Data Engineering Podcast

Summary In this episode Soham Azumdar, co-founder and CEO of Wisdom.ai, talks about what “context” really means in data engineering and AI systems. He explores why context has become such an overloaded term, spanning everything from semantic layers and data catalogs to tribal knowledge, query logs, dashboards, and even agent memory. Soham explained that the big shift is that context is no longer being prepared primarily for human analysts, but for LLMs and agents that can’t reliably fill in missing gaps on their own. That change raises the bar for how context is represented, validated, benchmarked, and maintained so that AI systems can produce trustworthy outcomes. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Your host is Tobias Macey and today I'm interviewing Soham Mazumdar about what "context" actually means in data engineering Interview Introduction How did you get involved in the area of data management? One of the perennial challenges of engineering in all forms is building a shared understanding of what a given word means. "Context" is one that is being used for an increasing number of purposes with the introduction of AI agents. Can you start by sharing some of the ways that this terminology overload has caused problems in your own experience? Data engineering has arguably always been about context engineering, but at the scale of human consumers. What are the substantive changes that AI/agentic consumers bring to the discipline? While we all understand the notion of "context", turning it into a useful and re-usable component is a different matter entirely. What are some of the ways that "business context" or "technical context" manifests as a tangible artifact? This also brings up the question of data modeling. What are some of the key attributes that are necessary when storing, enriching, evolving, and joining into that context? One could argue that the entire history of data warehousing is about building organizational context. What are the real differences in approach for today's work of capturing and activating that context? How does your work at Wisdom AI address the technical and operational burdens of capturing, modeling, and exposing context at the speed necessary to keep up with organizational demands? What are the most interesting, innovative, or unexpected ways that you have seen Wisdom AI used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Wisdom AI/context engineering? When is Wisdom AI the wrong choice? What do you have planned for the future of Wisdom AI? 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 Wisdom AI Context Engineering Knowledge Graph Ontology Snowflake Open Semantic Interchange (OSI) Semantic Layer Palantir Foundry The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode Yetunde Dada discusses Otto, Astronomer’s AI agent for Airflow, and the broader challenge of making agentic tooling actually useful for data engineers. She explored why generic coding assistants often fall short in data workflows, how Otto adds the missing context around Airflow, Astro, upgrades, and troubleshooting, and why Astronomer focused first on high-leverage use cases such as DAG authoring, investigation of pipeline failures, version migrations, and legacy scheduler modernization. She also discussed the practical realities of introducing agents into engineering teams: model choice, security boundaries, vendor lock-in concerns, validation of generated code, and the need for agents to fit into existing workflows rather than forcing users into new ones. Overall, this conversation offers a detailed look at how specialized AI agents can support data engineers today, and where Astronomer is headed next with a vision for self-healing pipelines that keep humans in control while automating more of the operational burden. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Your host is Tobias Macey and today I'm interviewing Yetunde Dada about Otto, Astronomer's expert Airflow agent Interview Introduction How did you get involved in the area of data management? Can you describe what Otto is and the story behind it? What are the core problems that you are trying to solve with Otto and for whom? What was your process for identifying the scope of activities that Otto should be incorporated into? Orchestration engines are a rich source of information. What are the aspects of Airflow that lend themselves to extending with this agentic context? What are the other supporting systems that are necessary to enable Otto to work effectively, especially in mixed orchestration environments? (e.g. metadata platforms) One of the explicit capabilities that you invested in is code review for Airflow DAGs. What are the pain points that you are trying to solve with a specialized review agent? Can you describe the architecture of the Otto system and how you're managing the complex task of context curation? In a production context accuracy and latency are both critical, and often in tension with each other. How do you monitor and optimize for each of those objectives?What are the options for tuning Otto's behavior to bias more toward one direction or another? What are some examples of the type of work that Otto can help automate? How is it measurably different from a generic coding agent that has MCP connections to something like an Open Metadata or DataHub for platform and data context, Airflow documentation, etc.? There are numerous general purpose and specialized agent systems available. What are some of the ways that Otto can work collaboratively with those other products? What are the most interesting, innovative, or unexpected ways that you have seen Otto used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Otto? What do you have planned for the future of Otto? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Astronomer Otto Announcement Post Astronomer Cosmo dbt automation Hadoop Spark Otto Automatic Pipeline Failure Investigation Otto Code Review Astro CLI Astro IDE Airflow MCP Kedro Quantum Black Django React Airflow Providers Pi Framework Agent Skills AGENTS.md The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode Ragnor Comerford talks about OmniGraph, a lakehouse-native graph storage layer designed around the needs of agentic systems. He explores how graphs are primarily a semantic model for representing the world, rather than just a specialized engine for traversal workloads, and how that perspective shaped OmniGraph’s design on top of object storage, Lance, Arrow, and DataFusion. Ragnor explained the motivation for combining graph semantics with Git-style branching and merging so that teams can manage probabilistic writers such as AI agents with stronger governance, shared context, and safer collaboration patterns. He also dug into the practical tradeoffs of building a graph engine for multi-agent coordination instead of traditional graph analytics use cases. He closed with a look at emerging use cases such as company “brain” systems, software development lifecycle graphs, research workflows, and event-driven agent orchestration, along with a broader conversation about composability, and sovereign AI infrastructure. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Your host is Tobias Macey and today I'm interviewing Ragnor Comerford about OmniGraph, a lakehouse-native graph storage layer with git semantics Interview Introduction How did you get involved in the area of data management? Can you describe what OmniGraph is and the story behind it? What was the original problem that you were trying to solve by creating it? There are numerous graph engines available, what are the properties of OmniGraph that differentiate it from the competition? Cypher (GQL) and Gremlin are all established languages with years of examples to work from. What are the benefits of developing a new and more constrained query interface for an agentic audience? How does that change the potential applications of OmniGraph? (e.g. general knowledge graph, fraud detection, SIEM, etc.) Can you describe the architecture of OmniGraph? You have built the system on top of several well-established open source components. What was your process for deciding what to use and how to compose it? What are some examples of systems that can be built with OmniGraph? What are other components/integration points that compose well with OmniGraph? Given the technologies that you are building on top of, what are the automatic benefits/integrations that you benefit from? Given that the underlying storage is Lance, and Lance's interoperability with Parquet/Iceberg, what are the opportunities for modeling graphs on top of existing lakehouse data? What are the most interesting, innovative, or unexpected ways that you have seen OmniGraph used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on OmniGraph? When is OmniGraph the wrong choice? What do you have planned for the future of OmniGraph? 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 OmniGraph ModernRelay Information Theory Lance Git Neo4J GraphRAG TigerGraph LakeHouse Iceberg Dolt PuppyGraph Podcast Episode Terraform Gremlin Cypher GQL SPARQL In-context Learning BM25 Indexing Data Fusion Adjacency Matrix Predicate Pushdown Agentic Mesh book (affiliate link) witan-council witan-code Web Assembly Clickhouse DSPy MCP-UI The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode Prukalpa Sankar, co-founder of Atlan, talks about what it takes to build a “context flywheel” for AI agents in data-intensive organizations. She explained why model intelligence alone isn’t enough to make AI useful in production, and how real performance depends on contextual intelligence: institutional knowledge, semantic meaning, procedural know-how, and access to the right tools. She also dug into how metadata catalogs are evolving into broader context layers that serve both humans and agents, and why agentic systems are changing the economics of metadata and governance work. Prakulpa shared Atlan’s perspective on bootstrapping context from existing systems such as warehouses, BI tools, query logs, and SaaS applications, then using simulation, traces, and human governance loops to improve agent accuracy over time. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Your host is Tobias Macey and today I'm interviewing Prukalpa Sankar about strategies for building a context flywheel for your data agents Interview Introduction How did you get involved in the area of data management? You have spent several years working in the metadata catalog space with Atlan. What are the notable changes in scope, adoption, and application that you have seen since we last spoke (June 2022)? The recurring theme since the start of 2026 has been agentic augmentation of all engineering workflows, including data. How do you differentiate between data catalogs, semantic layers, agent memory, context layers, etc. when architecting an AI-powered data-oriented system? One of the perennial problems with data catalogs, business glossaries, master data management, etc. is the up-front investment required to get a real-world impact. How can agents help reduce the activation energy needed to get to that return on effort? One of the perennial problems in data engineering is fragmentation and siloing of data. This is exacerbated by AI systems due to the introduction of vector data as a new specialization. What are the forces that you are seeing play into the current set of tensions and the architectural primitives that we need to bring to bear to keep things maintainable? Since the introduction of transformer-based generative models we have been combating hallucinations. While we have made progress, it is still critical to ensure accuracy and trustworthiness when working with business data. What are the policy elements of governance and technical controls to ensure a high degree of confidence in agent-generated context and business semantics? What are the most interesting, innovative, or unexpected ways that you have seen teams build context layers for their agentic data workloads? What are the most interesting, unexpected, or challenging lessons that you have learned while working on business context engineering? When is agent-managed context the wrong choice? What are your predictions for the next set of architectural shifts that will be driven by the pressures of AI-powered 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 Atlan Atlan Context Lakehouse Iceberg Business Glossary Master Data Management Semantic Layer Cube.dev MCP == Model Context Protocol A2A == Agent to Agent Protocol Decision Traces Apache Doris StarRocks The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode Jevin Maltais talks about the practical realities of building reliable, product-focused streaming systems with Kafka. Jevin shares lessons from roles at Zapier, Humi, and Clio, where real-time synchronization, customer data unification, and document sync at scale highlighted both the strengths and common misuses of Kafka. He digs into using events as the source of truth, materialized views with KTables, and how schema registries and type safety prevent downstream breakage. Jevin explains why teams often reach for heavyweight Kafka clusters without leveraging Streams, Connect, or interactive queries—and how his project, TypeStream, aims to make those capabilities accessible via config-as-code while keeping a thin abstraction and clear escape hatches. He also explore trade-offs across Kafka-compatible alternatives, CDC with Debezium in the real world, and where abstractions should stop so teams can scale responsibility as complexity grows. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is sponsored by DataDriven.io, the free data engineering interview prep platform built by data engineers for data engineers. Ever walked into a data engineering interview and gotten a question that has nothing to do with real data engineering work? Interviewing is its own skill, separate from the job. Watch your code execute live, inspect Spark internals, and whiteboard your data models and pipelines and defend your decisions. Unlike SQL-only or Python-only practice, DataDriven.io covers the full interview loop: star schemas, slowly changing dimensions, grain and fact table design, idempotency, watermarks, dead letter queues, change data capture, and backpressure. Every question comes from real Data Engineer interview loops at Google, Amazon, Meta, Stripe, Databricks, Netflix, and Airbnb. Go to dataengineeringpodcast.com/datadriven today to start practicing. Your host is Tobias Macey and today I'm interviewing Jevin Maltais about the challenges of building a reliable streaming Interview Introduction How did you get involved in the area of data management? Can you describe what Typestream is and the story behind it? What are the common challenges that teams encounter when trying to build on top of Kafka? How do those challenges/misconfigurations impact the team's ability to deliver on product goals? What are the fundamental design aspects of Kafka that contribute to the difficulties that teams encounter when using it as an element of their architecture? There have been numerous projects taking aim at Kafka, with varying approaches and degrees of effectiveness (e.g. RedPanda, AutoMQ, Pulsar, etc.). What are the tradeoffs that each of those approaches requires? What makes the original Kafka project so resilient in the face of all of that competition? Can you describe the architecture of Typestream and how each of the core elements contribute to a better user experience? For teams who want to take advantage of streaming capabilities, but don't want to invest in becoming Kafka experts, what does the Typestream workflow look like? If they don't want to manage the operational overhead of a Kafka cluster, how tightly coupled is Typestream to the original Kafka? (can someone use RedPanda or AutoMQ instead?) What are the most interesting, innovative, or unexpected ways that you have seen Typestream used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Typestream? When is Typestream the wrong choice? What do you have planned for the future of Typestream? Contact Info Website 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 Typestream Zapier Airflow Kafka KTables KSQL RedPanda Pulsar AutoMQ Kafka Schema Registry Debezium Change Data Capture Kafka Connect Terraform Kafka Compacted Topic The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA