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The skills a data engineer should learn — in order. From Data Modeling and SQL, through OLAP systems, dbt, and data formats, to processing engines, orchestration, Kafka, and stream processing. A practical roadmap built from real experience.
RDD, architecture, execution modes, planning, scheduling, resource allocation, memory management, cache, and joins. In short: everything about Spark.
How does Parquet organize data? Why the hybrid format? How do read/write processes work? And how does it help with OLAP workloads?
In this article, I sat down and relearned Git. It's not only about some Git commands, but also about what happens under the hood.
A completely new user would be overwhelmed by the diversity of cloud services. If you're a data engineer already overwhelmed by everything to learn, entering the Cloud without prior experience would leave you 2x as overwhelmed. Here's a vendor-agnostic guide to start.
Data architecture 101 — warehouse, lake, lakehouse, data mesh. Plus clarifications on Medallion, data modeling, and the Modern Data Stack.
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