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Artwork for Data Engineering Podcast

Data Engineering Podcast

Tobias Macey
Data Engineering
Machine Learning
Data Quality
Data Management
Data Governance
Data Migration
Generative AI
Data Architecture
Datafold
Cloud Computing
Data Integration
Artificial Intelligence
Kafka
Data Lakes
Data Warehousing
Data Modeling
AI Systems
Software Engineering
DBT
Business Intelligence

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.

PublishesTwice monthlyEpisodes516Founded10 years ago
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Artwork for Data Engineering Podcast

Latest Episodes

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 workf... more

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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... more

YouTube

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 product... more

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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 u... more

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Recent Guests

Ragnor Comerford
Creator/Creator of OmniGraph, focused on lakehouse-native graph storage with Git semantics
OmniGraph (organization)
Episode: Why Multi-Agent Systems Need Shared State, Graph Semantics, and Governance
Prakulpa Sankar
Founder of Atlan
Atlan
Episode: Building the Context Flywheel for AI Data Agents
Jevin Maltais
Fractional CTO, staff engineer type; led data/ETL at multiple companies
TypeStream
Episode: Holding Kafka Right: Product-Friendly Streaming with TypeStream
Shravan Gunda
CEO and founder of Kaarvi AI
Kaarvi AI
Episode: Text to Data Products: Kaarvi’s End-to-End AI for Ingestion, Quality, and Dashboards
Weimo Liu
Co-founder of PuppyGraph, expert in graph analytics and zero-copy ETL
PuppyGraph
Episode: Scaling Graph Analytics Without ETL: Inside PuppyGraph’s Architecture
Robert Nishihara
Co-founder of Anyscale, co-creator of Ray
Anyscale
Episode: Maximizing GPU Utilization: Heterogeneous Pipelines with Ray and Kubernetes
Himant Goyal
Senior Product Manager at Salesforce
Salesforce
Episode: Treat Metering Like Finance: Building Data Platforms for Consumption Economics
Rowan Cockett
CEO and co-founder of CurveNote; co-founder of Continuous Science Foundation
CurveNote; Continuous Science Foundation
Episode: Beyond the PDF: Rowan Cockett on Reproducible, Composable Science
Raj Shukla
CTO at SymphonyAI; vertical AI company focusing on domain-specific AI agents and models
SymphonyAI
Episode: Beyond Prompts: Practical Paths to Self‑Improving AI

Host

Tobias Macey
Host of Data Engineering Podcast; leads discussions on data engineering topics and tool ecosystems.

Reviews

4.7 out of 5 stars from 398 ratings
  • All of the episodes are promotions of tools

    No one absolutely no one goes in depth of anything in this podcast, everyone only come and talk about their tools.

    Apple Podcasts
    2
    masterbatters
    United Statesa year ago
  • Great guests and topics

    Always worth listening to understand what problems people are dealing with and considering how I can apply these lessons to my data engineering.

    Apple Podcasts
    5
    Mbarton98
    United States2 years ago
  • Azure

    I really enjoy this podcast and learn a lot from it. I wonder why none of data tools in Azure is never mentioned.

    Thanks

    Apple Podcasts
    4
    Fkn2013
    United States3 years ago
  • Very insightful podcast!

    When you are looking for more knowledge and clarity in the world of data engineering/management, then you have come to the right place.

    Apple Podcasts
    5
    More Than Engineering
    Philippines4 years ago
  • Interesting topics guests

    Tobias does a great job covering the future of data engineering - practical tips, the future of the industry with the founders of new tools, and no-nonsense advice on how to build data pipelines, viz, and process that will scale.

    Apple Podcasts
    5
    Googleduser
    United States4 years ago

Listeners Say

Key themes from listener reviews, highlighting what works and what could be improved about the show.

The show is a strong resource for understanding current tool landscapes and architectural patterns.
Listeners praise the depth and practical insights for data pipelines and governance.
Some listeners note advertising or tool promotions but still find value in the technical content.
Audience feedback often highlights actionable takeaways for data engineering teams.
Guests are consistently high-caliber and the conversations stay technically substantive.

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#235
Canada/Technology
Apple Podcasts
#48
Italy/Technology
Apple Podcasts
#58
India/Technology
Apple Podcasts
#89
Israel/Technology
Apple Podcasts
#133
Belgium/Technology
Apple Podcasts
#214
Singapore/Technology

Talking Points

Recent interactions between the hosts and their guests.

Why Multi-Agent Systems Need Shared State, Graph Semantics, and Governance
Q: Can you start by introducing yourself and your work on OmniGraph?
The guest describes his background in ML and life sciences, leading to OmniGraph's creation as a Lakehouse-native graph engine built on Lens, designed to support multi-agent workflows with governance and a shared world model.
Holding Kafka Right: Product-Friendly Streaming with TypeStream
Q: Who benefits most from TypeStream in an organization?
People already familiar with Kafka and Kafka Streams benefit most, especially enablement roles helping teams move faster, product engineers who want to avoid building many microservices, and smaller teams needing a clear on-ramp yet scalable architecture.
Holding Kafka Right: Product-Friendly Streaming with TypeStream
Q: What are the main tradeoffs when choosing Kafka-native approaches vs. alternatives like Kinesis or Pulsar?
Kafka-native approaches typically offer lowest latency and tight integration with Kafka tooling, but require more operational overhead. Alternatives like Kinesis or Pulsar can reduce ops burden and provide different guarantees, but may introduce vendor lock-in or require additional adapters. TypeStream is designed to be portable across distributions, easing swaps while preserving core capabilities.
Maximizing GPU Utilization: Heterogeneous Pipelines with Ray and Kubernetes
Q: What are the biggest challenges in maximizing hardware utilization for AI workloads?
Discussion of resource sharing between training and inference, multi-cloud capacity, elasticity, rack-level topology, and failure handling, with Ray as a coordinating layer across compute resources.
Maximizing GPU Utilization: Heterogeneous Pipelines with Ray and Kubernetes
Q: How has the Ray ecosystem evolved with Kubernetes and the broader compute stack?
The guest discusses the maturation of Kubernetes as the standard for orchestration, consolidation of the infrastructure stack, and Ray's integration with PyTorch, Kubernetes, and other tools to support diverse workloads.

Audience Metrics

Listeners, social reach, demographics and more for this podcast.

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Frequently Asked Questions About Data Engineering Podcast

What is Data Engineering Podcast about and what kind of topics does it cover?

A technically oriented program that centers on data engineering tooling, architectures, and best practices. Episodes frequently explore topics like data pipelines, streaming, governance, metadata, and AI-infused data platforms, with guests ranging from startup founders to platform leaders and researchers. The show tends to favor deep-dive conversations about real-world systems, scale, and the trade-offs of different data architectures, often highlighting practical patterns, tooling comparisons, and governance considerations. Noteworthy traits include a strong focus on cutting-edge approaches (e.g., lakehouse, agent-based workflows, reproducible science) and a clear emphasis on actionable insights for practitioners responsible for building r... more

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Which podcasts are similar to Data Engineering Podcast?

These podcasts share a similar audience with Data Engineering Podcast:

1. Talk Python To Me
2. Super Data Science: ML & AI Podcast with Jon Krohn
3. DataFramed
4. Software Engineering Daily
5. Software Engineering Radio - the podcast for professional software developers

How many episodes of Data Engineering Podcast are there?

Data Engineering Podcast launched 10 years ago and published 516 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on Data Engineering Podcast?

Recent guests on Data Engineering Podcast include:

1. Ragnor Comerford
2. Prakulpa Sankar
3. Jevin Maltais
4. Shravan Gunda
5. Weimo Liu
6. Robert Nishihara
7. Himant Goyal
8. Rowan Cockett

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