Python was already the most popular language in Spark before the latest batch of improvements (and Databricks and the Apache Spark community aren’t done). So it’s interesting to note the level of ...

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 ...

Spark also provides many language choices, including Scala, Java, Python, and R. The 2015 Spark Survey that polled the Spark community shows particularly rapid growth in Python and R.

But Spark has also had its share of impedance mismatch issues, such as making R and Python programs first-class citizens, or adapting to more compute-intensive processing of AI models.

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.

It includes the latest updates on new features from the Apache Spark 3.0 release, to help you: Learn the Python, SQL, Scala, or Java high-level APIs: DataFrames and Datasets.