Apache Spark 3.0 is now here, and it’s bringing a host of enhancements across its diverse range of capabilities. The headliner is an big bump in performance for the SQL engine and better coverage of ...

The Apache Spark community last week announced Spark 3.2, a significant new release of the distributed computing framework. Among the more exciting features are deeper support for the Python data ...

Spark can be deployed in a variety of ways, provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning, and graph processing.

Fastest Growing Areas in Spark (source: Databricks) "In Spark, a DataFrame is a distributed collection of data organized into named columns," the company said at the time. "It is conceptually ...

With Apache Spark Declarative Pipelines, engineers describe what their pipeline should do using SQL or Python, and Apache Spark handles the execution.