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Bookmark or Share- Examples - Apache SparkSpark 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
- Spark SQL & DataFrames - Apache SparkSeamlessly 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.
- Spark SQL and DataFrames - Spark 3.5.3 Documentation - Apache SparkSpark 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 Spark with more information about the structure of both the data and the computation being performed.
- Downloads - Apache SparkDownload Spark: Verify this release using the and project release KEYS by following these procedures . Note that Spark 3 is pre-built with Scala 2.12 in general and Spark 3.2+ provides additional pre-built distribution with Scala 2.13.
- Overview - Spark 3.5.3 Documentation - Apache SparkIt 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.
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