spark

Why we included this project

Apache Spark has been the workhorse of large-scale data pipelines for over a decade, and most teams reach for it once a single machine stops being enough. You get familiar APIs in Scala, Java, and Python, plus Spark SQL for relational queries and DataFrames, so analysts and engineers can work in the same tool. Beyond batch ETL, it handles streaming workloads through Structured Streaming, and MLlib and GraphX cover distributed machine learning and graph analytics without moving data to a separate system. The connector ecosystem is large, and the documentation and community knowledge built up through years of production use at thousands of companies make it a dependable choice. For teams building data products or internal analytics, that track record is the main reason to pick Spark.