Podcasts
Data Engineering Podcast
@dataengineeringpodcastTracked since Jul 2026
creating a podcast about modern data infrastructure.
Observed Aug 21, 2026 · staleModeled revenue
Paid members
0
growth unavailable
Total audience
22
incl. free followers
Estimated monthly gross
$0
Starting price
$3/mo
3 membership tiers
Pricing and benefits captured publicly
Membership tiersPatreon is not exposing patron counts by tier for this creator, so this section shows public prices and benefits only.
$0/moFreePublic prices and benefits only
$3/moAdvance WarningGet advanced warning of the topics for upcoming episodes so that you can suggest questions to ask during the episode.
$5/moFace TImeIf you patronize the show at this level then you get a 30 minute one-on-one call with the host Tobias Macey!
Growth signals: Data Engineering Podcast
Unavailable
Membership0 paid members · 30-day rate from 13 observed days
010
Jul 20268 weeks trackedNow
Data Engineering Podcast0 members · $0/mo
The chart only uses Data Engineering Podcast's observed public history. Comparison peers are listed below, but comparable trend series are not available yet.
Where Data Engineering Podcast ranks
Creator comparisonsCompare within
#1of 6 measured
Paid members in Recommended cohort
Semantic matches are not ready, so this fixed cohort uses the best available category, audience, pricing, and cadence evidence.
Ranking data · includes stale rows| Rank | Creator and why included | Paid members | Gap vs Data Engineering Podcast | Freshness |
|---|---|---|---|---|
| #1 |  Dan BehrendtPodcastsCategory, audience, pricing & cadence | 0 | Same value | Aug 21, 2026 |
| #1 |  DASSLASHERPodcastsCategory, audience, pricing & cadence | 0 | Same value | Aug 21, 2026 |
| #1 |  Data Engineering PodcastProfilePodcastsPaid members position in Recommended cohort | 0 | Reference | Aug 21, 2026Stale |
| #1 |  DazzPodcastsCategory, audience, pricing & cadence | 0 | Same value | Aug 21, 2026 |
| #1 |  Dead Mothers CollectivePodcastsCategory, audience, pricing & cadence | 0 | Same value | Aug 21, 2026 |
The rank uses the full fixed cohort. This table shows up to five creators around Data Engineering Podcast; the coverage summary above still counts everyone.
Recent posts
What they publish141Profile posts0 postsLast month0%Members-only
Previews available for 20 of 141 posts
Article Latest postPublicMetadata Management And Integration At LinkedIn With DataHub - Episode 147Sep 30, 2020 View post In order to scale the use of data across an organization there are a number of challenges related to discovery, governance, and integration that need to be solved. The key to those solutions is a robust and flexible metadata management system. LinkedIn has gone through several iterations on the most maintainable and scalable approach to metadata, leading them to their current work on DataHub. In this episode Mars Lan and Pardhu Gunnam explain how they designed the platform, how it integrates into their data platforms, and how it is being used to power data discovery and analytics at LinkedIn.
Sep 30, 2020 0 likes 0 commentsExploring The TileDB Universal Data Engine - Episode 146Sep 30, 2020ArticleSep 30, 2020Public 0 0Closing The Loop On Event Data Collection With Iteratively - Episode 145Sep 30, 2020ArticleSep 30, 2020Public 0 0A Practical Introduction To Graph Data Applications - Episode 144Sep 30, 2020 View post Finding connections between data and the entities that they represent is a complex problem. Graph data models and the applications built on top of them are perfect for representing relationships and finding emergent structures in your information. In this episode Denise Gosnell and Matthias Broecheler discuss their recent book, the Practitioner’s Guide To Graph Data, including the fundamental principles that you need to know about graph structures, the current state of graph support in database engines, tooling, and query languages, as well as useful tips on potential pitfalls when putting them into proArticleSep 30, 2020Public 0 0Build More Reliable Distributed Systems By Breaking Them With Jepsen - Episode 143Sep 30, 2020 View post A majority of the scalable data processing platforms that we rely on are built as distributed systems. This brings with it a vast number of subtle ways that errors can creep in. Kyle Kingsbury created the Jepsen framework for testing the guarantees of distributed data processing systems and identifying when and why they break. In this episode he shares his approach to testing complex systems, the common challenges that are faced by engineers who build them, and why it is important to understand their limitations. This was a great look at some of the underlying principles that power yourArticleSep 30, 2020Public 0 0Making Wind Energy More Efficient With Data At Turbit Systems - Episode 142Sep 30, 2020 View post Wind energy is an important component of an ecologically friendly power system, but there are a number of variables that can affect the overall efficiency of the turbines. Michael Tegtmeier founded Turbit Systems to help operators of wind farms identify and correct problems that contribute to suboptimal power outputs. In this episode he shares the story of how he got started working with wind energy, the system that he has built to collect data from the individual turbines, and how he is using machine learning to provide valuable insights to produce higher energy outputs. This was a great convArticleSep 30, 2020Public 0 0Open Source Production Grade Data Integration With Meltano - Episode 141Sep 30, 2020 View post The first stage of every data pipeline is extracting the information from source systems. There are a number of platforms for managing data integration, but there is a notable lack of a robust and easy to use open source option. The Meltano project is aiming to provide a solution to that situation. In this episode, project lead Douwe Maan shares the history of how Meltano got started, the motivation for the recent shift in focus, and how it is implemented. The Singer ecosystem has laid the groundwork for a great option to empower teams of all sizes to unlock the value of their Data and Meltano isArticleSep 30, 2020Public 0 0Building A Data Lake For The Database Administrator At Upsolver - Episode 135Sep 1, 2020ArticleSep 1, 2020Public 0 0Data Management Trends From An Investor Perspective - Episode 136Sep 1, 2020 View post The landscape of data management and processing is rapidly changing and evolving. There are certain foundational elements that have remained steady, but as the industry matures new trends emerge and gain prominence. In this episode Astasia Myers of Redpoint Ventures shares her perspective as an investor on which categories she is paying particular attention to for the near to medium term. She discusses the work being done to address challenges in the areas of data quality, observability, discovery, and streaming. This is a useful conversation to gain a macro perspective on where businesses are looking toArticleSep 1, 2020Public 0 0Accelerate Your Machine Learning With The StreamSQL Feature Store - Episode 137Sep 1, 2020 View post Machine learning is a process driven by iteration and experimentation which requires fast and easy access to relevant features of the data being processed. In order to reduce friction in the process of developing and delivering models there has been a recent trend toward building a dedicated feature. In this episode Simba Khadder discusses his work at StreamSQL building a feature store to make creation, discovery, and monitoring of features fast and easy to manage. He describes the architecture of the system, the benefits of streaming data for machine learning, and how a feature store proviArticleSep 1, 2020Public 0 0Bringing Business Analytics To End Users With GoodData - Episode 138Sep 1, 2020 View post The majority of analytics platforms are focused on use internal to an organization by business stakeholders. As the availability of data increases and overall literacy in how to interpret it and take action improves there is a growing need to bring business intelligence use cases to a broader audience. GoodData is a platform focused on simplifying the work of bringing data to employees and end users. In this episode Sheila Jung and Philip Farr discuss how the GoodData platform is being used, how it is architected to provide scalable and performant analytics, and how it integrates into customer’s dataArticleSep 1, 2020Public 0 0Data Collection And Management For Teaching Machines To Hear At Audio Analytic - Episode 139Sep 1, 2020ArticleSep 1, 2020Public 0 0
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