Summary In this crossover episode, Max Beauchemin explores how multiplayer, multi‑agent engineering is transforming the way individuals and teams build data and AI systems. He digs into the shifting boundary between data and AI engineering, the rise of “context as code,” and how just‑in‑time retrieval via MCP and CLIs lets agents gather what they need without bloating context windows. Max shares hard‑won practices from going “AI‑first” for most tasks, where humans focus on orchestration and taste, and the new bottlenecks that appear — code review, QA, async coordination — when execution accelerates 2–10x. He also dives deep into Agor, his open‑source agent orchestration platform: a spatial, multiplayer workspace that manages Git worktrees and live dev environments, templatizes prompts by workflow zones, supports session forking and sub‑sessions, and exposes an internal MCP so agents can schedule, monitor, and even coordinate other agents.

Announcements Hello 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.Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Maxime Beauchemin about the impact of multi-player multi-agent engineering on individual and team velocity for building better data systemsInterview IntroductionHow did you get involved in the area of data management?Can you start by giving an overview of the types of work that you are relying on AI development agents for?As you bring agents into the mix for software engineering, what are the bottlenecks that start to show up?In my own experience there are a finite number of agents that I can manage in parallel. How does Agor help to increase that limit?How does making multi-agent management a multi-player experience change the dynamics of how you apply agentic engineering workflows?Contact Info LinkedInLinks AgorApache AirflowApache SupersetPresetClaude CodeCodexPlaywright MCPTmuxGit WorktreesOpencode.aiGitHub CodespacesOnaThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary  In this episode Preeti Somal, EVP of Engineering at Temporal, talks about the durable execution model and how it reshapes the way teams build reliable, stateful systems for data and AI. She explores Temporal’s code‑first programming model—workflows, activities, task queues, and replay—and how it eliminates hand‑rolled retry, checkpoint, and error‑handling scaffolding while letting data remain where it lives. Preeti shares real-world patterns for replacing DAG-first orchestration, integrating application and data teams through signals and Nexus for cross-boundary calls, and using Temporal to coordinate long-running, human-in-the-loop, and agentic AI workflows with full observability and auditability. Shee also discusses heuristics for choosing Temporal alongside (or instead of) traditional orchestrators, managing scale without moving large datasets, and lessons from running durable execution as a cloud service. 

Announcements  Hello 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. Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Preeti Somal about how to incorporate durable execution and state management into AI application architectures Interview   IntroductionHow did you get involved in the area of data management?Can you describe what durable execution is and how it impacts system architecture?With the strong focus on state maintenance and high reliability, what are some of the most impactful ways that data teams are incorporating tools like Temporal into their work?One of the core primitives in Temporal is a "workflow". How does that compare to similar primitives in common data orchestration systems such as Airflow, Dagster, Prefect, etc.?  What are the heuristics that you recommend when deciding which tool to use for a given task, particularly in data/pipeline oriented projects? Even if a team is using a more data-focused orchestration engine, what are some of the ways that Temporal can be applied to handle the processing logic of the actual data?AI applications are also very dependent on reliable data to be effective in production contexts. What are some of the design patterns where durable execution can be integrated into RAG/agent applications?What are some of the conceptual hurdles that teams experience when they are starting to adopt Temporal or other durable execution frameworks?What are the most interesting, innovative, or unexpected ways that you have seen Temporal/durable execution used for data/AI services?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Temporal?When is Temporal/durable execution the wrong choice?What do you have planned for the future of Temporal for data and AI systems? Contact Info   LinkedIn Parting 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   TemporalDurable ExecutionFlinkMachine Learning EpochSpark StreamingAirflowDirected Acyclic Graph (DAG)Temporal NexusTensorZeroAI Engineering Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA  

Summary In this episode of the Data Engineering Podcast Ariel Pohoryles, head of product marketing for Boomi's data management offerings, talks about a recent survey of 300 data leaders on how organizations are investing in data to scale AI. He shares a paradox uncovered in the research: while 77% of leaders trust the data feeding their AI systems, only 50% trust their organization's data overall. Ariel explains why truly productionizing AI demands broader, continuously refreshed data with stronger automation and governance, and highlights the challenges posed by unstructured data and vector stores. The conversation covers the need to shift from manual reviews to automated pipelines, the resurgence of metadata and master data management, and the importance of guardrails, traceability, and agent governance. Ariel also predicts a growing convergence between data teams and application integration teams and advises leaders to focus on high-value use cases, aggressive pipeline automation, and cataloging and governing the coming sprawl of AI agents, all while using AI to accelerate data engineering itself.

Announcements Hello 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.Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Ariel Pohoryles about data management investments that organizations are making to enable them to scale AI implementationsInterview IntroductionHow did you get involved in the area of data management?Can you start by describing the motivation and scope of your recent survey on data management investments for AI across your respondents?What are the key takeaways that were most significant to you?The survey reveals a fascinating paradox: 77% of leaders trust the data used by their AI systems, yet only half trust their organization's overall data quality. For our data engineering audience, what does this suggest about how companies are currently sourcing data for AI? Does it imply they are using narrow, manually-curated "golden datasets," and what are the technical challenges and risks of that approach as they try to scale?The report highlights a heavy reliance on manual data quality processes, with one expert noting companies feel it's "not reliable to fully automate validation" for external or customer data. At the same time, maturity in "Automated tools for data integration and cleansing" is low, at only 42%. What specific technical hurdles or organizational inertia are preventing teams from adopting more automation in their data quality and integration pipelines?There was a significant point made that with generative AI, "biases can scale much faster," making automated governance essential. From a data engineering perspective, how does the data management strategy need to evolve to support generative AI versus traditional ML models? What new types of data quality checks, lineage tracking, or monitoring for feedback loops are required when the model itself is generating new content based on its own outputs?The report champions a "centralized data management platform" as the "connective tissue" for reliable AI. How do you see the scale and data maturity impacting the realities of that effort?How do architectural patterns in the shape of cloud warehouses, lakehouses, data mesh, data products, etc. factor into that need for centralized/unified platforms?A surprising finding was that a third of respondents have not fully grasped the risk of significant inaccuracies in their AI models if they fail to prioritize data management. In your experience, what are the biggest blind spots for data and analytics leaders?Looking at the maturity charts, companies rate themselves highly on "Developing a data management strategy" (65%) but lag significantly in areas like "Automated tools for data integration and cleansing" (42%) and "Conducting bias-detection audits" (24%). If you were advising a data engineering team lead based on these findings, what would you tell them to prioritize in the next 6-12 months to bridge the gap between strategy and a truly scalable, trustworthy data foundation for AI?The report states that 83% of companies expect to integrate more data sources for their AI in the next year. For a data engineer on the ground, what is the most important capability they need to build into their platform to handle this influx?What are the most interesting, innovative, or unexpected ways that you have seen teams addressing the new and accelerated data needs for AI applications?What are some of the noteworthy trends or predictions that you have for the near-term future of the impact that AI is having or will have on data teams and systems?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 BoomiData ManagementIntegration & Automation DemoAgentstudioData Connector Agent WebinarSurvey ResultsData GovernanceShadow ITPodcast EpisodeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Omri Lifshitz (CTO) and Ido Bronstein (CEO) of Upriver talk about the growing gap between AI's demand for high-quality data and organizations' current data practices. They discuss why AI accelerates both the supply and demand sides of data, highlighting that the bottleneck lies in the "middle layer" of curation, semantics, and serving. Omri and Ido outline a three-part framework for making data usable by LLMs and agents: collect, curate, serve, and share challenges of scaling from POCs to production, including compounding error rates and reliability concerns. They also explore organizational shifts, patterns for managing context windows, pragmatic views on schema choices, and Upriver's approach to building autonomous data workflows using determinism and LLMs at the right boundaries. The conversation concludes with a look ahead to AI-first data platforms where engineers supervise business semantics while automation stitches technical details end-to-end.

Announcements Hello 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.Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Omri Lifshitz and Ido Bronstein about the challenges of keeping up with the demand for data when supporting AI systemsInterview IntroductionHow did you get involved in the area of data management?We're here to talk about "The Growing Gap Between Data & AI". From your perspective, what is this gap, and why do you think it's widening so rapidly right now?How does this gap relate to the founding story of Upriver? What problems were you and your co-founders experiencing that led you to build this?The core premise of new AI tools, from RAG pipelines to LLM agents, is that they are only as good as the data they're given. How does this "garbage in, garbage out" problem change when the "in" is not a static file but a complex, high-velocity, and constantly changing data pipeline?Upriver is described as an "intelligent agent system" and an "autonomous data engineer." This is a fascinating "AI to solve for AI" approach. Can you describe this agent-based architecture and how it specifically works to bridge that data-AI gap?Your website mentions a "Data Context Layer" that turns "tribal knowledge" into a "machine-usable mode." This sounds critical for AI. How do you capture that context, and how does it make data "AI-ready" in a way that a traditional data catalog or quality tool doesn't?What are the most innovative or unexpected ways you've seen companies trying to make their data "AI-ready"? And where are the biggest points of failure you observe?What has been the most challenging or unexpected lesson you've learned while building an AI system (Upriver) that is designed to fix the data foundation for other AI systems?When is an autonomous, agent-based approach not the right solution for a team's data quality problems? What organizational or technical maturity is required to even start closing this data-AI gap?What do you have planned for the future of Upriver? And looking more broadly, how do you see this gap between data and AI evolving over the next few years?Contact Info Ido - LinkedInOmri - 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 UpriverRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodeAI AgentContext WindowModel Finetuning)The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Matt Topper, president of UberEther, talks about the complex challenge of identity, credentials, and access control in modern data platforms. With the shift to composable ecosystems, integration burdens have exploded, fracturing governance and auditability across warehouses, lakes, files, vector stores, and streaming systems. Matt shares practical solutions, including propagating user identity via JWTs, externalizing policy with engines like OPA/Rego and Cedar, and using database proxies for native row/column security. He also explores catalog-driven governance, lineage-based label propagation, and OpenTDF for binding policies to data objects. The conversation covers machine-to-machine access, short-lived credentials, workload identity, and constraining access by interface choke points, as well as lessons from Zanzibar-style policy models and the human side of enforcement. Matt emphasizes the need for trust composition - unifying provenance, policy, and identity context - to answer questions about data access, usage, and intent across the entire data path.

Announcements Hello 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.Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Matt Topper about the challenges of managing identity and access controls in the context of data systemsInterview IntroductionHow did you get involved in the area of data management?The data ecosystem is a uniquely challenging space for creating and enforcing technical controls for identity and access control. What are the key considerations for designing a strategy for addressing those challenges?For data acess the off-the-shelf options are typically on either extreme of too coarse or too granular in their capabilities. What do you see as the major factors that contribute to that situation?Data governance policies are often used as the primary means of identifying what data can be accesssed by whom, but translating that into enforceable constraints is often left as a secondary exercise. How can we as an industry make that a more manageable and sustainable practice?How can the audit trails that are generated by data systems be used to inform the technical controls for identity and access?How can the foundational technologies of our data platforms be improved to make identity and authz a more composable primitive?How does the introduction of streaming/real-time data ingest and delivery complicate the challenges of security controls?What are the most interesting, innovative, or unexpected ways that you have seen data teams address ICAM?What are the most interesting, unexpected, or challenging lessons that you have learned while working on ICAM?What are the aspects of ICAM in data systems that you are paying close attention to?What are your predictions for the industry adoption or enforcement of those controls?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 UberEtherJWT == JSON Web TokenOPA == Open Policy AgentRegoPingIdentityOktaMicrosoft EntraSAML == Security Assertion Markup LanguageOAuthOIDC == OpenID ConnectIDP == Identity ProviderKubernetesIstioAmazon CEDAR policy languageAWS IAMPII == Personally Identifiable InformationCISO == Chief Information Security OfficerOpenTDFOpenFGAGoogle ZanzibarRisk Management FrameworkModel Context ProtocolGoogle Data ProjectTPM == Trusted Platform ModulePKI == Public Key InfrastructurePassskeysDuckLakePodcast EpisodeAccumuloJDBCOpenBaoHashicorp VaultLDAPThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode Kate Shaw, Senior Product Manager for Data and SLIM at SnapLogic, talks about the hidden and compounding costs of maintaining legacy systems—and practical strategies for modernization. She unpacks how “legacy” is less about age and more about when a system becomes a risk: blocking innovation, consuming excess IT time, and creating opportunity costs. Kate explores technical debt, vendor lock-in, lost context from employee turnover, and the slippery notion of “if it ain’t broke,” especially when data correctness and lineage are unclear. Shee digs into governance, observability, and data quality as foundations for trustworthy analytics and AI, and why exit strategies for system retirement should be planned from day one. The discussion covers composable architectures to avoid monoliths and big-bang migrations, how to bridge valuable systems into AI initiatives without lock-in, and why clear success criteria matter for AI projects. Kate shares lessons from the field on discovery, documentation gaps, parallel run strategies, and using integration as the connective tissue to unlock data for modern, cloud-native and AI-enabled use cases. She closes with guidance on planning migrations, defining measurable outcomes, ensuring lineage and compliance, and building for swap-ability so teams can evolve systems incrementally instead of living with a “bowl of spaghetti.”

Announcements Hello 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 Kate Shaw about the true costs of maintaining legacy systemsInterview IntroductionHow did you get involved in the area of data management?What are your crtieria for when a given system or service transitions to being "legacy"?In order for any service to survive long enough to become "legacy" it must be serving its purpose and providing value. What are the common factors that prompt teams to deprecate or migrate systems?What are the sources of monetary cost related to maintaining legacy systems while they remain operational?Beyond monetary cost, economics also have a concept of "opportunity cost". What are some of the ways that manifests in data teams who are maintaining or migrating from legacy systems?How does that loss of productivity impact the broader organization?How does the process of migration contribute to issues around data accuracy, reliability, etc. as well as contributing to potential compromises of security and compliance?Once a system has been replaced, it needs to be retired. What are some of the costs associated with removing a system from service?What are the most interesting, innovative, or unexpected ways that you have seen teams address the costs of legacy systems and their retirement?What are the most interesting, unexpected, or challenging lessons that you have learned while working on legacy systems migration?When is deprecation/migration the wrong choice?How have evolutionary architecture patterns helped to mitigate the costs of system retirement?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 SnapLogicSLIM == SnapLogic Intelligent ModernizerOpportunity CostSunk Cost FallacyData GovernanceEvolutionary ArchitectureThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Nick Schrock, CTO and founder of Dagster Labs, to discuss Compass - a Slack-native, agentic analytics system designed to keep data teams connected with business stakeholders. Nick shares his journey from initial skepticism to embracing agentic AI as model and application advancements made it practical for governed workflows, and explores how Compass redefines the relationship between data teams and stakeholders by shifting analysts into steward roles, capturing and governing context, and integrating with Slack where collaboration already happens. The conversation covers organizational observability through Compass's conversational system of record, cost control strategies, and the implications of agentic collaboration on Conway's Law, as well as what's next for Compass and Nick's optimistic views on AI-accelerated software engineering.

Announcements Hello 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 Nick Schrock about building an AI analyst that keeps data teams in the loopInterview IntroductionHow did you get involved in the area of data management?Can you describe what Compass is and the story behind it?context repository structurehow to keep it relevant/avoid sprawl/duplicationproviding guardrailshow does a tool like Compass help provide feedback/insights back to the data teams?preparing the data warehouse for effective introspection by the AILLM selectioncost managementcaching/materializing ad-hoc queriesWhy Slack and enterprise chat are important to b2b softwareHow AI is changing stakeholder relationshipsHow not to overpromise AI capabilities How does Compass relate to BI?How does Compass relate to Dagster and Data Infrastructure?What are the most interesting, innovative, or unexpected ways that you have seen Compass used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Compass?When is Compass the wrong choice?What do you have planned for the future of Compass?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 DagsterDagster LabsDagster PlusDagster CompassChris Bergh DataOps EpisodeRise of Medium Code blog postContext EngineeringData StewardInformation ArchitectureConway's LawTemporal durable execution frameworkThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In 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.

Announcements Hello 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 analyticsInterview IntroductionHow 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 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 Metric 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

Summary In 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-amble I 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.

Announcements Hello 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 workloadsInterview IntroductionHow 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 Info LinkedInParting Question From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Links Flex 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

Summary In 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.

Announcements Hello 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 databasesInterview IntroductionHow 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 Info BlogLinkedInParting 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 AWS 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

Summary In 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.

Announcements Hello 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 ecosystemInterview IntroductionHow 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 Info HannesWebsiteMarkWebsiteParting 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 DuckDBPodcast 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

Summary In this episode of the Data Engineering Podcast Akshay Agrawal from Marimo discusses the innovative new Python notebook environment, which offers a reactive execution model, full Python integration, and built-in UI elements to enhance the interactive computing experience. He discusses the challenges of traditional Jupyter notebooks, such as hidden states and lack of interactivity, and how Marimo addresses these issues with features like reactive execution and Python-native file formats. Akshay also explores the broader landscape of programmatic notebooks, comparing Marimo to other tools like Jupyter, Streamlit, and Hex, highlighting its unique approach to creating data apps directly from notebooks and eliminating the need for separate app development. The conversation delves into the technical architecture of Marimo, its community-driven development, and future plans, including a commercial offering and enhanced AI integration, emphasizing Marimo's role in bridging the gap between data exploration and production-ready applications.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementTired of data migrations that drag on for months or even years? What if I told you there's a way to cut that timeline by up to 6x while guaranteeing accuracy? Datafold's Migration Agent is the only AI-powered solution that doesn't just translate your code; it validates every single data point to ensure perfect parity between your old and new systems. Whether you're moving from Oracle to Snowflake, migrating stored procedures to dbt, or handling complex multi-system migrations, they deliver production-ready code with a guaranteed timeline and fixed price. Stop burning budget on endless consulting hours. Visit dataengineeringpodcast.com/datafold to book a demo and see how they're turning months-long migration nightmares into week-long success stories.Your host is Tobias Macey and today I'm interviewing Akshay Agrawal about Marimo, a reusable and reproducible Python notebook environmentInterview IntroductionHow did you get involved in the area of data management?Can you describe what Marimo is and the story behind it?What are the core problems and use cases that you are focused on addressing with Marimo?What are you explicitly not trying to solve for with Marimo?Programmatic notebooks have been around for decades now. Jupyter was largely responsible for making them popular outside of academia. How have the applications of notebooks changed in recent years?What are the limitations that have been most challenging to address in production contexts?Jupyter has long had support for multi-language notebooks/notebook kernels. What is your opinion on the utility of that feature as a core concern of the notebook system?Beyond notebooks, Streamlit and Hex have become quite popular for publishing the results of notebook-style analysis. How would you characterize the feature set of Marimo for those use cases?For a typical data team that is working across data pipelines, business analytics, ML/AI engineering, etc. How do you see Marimo applied within and across those contexts?One of the common difficulties with notebooks is that they are largely a single-player experience. They may connect into a shared compute cluster for scaling up execution (e.g. Ray, Dask, etc.). How does Marimo address the situation where a data platform team wants to offer notebooks as a service to reduce the friction to getting started with analyzing data in a warehouse/lakehouse context?How are you seeing teams integrate Marimo with orchestrators (e.g. Dagster, Airflow, Prefect)?What are some of the most interesting or complex engineering challenges that you have had to address while building and evolving Marimo?\What are the most interesting, innovative, or unexpected ways that you have seen Marimo used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Marimo?When is Marimo the wrong choice?What do you have planned for the future of Marimo?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 MarimoJupyterIPythonStreamlitPodcast.init EpisodeVector EmbeddingsDimensionality ReductionKagglePytestPEP 723 script dependency metadataMatLabVisicalcMathematicaRMarkdownRShinyElixir LivebookDatabricks NotebooksPapermillPluto - Julia NotebookHexDirected Acyclic Graph (DAG)Sumble Kaggle founder Anthony Goldblum's startupRayDaskJupytextnbdevDuckDBPodcast EpisodeIcebergSupersetjupyter-marimo-proxyJupyterHubBinderNixAnyWidgetJupyter WidgetsMatplotlibAltairPlotlyDataFusionPolarsMotherDuckThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary

This podcast started almost exactly six years ago, and the technology landscape was much different than it is now. In that time there have been a number of generational shifts in how data engineering is done. In this episode I reflect on some of the major themes and take a brief look forward at some of the upcoming changes.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Your host is Tobias Macey and today I'm reflecting on the major trends in data engineering over the past 6 years

Interview

Introduction 6 years of running the Data Engineering Podcast Around the first time that data engineering was discussed as a role

Followed on from hype about "data science"

Hadoop era Streaming Lambda and Kappa architectures

Not really referenced anymore

"Big Data" era of capture everything has shifted to focusing on data that presents value

Regulatory environment increases risk, better tools introduce more capability to understand what data is useful

Data catalogs

Amundsen and Alation

Orchestration engine

Oozie, etc. -> Airflow and Luigi -> Dagster, Prefect, Lyft, etc. Orchestration is now a part of most vertical tools

Cloud data warehouses Data lakes DataOps and MLOps Data quality to data observability Metadata for everything

Data catalog -> data discovery -> active metadata

Business intelligence

Read only reports to metric/semantic layers Embedded analytics and data APIs

Rise of ELT

dbt Corresponding introduction of reverse ETL

What are the most interesting, unexpected, or challenging lessons that you have learned while working on running the podcast? What do you have planned for the future of the podcast?

Parting 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 Machine Learning Podcast helps you go from idea to production with machine learning. 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. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

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

Looking for the simplest way to get the freshest data possible to your teams? Because let's face it: if real-time were easy, everyone would be using it. Look no further than Materialize, the streaming database you already know how to use.

Materialize’s PostgreSQL-compatible interface lets users leverage the tools they already use, with unsurpassed simplicity enabled by full ANSI SQL support. Delivered as a single platform with the separation of storage and compute, strict-serializability, active replication, horizontal scalability and workload isolation — Materialize is now the fastest way to build products with streaming data, drastically reducing the time, expertise, cost and maintenance traditionally associated with implementation of real-time features.

Sign up now for early access to Materialize and get started with the power of streaming data with the same simplicity and low implementation cost as batch cloud data warehouses.

Go to materialize.comSupport Data Engineering Podcast

Summary Building data products is an undertaking that has historically required substantial investments of time and talent. With the rise in cloud platforms and self-serve data technologies the barrier of entry is dropping. Shane Gibson co-founded AgileData to make analytics accessible to companies of all sizes. In this episode he explains the design of the platform and how it builds on agile development principles to help you focus on delivering value.

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 their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit dataengineeringpodcast.com/prefect. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., are already available as plug-and-play connectors with reliable, intuitive SaaS solutions. Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance. Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transparent pricing and 24*7 live support, makes it consistently voted by users as the Leader in the Data Pipeline category on review platforms like G2. Go to dataengineeringpodcast.com/hevodata and sign up for a free 14-day trial that also comes with 24×7 support. Your host is Tobias Macey and today I’m interviewing Shane Gibson about AgileData

Summary A lot of the work that goes into data engineering is trying to make sense of the "data exhaust" from other applications and services. There is an undeniable amount of value and utility in that information, but it also introduces significant cost and time requirements. In this episode Nick King discusses how you can be intentional about data creation in your applications and services to reduce the friction and errors involved in building data products and ML applications. He also describes the considerations involved in bringing behavioral data into your systems, and the ways that he and the rest of the Snowplow team are working to make that an easy addition to your platforms.

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 their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit dataengineeringpodcast.com/prefect. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., are already available as plug-and-play connectors with reliable, intuitive SaaS solutions. Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance. Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transparent pricing and 24*7 live support, makes it consistently voted by users as the Leader in the Data Pipeline

Summary Business intelligence has grown beyond its initial manifestation as dashboards and reports. In its current incarnation it has become a ubiquitous need for analytics and opportunities to answer questions with data. In this episode Amir Orad discusses the Sisense platform and how it facilitates the embedding of analytics and data insights in every aspect of organizational and end-user experiences.

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 their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit dataengineeringpodcast.com/prefect. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., are already available as plug-and-play connectors with reliable, intuitive SaaS solutions. Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance. Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transparent pricing and 24*7 live support, makes it consistently voted by users as the Leader in the Data Pipeline category on review platforms like G2. Go to dataengineeringpodcast.com/hevodata and sign up for a free 14-day trial that also comes with 24×7 support. Your host is Tobias Macey and today I’m interviewing Amir Orad about Sisense, a platform focused on providing intelligent analytics

Summary Agile methodologies have been adopted by a majority of teams for building software applications. Applying those same practices to data can prove challenging due to the number of systems that need to be included to implement a complete feature. In this episode Shane Gibson shares practical advice and insights from his years of experience as a consultant and engineer working in data about how to adopt agile principles in your data work so that you can move faster and provide more value to the business, while building systems that are maintainable and adaptable.

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 their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit dataengineeringpodcast.com/prefect. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., are already available as plug-and-play connectors with reliable, intuitive SaaS solutions. Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance. Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transparent pricing and 24*7 live support, makes it consistently voted by users as the Leader in the Data Pipeline category on review platforms like G2. Go to dataengineeringpodcast.com/hevodata and sign up for a free 14-day trial that also comes with 24×7 support. Your host is Tobias Macey and today I’m interviewing Shane Gibson about how to bring Agile practices to your data management workflows

Interview

Introduction How did you get involved in the area of data management? Can you describe what AgileData is and the story behind it? What are the main industries and/or use cases that you are focused on supporting? The data ecosystem has been trying on different paradigms from software development for some time now (e.g. DataOps, version control, etc.). What are the aspects of Agile that do and don’t map well to data engineering/analysis? One of the perennial challenges of data analysis is how to approach data modeling. How do you balance the need to provide value with the long-term impacts of incomplete or underinformed modeling decisions made in haste at the beginning of a project?

How do you design in affordances for refactoring of the data models without breaking downstream assets?

Another aspect of implementing data products/platforms is how to manage permissions and governance. What are the incremental ways that those principles can be incorporated early and evolved along with the overall analytical products? What are some of the organizational design strategies that you find most helpful when establishing or training a team who is working on data products? In order to have a useful target to work toward it’s necessary to understand what the data consumers are hoping to achieve. What are some of the challenges of doing requirements gathering for data products? (e.g. not knowing what information is available, consumers not understanding what’s hard vs. easy, etc.)

How do you work with the "customers" to help them understand what a reasonable scope is and translate that to the actual project stages for the engineers?

What are some of the perennial questions or points of confusion that you have had to address with your clients on how to design and implement analytical assets? What are the most interesting, innovative, or unexpected ways that you have seen agile principles used for data? What are the most interesting, unexpected, or challenging lessons that you have learned while working on AgileData? When is agile the wrong choice for a data project? What do you have planned for the future of AgileData?

Contact Info

LinkedIn @shagility on Twitter

Parting 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 Machine Learning Podcast helps you go from idea to production with machine learning. 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. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

AgileData OptimalBI How To Make Toast Data Mesh Information Product Canvas DataKitchen

Podcast Episode

Great Expectations

Podcast Episode

Soda Data

Podcast Episode

Google DataStore Unfix.work Activity Schema

Podcast Episode

Data Vault

Podcast Episode

Star Schema Lean Methodology Scrum Kanban

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

Sponsored By: Atlan:

Have you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means?

Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more.

Go to dataengineeringpodcast.com/atlan and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription.Prefect:

Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit…

Summary Logistics and supply chains are under increased stress and scrutiny in recent years. In order to stay ahead of customer demands, businesses need to be able to react quickly and intelligently to changes, which requires fast and accurate insights into their operations. Pathway is a streaming database engine that embeds artificial intelligence into the storage, with functionality designed to support the spatiotemporal data that is crucial for shipping and logistics. In this episode Adrian Kosowski explains how the Pathway product got started, how its design simplifies the creation of data products that support supply chain operations, and how developers can help to build an ecosystem of applications that allow businesses to accelerate their time to insight.

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 their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit dataengineeringpodcast.com/prefect. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., are already available as plug-and-play connectors with reliable, intuitive SaaS solutions. Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance. Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transparent pricing and 24*7 live s

Summary The core of any data platform is the centralized storage and processing layer. For many that is a data warehouse, but in order to support a diverse and constantly changing set of uses and technologies the data lakehouse is a paradigm that offers a useful balance of scale and cost, with performance and ease of use. In order to make the data lakehouse available to a wider audience the team at Iomete built an all-in-one service that handles management and integration of the various technologies so that you can worry about answering important business questions. In this episode Vusal Dadalov explains how the platform is implemented, the motivation for a truly open architecture, and how they have invested in integrating with the broader ecosystem to make it easy for you to get started.

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 their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit dataengineeringpodcast.com/prefect. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., are already available as plug-and-play connectors with reliable, intuitive SaaS solutions. Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance. Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transpar

Summary The global economy is dependent on complex and dynamic networks of supply chains powered by sophisticated logistics. This requires a significant amount of data to track shipments and operational characteristics of materials and goods. Roambee is a platform that collects, integrates, and analyzes all of that information to provide companies with the critical insights that businesses need to stay running, especially in a time of such constant change. In this episode Roambee CEO, Sanjay Sharma, shares the types of questions that companies are asking about their logistics, the technical work that they do to provide ways to answer those questions, and how they approach the challenge of data quality in its many forms.

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 their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit dataengineeringpodcast.com/prefect. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., are already available as plug-and-play connectors with reliable, intuitive SaaS solutions. Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance. Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transparent pricing and 24*7 live support, makes it consistently voted by user