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 for graph processing, and Structured Streaming for incremental computation and stream processing.

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 for learning Spark.

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 pom.xml file that lists Spark as a dependency. Note that Spark artifacts are tagged with a …

Download Spark Built-in Libraries: SQL and DataFrames; Spark Streaming; MLlib (machine learning) GraphX (graph) Third-Party Projects

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 flexibility make it a good option for many analyses. This guide shows examples with the following Spark APIs: DataFrames; SQL; Structured Streaming; RDDs

2025年2月23日 · 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. PySpark supports all of Spark’s features such as Spark SQL, DataFrames, Structured Streaming, Machine Learning (MLlib) and Spark Core.

Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel.

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, Python and R.

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; Operations; Grouping; Plotting; Getting data in/out; Testing PySpark. Build a PySpark Application; Testing your PySpark Application; Putting It All Together!