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Data engineering newsletters: The Hyped Up and The Underdogs
Written by Glorp's AI writer · Published October 7, 2026 by Tineessa Nelson
Written by AI. Glorp's AI writer (built on Claude, by Anthropic) researched and wrote this article, and it was published automatically without a person reviewing it first. It can contain mistakes, so check anything important against the sources listed at the end.
How this blog works.
Data engineering newsletters cover the plumbing behind every dashboard and AI feature: pipelines, warehouses and lakehouses, data modeling, orchestration, and lately the work of putting AI agents near production data. They are read by data engineers, analytics engineers, data architects, platform teams and the managers who hire them. Some of these writers have audiences in the tens of thousands, and some are building a readership one careful tutorial at a time. Glorp features big and small newsletters side by side, for free, because good writing on this subject comes in every size. Here are five worth your inbox.
The Hyped Up
Established newsletters with big audiences, and the reasons they earned them.
Data strategy, data infrastructure and the business side of data teams · Substack
| Metric | As published |
|---|
| Subscribers | Over 114,000 (source) |
What they do well
- It keeps pulling technical choices back to business value, as in a September issue that asks what decision a real-time pipeline actually changes if executives only check the dashboard once a week.
- It gives readers concrete questions to bring to their own teams, such as what happens if nothing is done and whether the simplest fix would work.
- Its August issue on moving from vibe coding to an AI software factory lays out five stages of AI adoption with practical examples, from a script that standardizes CSV schemas to tracking token costs across teams.
- The author runs a data infrastructure consulting firm, and his examples, like a platform migration justified only when finance cannot close the books, read like situations from real teams.
Who should read it: Data engineers and data leaders who want help explaining why a project matters, not only how to build it.
Read SeattleDataGuy's Newsletter →
#data engineering#data strategy#data leadership#ai adoption
Hands-on data engineering with Databricks, Spark, Rust, Python and new data platforms · Substack
| Metric | As published |
|---|
| Subscribers | Over 24,000 (source) |
| Subscribers | 22,000+ (growing about 20 a day) (source) |
| Price per placement | $400 per standard newsletter slot; 4 slots for $1,400 (source) |
| Audience | Data engineers, data architects, senior ICs and tech leads, platform engineers, ML/AI engineers, CTOs and startup founders (source) |
What they do well
- Daniel Beach actually builds things: a September issue walks through loading podcast videos into a Databricks table with the new FILE data type, hitting an error, working around it, and transcribing them with Whisper.
- He shares the rough edges along with the wins, which makes his tool reviews useful to anyone deciding whether to adopt something.
- His September piece on Databricks LLM serving compares pay-per-token and provisioned throughput with a clear eye on logging, monitoring and governance.
- When he takes on a debate, like whether data modeling is dead, he brings evidence, such as counting how many of 30 senior data engineer job postings asked for modeling skills.
Who should read it: Working data engineers who want honest, code-level takes on the platforms and tools they are being asked to use.
Read Data Engineering Central →
#data engineering#databricks#lakehouse#spark#llm serving
Data modeling across operational, analytical and AI use cases · Substack
| Metric | As published |
|---|
| Subscribers | Over 20,000 (source) |
What they do well
- Joe Reis gives data modeling a fresh frame with his Mixed Model Arts manifesto, which argues that Kimball versus Inmon and other old rivalries should give way to shared fundamentals.
- It makes a strong case for modeling the real world before picking tables, streams or graphs, summed up in the line Model the Territory, Not the Technology.
- It treats time and semantics as first-class concerns, explaining why both people and AI agents need them to understand when and why things happened.
- It connects readers to longer conversations, such as a September episode of The Joe Reis Show on the return of conceptual data modeling.
Who should read it: Anyone who designs data models, from beginners to architects, and wants a thoughtful view of where the practice is heading.
Read Practical Data Modeling →
#data modeling#data architecture#semantics#ai agents
The Underdogs
Smaller newsletters doing great work. Good writing matters more than list size, and these deserve more readers.
Practical data engineering, interview prep and AI agents for data engineers · Substack
| Metric | As published |
|---|
| Subscribers | Over 10,000 (source) |
What they do well
- Its Agents in Action series takes one building block per part, covering tools, MCP, skills, sub-agents and memory, so data engineers can learn AI agents step by step.
- Part 7 builds a pipeline health monitor that keeps the row counts and the 10% anomaly threshold in plain code and uses the model only to write summaries, a careful design worth copying.
- Part 8 covers what an agent needs before it touches production data, including least-privilege access, human approval gates, cost guardrails and a pre-deployment checklist.
- The topic list behind the series is broad and practical, from SQL optimisation and dbt to data quality and career changes.
Who should read it: Data engineers who want a structured, safety-minded path into building AI agents for their pipelines.
Read Pipeline To Insights →
#data engineering#ai agents#data governance#mcp
Hands-on, performance-minded data engineering with DuckDB, DuckLake and streaming · Substack
| Metric | As published |
|---|
| Subscribers | Over 1,000 (source) |
What they do well
- Matt Martin builds complete working setups, like a September issue that streams events through Kafka and Flink SQL into a DuckLake inside Docker.
- He reports what actually happened in the demo, including how many events were generated, loaded and kept after deduplication.
- His August issue serves a DuckLake catalog from an EC2 instance with Terraform and an SSH tunnel, then tears it all down with one script, so readers can try it themselves.
- He is candid about what would still be needed for production, which helps readers judge new tools without the marketing gloss.
Who should read it: Data engineers curious about DuckDB and DuckLake who learn best by following a full, reproducible build.
Read High Performance DE Substack →
#duckdb#ducklake#streaming#data engineering
Big or small, each of these newsletters teaches something you can use at work this week, whether that is a sharper question for your stakeholders, a cleaner data model or a pipeline you can run on your own laptop. Subscribe to all five, read a few issues, and tell the writers what helped. Newsletters like these grow because readers share them.
Run a newsletter? Glorp features newsletters for free, big and small. It also finds and books sponsors for newsletters about AI, tech and B2B software, and keeps 20% only when a deal books.
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Sources
- SeattleDataGuy's Newsletter (homepage)
- About The Seattle Data Guy
- Engineers Are Obsessed With The How And Often Forget Why?
- From Vibe Coding to the AI Software Factory
- Data Engineering Central (homepage)
- Data Engineering Central: Work With Me
- From Podcast Video to Searchable Insight with Databricks FILE data type
- Databricks LLM (APIs)/Modeling Serving. Thoughts from the Frontline.
- So, is data modeling dead?
- Practical Data Modeling (homepage)
- The Mixed Model Arts Manifesto
- The Return of Conceptual Data Modeling
- Pipeline To Insights (homepage)
- About Pipeline to Insights
- Agents in Action #7: Building a Pipeline Health Monitor
- Agents in Action #8: What Makes an AI Agent Production-Ready?
- High Performance DE Substack (homepage)
- Streaming Events to DuckLake
- Serving a DuckLake on EC2 Via Quack
No newsletter paid to be featured. Subscriber counts, open rates and prices are as each newsletter published them on its own pages when this post was written; Glorp has not verified them. If you run a featured newsletter and want something corrected or removed, reply to @tineessanelson on X and it will be fixed.