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PODCAST

Data Engineering Podcast

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

technology

All Episodes

00:52:52

Text to Data Products: Kaarvi’s End-to-End AI for...

Data Engineering Podcast ·

2026/06/08

SummaryIn this episode Shravan Gunda, founder and CEO of Kaarvi AI, talks about building an AI-native, agent-driven data platform designed to eliminate the janitorial work that consumes most data teams. He explores Kaarvi’s multi-agent architecture that runs queries across seven LLMs in parallel for reliability, its synthetic data generator that mirrors source schemas for quick testing, and “Hey Kaarvi” chat for text-to-SQL, text-to-transformations, and text-to-dashboard workflows. He also digs into

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00:54:20

Scaling Graph Analytics Without ETL: Inside...

Data Engineering Podcast ·

2026/06/01

SummaryIn this episode Weimo Liu, co‑founder of PuppyGraph, talks about the engineering behind their “zero-copy” graph querying engine for lakehouse and database sources. He explores how PuppyGraph lets you run Cypher and Gremlin traversals and graph algorithms directly on data in Iceberg, Delta, Hudi, Hive, and even MongoDB—without loading into a separate graph store. Weimo explains their edge-sharded, vectorized, MPP architecture that tackles hub nodes, multi-hop traversals, and shuffle at scale,

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00:58:34

Maximizing GPU Utilization: Heterogeneous Pipelines...

Data Engineering Podcast ·

2026/05/06

SummaryIn this episode Robert Nishihara, co-founder of Anyscale and co-creator of Ray, talks about maximizing hardware utilization for AI and data-intensive workloads. He explores Ray’s evolution alongside Kubernetes and PyTorch, and why consolidation at these layers has enabled a new generation of complex, heterogeneous workloads. Robert explains how data preparation has shifted to GPU- and inference-heavy, multimodal pipelines; where Ray fits compared to Spark and workflow orchestrators; and why

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00:59:24

The AI-First Data Engineer: 10–50x Productivity and...

Data Engineering Podcast ·

2026/04/07

SummaryIn this episode, I sit down with Gleb Mezhanskiy, CEO and co-founder of Datafold, to explore how agentic AI is reshaping data engineering. We unpack the leap from chat-assisted coding to truly agentic workflows where AI not only writes SQL and dbt models but also executes queries, debugs, runs tests, and ships production-ready outcomes. Gleb explains why teams that master this AI-first loop can see 10–50x gains, how security/compliance concerns can be addressed with platform-native LLM

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00:50:19

Treat Metering Like Finance: Building Data Platforms...

Data Engineering Podcast ·

2026/03/29

SummaryIn this episode Himant Goyal, Senior Product Manager at Salesforce, talks about how data platform investments enable reliable, accurate metering for consumption-based business models. Himant explains why consumption turns operations into a real-time optimization problem spanning metering, cost attribution, billing, governance, and cross-functional ownership. He explores the richness required in usage data to support sophisticated pricing, the importance of treating metering like a financial

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00:42:40

Beyond the PDF: Rowan Cockett on Reproducible,...

Data Engineering Podcast ·

2026/03/22

SummaryIn this episode Rowan Cockett, co-founder and CEO of CurveNote and co-founder of the Continuous Science Foundation, talks about building data systems that make scientific research reproducible, reusable, and easier to communicate. He digs into the sociotechnical roots of the reproducibility crisis - from data integrity and access to entrenched publishing incentives and PDF-bound workflows. He explores open standards and tools like Jupyter, Jupyter Book, and the push toward cloud-optimized

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01:01:50

Beyond Prompts: Practical Paths to Self‑Improving AI

Data Engineering Podcast ·

2026/03/16

SummaryIn this episode Raj Shukla, CTO of SymphonyAI, explores what it really takes to build self‑improving AI systems that work in production. Raj unpacks how agentic systems interact with real-world environments, the feedback loops that enable continuous learning, and why intelligent memory layers often provide the most practical middle ground between prompt tweaks and full Reinforcement Learning. He discusses the architecture needed around models - data ingestion, sensors, action layers,

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01:05:01

Orion at Gravity: Trustworthy AI Analysts for the...

Data Engineering Podcast ·

2026/03/08

SummaryIn this episode of the Data Engineering Podcast, Lucas Thelosen and Drew Gilson, co-founders of Gravity, discuss their vision for agentic analytics in the enterprise, enabled by semantic layers and broader context engineering. They share their journey from Looker and Google to building Orion, an AI analyst that combines data semantics with rich business context to deliver trustworthy and actionable insights. Lucas and Drew explain how Orion uses governed, role-specific "custom agents" to

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00:45:02

From Models to Momentum: Uniting Architects and...

Data Engineering Podcast ·

2026/03/02

SummaryIn this episode of the Data Engineering Podcast, Jamie Knowles (Product Director) and Ryan Hirsch (Product Marketing Manager) discuss the importance of enterprise data modeling with ER/Studio. They highlight how clear, shared semantic models are a foundational discipline for modern data engineering, preventing semantic drift, speeding up delivery, and reducing rework. Jamie explains that ER/Studio helps teams define logical models that translate into physical designs and code across

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00:57:47

From Data Models to Mind Models: Designing AI Memory...

Data Engineering Podcast ·

2026/02/22

SummaryIn this episode of the Data Engineering Podcast, Vasilije "Vas" Markovich, founder of Cognee, discusses building agentic memory, a crucial aspect of artificial intelligence that enables systems to learn, adapt, and retain knowledge over time. He explains the concept of agentic memory, highlighting the importance of distinguishing between permanent and session memory, graph+vector layers, latency trade-offs, and multi-tenant isolation to ensure safe knowledge sharing or protection. The

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00:50:43

Prompt Management, Tracing, and Evals: The New Table...

Data Engineering Podcast ·

2026/02/15

SummaryIn this episode of the Data Engineering Podcast, Aman Agarwal, creator of OpenLit, discusses the operational groundwork required to run LLM-powered applications reliably and cost-effectively. He highlights common blind spots that teams face, including opaque model behavior, runaway token costs, and brittle prompt management, and explains how OpenTelemetry-native observability can turn these black-box interactions into stepwise, debuggable traces across models, tools, and data stores. Aman

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00:46:45

From Legacy to AI-Ready: How MongoDB AMP Accelerates...

Data Engineering Podcast ·

2026/02/08

SummaryIn this episode, Shilpa Kolhar, SVP of Product and Engineering at MongoDB, discusses using MongoDB as a unified foundation for AI-driven and agentic applications. She explains how the Application Modernization Platform (AMP) accelerates the transition from legacy relational systems to a document-first architecture, driven by the need for AI-readiness and speed of change. Shilpa highlights MongoDB's features, such as its native JSON document model, Atlas Vector Search, auto-embeddings, and

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00:56:53

Branches, Diffs, and SQL: How Dolt Powers Agentic...

Data Engineering Podcast ·

2026/02/01

SummaryIn this episode Tim Sehn, founder and CEO of DoltHub, talks about Dolt - the world’s first version‑controlled SQL database - and why Git‑style semantics belong at the heart of data systems and AI workflows. Tim explains how Dolt combines a MySQL/Postgres‑compatible interface with a novel storage engine built on a “Prollytree” to enable fast, row‑level branching, merging, and diffs of both schema and data. He digs into real production use cases: powering applications that expose version

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00:40:50

Logical First, Physical Second: A Pragmatic Path to...

Data Engineering Podcast ·

2026/01/25

SummaryIn this episode of the Data Engineering Podcast Jamie Knowles, Product Director for ER/Studio, talks about data architecture and its importance in driving business meaning. He discusses how data architecture should start with business meaning, not just physical schemas, and explores the pitfalls of jumping straight to physical designs. Jamie shares his practical definition of data architecture centered on shared semantic models that anchor transactional, analytical, and event-driven systems

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01:12:21

Your Data, Your Lake: How Observe Uses Iceberg and...

Data Engineering Podcast ·

2026/01/18

SummaryIn this episode Jacob Leverich, cofounder and CTO of Observe, talks about applying lakehouse architectures to observability workloads. Jacob discusses Observe’s decision to leverage cloud-native warehousing and open table formats for scale and cost efficiency. He digs into the core pain points teams face with fragmented tools, soaring costs, and data silos, and how a lakehouse approach - paired with streaming ingest via OpenTelemetry, Kafka-backed durability, curated/columnarized tables, and

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00:56:42

Semantic Operators Meet Dataframes: Building Context...

Data Engineering Podcast ·

2026/01/12

SummaryIn this episode Kostas Pardalis talks about Fenic - an open-source, PySpark-inspired dataframe engine designed to bring LLM-powered semantics into reliable data engineering workflows. Kostas shares why today’s data infrastructure assumptions (BI-first, expert-operated, CPU-bound) fall short for AI-era tasks that are increasingly inference- and IO-bound. He explores how Fenic introduces semantic operators (e.g., semantic filter, extract, join) as first-class citizens in the logical plan so the

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00:49:21

Beyond Dashboards: How Data Teams Earn a Seat at the...

Data Engineering Podcast ·

2026/01/05

SummaryIn this episode Goutham Budati about his Data–Perspective–Action framework and how it empowers data teams to become true business partners. Gautham traces his path from automating Excel reports to leading high‑impact data organizations, then breaks down why technical excellence alone isn’t enough: teams must pair reliable data systems with deliberate storytelling, clear problem framing, and concrete action plans. He digs into tactics for moving from reactive ticket-taking to proactive

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00:59:24

Unfreezing The Data Lake: The Future-Proof File Format

Data Engineering Podcast ·

2025/12/29

SummaryIn this episode PhD researcher Xinyu Zeng talks about F3, the “future-proof file format” designed to address today’s hardware realities and evolving workloads. He digs into the limitations of Parquet and ORC - especially CPU-bound decoding, metadata overhead for wide-table projections, and poor random-access behavior for ML training and serving - and how F3 rethinks layout and encodings to be efficient, interoperable, and extensible. Xinyu explains F3’s two major ideas: a decoupled, flexible

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01:06:17

From Context to Semantics: How Metadata Powers...

Data Engineering Podcast ·

2025/12/21

SummaryIn this episode Suresh Srinivas and Sriharsha Chintalapani explore how metadata platforms are evolving from human-centric catalogs into the foundational context layer for AI and agentic systems. They discuss the origins and growth of OpenMetadata and Collate, why “context” is necessary but “semantics” is critical for precise AI outcomes, and how a schema-first, API-first, unified platform enables discovery, observability, and governance in one workflow. They share how AI agents can now

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00:26:59

From Data Engineering to AI Engineering: Where the...

Data Engineering Podcast ·

2025/12/14

SummaryIn this solo episode of the Data Engineering Podcast, host Tobias Macey reflects on how AI has transformed the practice and pace of data engineering over time. Starting from its origins in the Hadoop and cloud warehouse era, he explores the discipline's evolution through ML engineering and MLOps to today's blended boundaries between data, ML, and AI engineering. The conversation covers how unstructured data is becoming more prominent, vectors and knowledge graphs are emerging as key

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512 results

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