Best of Data Engineering — April 2026

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   Article

   Hacker News·5w

   Drunk Post: Things I’ve Learned as a Senior Engineer

   A preserved Reddit post from a data engineer with 10+ years of experience, written candidly after a few drinks. Covers career advice (change companies to advance, be honest with managers), technical opinions (SQL is king, best code is no code, TDD is a cult), data engineering specifics (Airflow, streaming, ML project failure rates), and life reflections. Touches on work-life balance, remote work tradeoffs, tech stack philosophy, documentation as an underrated skill, and the importance of kindness. Raw, unfiltered, and widely relatable.

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2. 2
   Article

   ByteByteGo·8w

   How Datadog Redefined Data Replication

   Datadog's Metrics Summary page suffered 7-second p90 latency due to expensive joins on 82K metrics against 817K configurations in Postgres. The root cause was using a transactional database for search workloads. The solution was Change Data Capture (CDC) using Debezium to stream Postgres WAL changes into Kafka, then into a dedicated search platform. Datadog chose asynchronous replication for resilience at scale, accepting brief replication lag as a tradeoff. To handle schema evolution safely, they built automated SQL validation and a Kafka Schema Registry enforcing backward compatibility with Avro serialization. Finally, they used Temporal to automate pipeline provisioning end-to-end, turning a one-off fix into a company-wide data replication platform supporting Postgres-to-Postgres, Postgres-to-Iceberg, Cassandra, and cross-region Kafka pipelines.

   43

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3. 3
   Article

   freeCodeCamp·6w

   Efficient Data Processing in Python: Batch vs Streaming Pipelines Explained

   A practical guide comparing batch and streaming data pipelines in Python. Covers the architectural differences, tradeoffs, and when to use each approach. Includes working Python code for both patterns using pandas for batch ETL and generator functions for streaming event processing. Also explains hybrid architectures like Lambda and Kappa for systems that need both. Key decision factors: data freshness requirements, processing complexity, and operational capacity. The recommendation is to default to batch and only adopt streaming when a concrete real-time requirement demands it.

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