Data Engineering Podcast

• The Data Model That Captures Your Business: Metric Trees Explained

  SummaryIn this episode of the Data Engineering Podcast Vijay Subramanian, founder and CEO of Trace, talks about metric trees - a new approach to data modeling that directly captures a company's business model. Vijay shares insights from his decade-long experience building data practices at Rent the Runway and explains how the modern data stack has led to a proliferation of dashboards without a coherent way for business consumers to reason about cause, effect, and action. He explores how metric trees differ from and interoperate with other data modeling approaches, serve as a backend for analytical workflows, and provide concrete examples like modeling Uber's revenue drivers and customer journeys. Vijay also discusses the potential of AI agents operating on metric trees to execute workflows, organizational patterns for defining inputs and outputs with business teams, and a vision for analytics that becomes invisible infrastructure embedded in everyday decisions.AnnouncementsHello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data 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 Vijay Subramanian about metric trees and how they empower more effective and adaptive analyticsInterviewIntroductionHow did you get involved in the area of data management?Can you describe what metric trees are and their purpose?How do metric trees relate to metric/semantic layers?What are the shortcomings of existing data modeling frameworks that prevent effective use of those assets?How do metric trees build on top of existing investments in dimensional data models?What are some strategies for engaging with the business to identify metrics and their relationships?What are your recommendations for storage, representation, and retrieval of metric trees?How do metric trees fit into the overall lifecycle of organizational data workflows?When creating any new data asset it introduces overhead of maintenance, monitoring, and evolution. How do metric trees fit into existing testing and validation frameworks that teams rely on for dimensional modeling?What are some of the key differences in useful evaluation/testing that teams need to develop for metric trees?How do metric trees assist in context engineering for AI-powered self-serve access to organizational data?What are the most interesting, innovative, or unexpected ways that you have seen metric trees used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on metric trees and operationalizing them at Trace?When is a metric tree the wrong abstraction?What do you have planned for the future of Trace and applications of metric trees?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.LinksMetric TreeTraceModern Data StackHadoopVerticaLuigidbtRalph KimballBill InmonMetric LayerDimensional Data WarehouseMaster Data ManagementData GovernanceFinancial P&L (Profit and Loss)EBITDA ==Earnings before interest, taxes, depreciation and amortizationThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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• From GPUs-as-a-Service to Workloads-as-a-Service: Flex AI’s Path to High-Utilization AI Infra

  SummaryIn this crossover episode of the AI Engineering Podcast, host Tobias Macey interviews Brijesh Tripathi, CEO of Flex AI, about revolutionizing AI engineering by removing DevOps burdens through "workload as a service". Brijesh shares his expertise from leading AI/HPC architecture at Intel and deploying supercomputers like Aurora, highlighting how access friction and idle infrastructure slow progress. Join them as they discuss Flex AI's innovative approach to simplifying heterogeneous compute, standardizing on consistent Kubernetes layers, and abstracting inference across various accelerators, allowing teams to iterate faster without wrestling with drivers, libraries, or cloud-by-cloud differences. Brijesh also shares insights into Flex AI's strategies for lifting utilization, protecting real-time workloads, and spanning the full lifecycle from fine-tuning to autoscaled inference, all while keeping complexity at bay.Pre-ambleI hope you enjoy this cross-over episode of the AI Engineering Podcast, another show that I run to act as your guide to the fast-moving world of building scalable and maintainable AI systems. As generative AI models have grown more powerful and are being applied to a broader range of use cases, the lines between data and AI engineering are becoming increasingly blurry. The responsibilities of data teams are being extended into the realm of context engineering, as well as designing and supporting new infrastructure elements that serve the needs of agentic applications. This episode is an example of the types of work that are not easily categorized into one or the other camp.AnnouncementsHello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data 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 Brijesh Tripathi about FlexAI, a platform offering a service-oriented abstraction for AI workloadsInterviewIntroductionHow did you get involved in machine learning?Can you describe what FlexAI is and the story behind it?What are some examples of the ways that infrastructure challenges contribute to friction in developing and operating AI applications?How do those challenges contribute to issues when scaling new applications/businesses that are founded on AI?There are numerous managed services and deployable operational elements for operationalizing AI systems. What are some of the main pitfalls that teams need to be aware of when determining how much of that infrastructure to own themselves?Orchestration is a key element of managing the data and model lifecycles of these applications. How does your approach of "workload as a service" help to mitigate some of the complexities in the overall maintenance of that workload?Can you describe the design and architecture of the FlexAI platform?How has the implementation evolved from when you first started working on it?For someone who is going to build on top of FlexAI, what are the primary interfaces and concepts that they need to be aware of?Can you describe the workflow of going from problem to deployment for an AI workload using FlexAI?One of the perennial challenges of making a well-integrated platform is that there are inevitably pre-existing workloads that don't map cleanly onto the assumptions of the vendor. What are the affordances and escape hatches that you have built in to allow partial/incremental adoption of your service?What are the elements of AI workloads and applications that you are explicitly not trying to solve for?What are the most interesting, innovative, or unexpected ways that you have seen FlexAI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on FlexAI?When is FlexAI the wrong choice?What do you have planned for the future of FlexAI?Contact InfoLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?LinksFlex AIAurora Super ComputerCoreWeaveKubernetesCUDAROCmTensor Processing Unit (TPU)PyTorchTritonTrainiumASIC == Application Specific Integrated CircuitSOC == System On a ChipLoveableFlexAI BlueprintsTenstorrentThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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• From RAG to Relational: How Agentic Patterns Are Reshaping Data Architecture

  SummaryIn this episode of the AI Engineering Podcast Mark Brooker, VP and Distinguished Engineer at AWS, talks about how agentic workflows are transforming database usage and infrastructure design. He discusses the evolving role of data in AI systems, from traditional models to more modern approaches like vectors, RAG, and relational databases. Mark explains why agents require serverless, elastic, and operationally simple databases, and how AWS solutions like Aurora and DSQL address these needs with features such as rapid provisioning, automated patching, geodistribution, and spiky usage. The conversation covers topics including tool calling, improved model capabilities, state in agents versus stateless LLM calls, and the role of Lambda and AgentCore for long-running, session-isolated agents. Mark also touches on the shift from local MCP tools to secure, remote endpoints, the rise of object storage as a durable backplane, and the need for better identity and authorization models. The episode highlights real-world patterns like agent-driven SQL fuzzing and plan analysis, while identifying gaps in simplifying data access, hardening ops for autonomous systems, and evolving serverless database ergonomics to keep pace with agentic development.AnnouncementsHello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data 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 Marc Brooker about the impact of agentic workflows on database usage patterns and how they change the architectural requirements for databasesInterviewIntroductionHow did you get involved in the area of data management?Can you describe what the role of the database is in agentic workflows?There are numerous types of databases, with relational being the most prevalent. How does the type and purpose of an agent inform the type of database that should be used?Anecdotally I have heard about how agentic workloads have become the predominant "customers" of services like Neon and Fly.io. How would you characterize the different patterns of scale for agentic AI applications? (e.g. proliferation of agents, monolithic agents, multi-agent, etc.)What are some of the most significant impacts on workload and access patterns for data storage and retrieval that agents introduce?What are the categorical differences in that behavior as compared to programmatic/automated systems?You have spent a substantial amount of time on Lambda at AWS. Given that LLMs are effectively stateless, how does the added ephemerality of serverless functions impact design and performance considerations around having to "re-hydrate" context when interacting with agents?What are the most interesting, innovative, or unexpected ways that you have seen serverless and database systems used for agentic workloads?What are the most interesting, unexpected, or challenging lessons that you have learned while working on technologies that are supporting agentic applications?Contact InfoBlogLinkedInParting 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.LinksAWS Aurora DSQLAWS LambdaThree Tier ArchitectureVector DatabaseGraph DatabaseRelational DatabaseVector EmbeddingRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodeGraphRAGAI Engineering Podcast EpisodeLLM Tool CallingMCP == Model Context ProtocolA2A == Agent 2 Agent ProtocolAWS Bedrock AgentCoreStrandsLangChainKiroThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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• Duck Lake: Simplifying the Lakehouse Ecosystem

  SummaryIn this episode of the Data Engineering Podcast Hannes Mühleisen and Mark Raasveldt, the creators of DuckDB, share their work on Duck Lake, a new entrant in the open lakehouse ecosystem. They discuss how Duck Lake, is focused on simplicity, flexibility, and offers a unified catalog and table format compared to other lakehouse formats like Iceberg and Delta. Hannes and Mark share insights into how Duck Lake revolutionizes data architecture by enabling local-first data processing, simplifying deployment of lakehouse solutions, and offering benefits such as encryption features, data inlining, and integration with existing ecosystems.AnnouncementsHello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data 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 Hannes Mühleisen and Mark Raasveldt about DuckLake, the latest entrant into the open lakehouse ecosystemInterviewIntroductionHow did you get involved in the area of data management?Can you describe what DuckLake is and the story behind it?What are the particular problems that DuckLake is solving for?How does this compare to the capabilities of MotherDuck?Iceberg and Delta already have a well established ecosystem, but so does DuckDB. Who are the primary personas that you are trying to focus on in these early days of DuckLake?One of the major factors driving the adoption of formats like Iceberg is cost efficiency for large volumes of data. That brings with it challenges of large batch processing of data. How does DuckLake account for these axes of scale?There is also a substantial investment in the ecosystem of technologies that support Iceberg. The most notable ecosystem challenge for DuckDB and DuckLake is in the query layer. How are you thinking about the evolution and growth of that capability beyond DuckDB (e.g. support in Trino/Spark/Flink)?What are your opinions on the viability of a future where DuckLake and Iceberg become a unified standard and implementation? (why can't Iceberg REST catalog implementations just use DuckLake under the hood?)Digging into the specifics of the specification and implementation, what are some of the capabilities that it offers above and beyond Iceberg?Is it now possible to enforce PK/FK constraints, indexing on underlying data?Given that DuckDB has a vector type, how do you think about the support for vector storage/indexing?How do the capabilities of DuckLake and the integration with DuckDB change the ways that data teams design their data architecture and access patterns?What are your thoughts on the impact of "data gravity" in today's data ecosystem, with engines like DuckDB, KuzuDB, LanceDB, etc. available for embedded and edge use cases?What are the most interesting, innovative, or unexpected ways that you have seen DuckLake used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on DuckLake?When is DuckLake the wrong choice?What do you have planned for the future of DuckLake?Contact InfoHannesWebsiteMarkWebsiteParting 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.LinksDuckDBPodcast EpisodeDuckLakeDuckDB LabsMySQLCWIMonetDBIcebergIceberg REST CatalogDeltaHudiLanceDuckDB Iceberg ConnectorACID == Atomicity, Consistency, Isolation, DurabilityMotherDuckMotherDuck Managed DuckLakeTrinoSparkPrestoSpark DuckLake DemoDelta KernelArrowdltS3 TablesAttribute Based Access Control (ABAC)ParquetArrow FlightHadoopHDFSDuckLake RoadmapThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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• Aligning Business and Data: The Essential Role of Data Modeling

  SummaryIn this episode of the Data Engineering Podcast Serge Gershkovich, head of product at SQL DBM, talks about the socio-technical aspects of data modeling. Serge shares his background in data modeling and highlights its importance as a collaborative process between business stakeholders and data teams. He debunks common misconceptions that data modeling is optional or secondary, emphasizing its crucial role in ensuring alignment between business requirements and data structures. The conversation covers challenges in complex environments, the impact of technical decisions on data strategy, and the evolving role of AI in data management. Serge stresses the need for business stakeholders' involvement in data initiatives and a systematic approach to data modeling, warning against relying solely on technical expertise without considering business alignment.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.Enterprises today face an enormous challenge: they’re investing billions into Snowflake and Databricks, but without strong foundations, those investments risk becoming fragmented, expensive, and hard to govern. And that’s especially evident in large, complex enterprise data environments. That’s why companies like DirecTV and Pfizer rely on SqlDBM. Data modeling may be one of the most traditional practices in IT, but it remains the backbone of enterprise data strategy. In today’s cloud era, that backbone needs a modern approach built natively for the cloud, with direct connections to the very platforms driving your business forward. Without strong modeling, data management becomes chaotic, analytics lose trust, and AI initiatives fail to scale. SqlDBM ensures enterprises don’t just move to the cloud—they maximize their ROI by creating governed, scalable, and business-aligned data environments. If global enterprises are using SqlDBM to tackle the biggest challenges in data management, analytics, and AI, isn’t it worth exploring what it can do for yours? Visit dataengineeringpodcast.com/sqldbm to learn more.Your host is Tobias Macey and today I'm interviewing Serge Gershkovich about how and why data modeling is a sociotechnical endeavorInterviewIntroductionHow did you get involved in the area of data management?Can you start by describing the activities that you think of when someone says the term "data modeling"?What are the main groupings of incomplete or inaccurate definitions that you typically encounter in conversation on the topic?How do those conceptions of the problem lead to challenges and bottlenecks in execution?Data modeling is often associated with data warehouse design, but it also extends to source systems and unstructured/semi-structured assets. How does the inclusion of other data localities help in the overall success of a data/domain modeling effort?Another aspect of data modeling that often consumes a substantial amount of debate is which pattern to adhere to (star/snowflake, data vault, one big table, anchor modeling, etc.). What are some of the ways that you have found effective to remove that as a stumbling block when first developing an organizational domain representation?While the overall purpose of data modeling is to provide a digital representation of the business processes, there are inevitable technical decisions to be made. What are the most significant ways that the underlying technical systems can help or hinder the goals of building a digital twin of the business?What impact (positive and negative) are you seeing from the introduction of LLMs into the workflow of data modeling?How does tool use (e.g. MCP connection to warehouse/lakehouse) help when developing the transformation logic for achieving a given domain representation? What are the most interesting, innovative, or unexpected ways that you have seen organizations address the data modeling lifecycle?What are the most interesting, unexpected, or challenging lessons that you have learned while working with organizations implementing a data modeling effort?What are the overall trends in the ecosystem that you are monitoring related to data modeling practices?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?LinkssqlDBMSAPJoe ReisERD == Entity Relation DiagramMaster Data ManagementdbtData ContractsData Modeling With Snowflake book by Serge (affiliate link)Type 2 DimensionData VaultStar SchemaAnchor ModelingRalph KimballBill InmonSixth Normal FormMCP == Model Context ProtocolThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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