help you keep up with the rapid rate of changeInterviewIntroductionHow did you get involved in the area of data management?Can you describe what Ascend is and the story behind it?The last time we spoke was August of 2022. What are the most notable or interesting evolutions in your platform since then?In that same time "AI" has taken up all of the oxygen in the data ecosystem. How has that impacted the ways that you and your customers think about their priorities?The introduction of AI as an API has caused many organizations to try and leap-frog their data maturity journey and jump straight to building with advanced capabilities. How is that impacting the pressures and priorities felt by data teams?At the same time that AI-focused product goals are straining data teams capacities, AI also has the potential to act as an accelerator to their work. What are the roadblocks/speedbumps that are in the way of that capability?Many data teams are incorporating AI tools into parts of their workflow, but it can be clunky and cumbersome. How are you thinking about the fundamental changes in how your platform works with AI at its center?Can you describe the technical architecture that you have evolved toward that allows for AI to drive the experience rather than being a bolt-on?What are the concrete impacts that these new capabilities have on teams who are using Ascend?What are the most interesting, innovative, or unexpected ways that you have seen Ascend + AI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on incorporating AI into the core of Ascend?When is Ascend the wrong choice?What do you have planned for the future of AI in Ascend?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?Closing AnnouncementsThank 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.LinksAscendCursor AI Code EditorDevinGitHub CopilotOpenAI DeepResearchS3 TablesAWS GlueAWS BedrockSnowparkCo-Intelligence: Living and Working with AI by Ethan Mollick (affiliate link)OpenAI o3The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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