Data Engineering Blogs Worth Reading

Company blogs are the closest public view of production data work. Uber's engineering blog has a Data category, and its posts include one on scaling exact COUNT(DISTINCT) for high-cardinality metrics in distributed data pipelines, a problem every metrics team meets. Engineering at Meta describes its Data Infrastructure category as platforms and tools for managing data at Meta scale, from collection to analysis, and its posts there include one on migrating data ingestion systems.

LinkedIn's engineering blog groups posts under Data Management and Data Streams and Processing, among others. It has covered scaling Spark and Flink shuffle with Apache Celeborn. Spotify Engineering has Data and Data Science categories, with posts such as indexing the data lake for online point queries. Stripe's dev blog is narrower on data, but its engineering posts on running a large database fleet matter to anyone who owns a source system.

These companies run at a scale most teams never reach, so copy the reasoning, not the architecture. A design that needs a custom log store at 1 company may need a managed queue at yours. The useful part is why the simpler option failed there.

Platform blogs explain the tools you run. The Snowflake Engineering Blog has a Data Engineering category beside Core Platform and Open Source. The Databricks Blog has an Engineering category beside Platform and Research, and another for Data Strategy; its data engineering material sits under those rather than in a category of its own. The Confluent Blog tags posts for Apache Kafka and Apache Flink as well as Stream Processing, and it's where Kafka release posts land.

Sort what you read here into 2 piles. Posts that explain a mechanism, such as how a table format commits or how a consumer group rebalances, hold up for years. Launch posts and customer stories describe a product at one moment and belong to the vendor's pitch. If you interview on these tools, the mechanism posts pair well with Kafka interview questions.

Independent writers cover what company blogs skip: fundamentals and career moves, and they're candid about trade-offs. Start Data Engineering publishes on Python and SQL, pipeline design, dbt and Airflow, and Spark, and its recent posts include one on designing pipelines for fact and dimension tables. Seattle Data Guy is the blog of a consulting business, and its posts weigh tools against each other, for example the trade-offs among 5 CDC tools.

The same caveat applies in both directions. A consultant sees many stacks but writes from outside the incident, and a company engineer sees one stack from the inside. Read one of each on the same topic and note where they disagree, because that gap is usually the real decision.

A post is a design that has already survived review, which makes it good material for the system design round. Read it for 5 things: the scale, the constraint that forced a new design, the alternatives rejected, the trade-off accepted and what the authors say still hurts. Most posts state the first 3 and bury the last 2 near the end.

Then close the tab and explain the design aloud as if the interviewer had asked you to build it. Where you reach for a detail you don't have, reopen the post. That gap is what interview pressure exposes. When you want to test the explanation on a question, use data pipeline interview questions and data pipeline practice problems, and see what the system design round asks of a design.

Blogs show the projects that worked and were worth writing about. Abandoned migrations, bad on-call weeks and the cost of running the system rarely appear, so a design can look cleaner in print than it is in production. Treat each post as one team's account, not a standard.

Some well-known company blogs couldn't be opened while this page was written, so they aren't listed here rather than described from memory. For other ways to keep up, see the guides to data engineering newsletters and the data engineering community, and the data engineer roadmap for what to learn first.