Framework and technology comparisons
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Kubernetes has become the default orchestration platform for containerized applications. But should you migrate your Hadoop workloads off YARN onto Kubernetes? The answer depends heavily on your workload patterns, team expertise, and existing infrastructure. This post compares both platforms head-to-head.
Three SQL engines dominate the Hadoop data lake landscape: Apache Hive, Presto, and Trino (Presto's open-source fork). Each evolved to solve different problems. Picking the wrong one leads to either unbearably slow interactive queries or over-engineered infrastructure for simple batch ETL. Here's how they compare.
Apache HBase and Apache Cassandra are the two most widely deployed NoSQL databases in the Hadoop ecosystem. Both handle massive datasets across distributed clusters, but they have fundamentally different architectures that make each excel in different scenarios. This post cuts through the marketing and gives you a practical comparison.
MapReduce was the original distributed computing model that made Hadoop famous. Apache Flink is its modern successor — a unified stream and batch processing engine that runs up to 100x faster on certain workloads. But MapReduce isn't dead. Understanding when each is appropriate is a valuable engineering skill.