Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.

It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX …

The documentation linked to above covers getting started with Spark, as well the built-in components MLlib, Spark Streaming, and GraphX. In addition, this page lists other resources …

Unlike the earlier examples with the Spark shell, which initializes its own SparkSession, we initialize a SparkSession as part of the program. To build the program, we also write a Maven …

Spark is a great engine for small and large datasets. It can be used with single-node/localhost environments, or distributed clusters. Spark’s expansive API, excellent performance, and …

Feb 23, 2025 · PySpark combines Python’s learnability and ease of use with the power of Apache Spark to enable processing and analysis of data at any size for everyone familiar with Python. …

Seamlessly mix SQL queries with Spark programs. Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar DataFrame API. Usable in Java, Scala, …

Spark SQL, DataFrames and Datasets Guide. Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide …

Quickstart: Spark Connect. Launch Spark server with Spark Connect; Connect to Spark Connect server; Create DataFrame; Quickstart: Pandas API on Spark. Object Creation; Missing Data; …

The spark-submit script in Spark’s bin directory is used to launch applications on a cluster. It can use all of Spark’s supported cluster managers through a uniform interface so you don’t have to …