Apache Spark
Unified engine for large-scale data analytics.
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. It addresses the need to process large-scale data across batch and streaming workloads within a single unified framework.
Key capabilities include SQL and DataFrames for distributed ANSI SQL queries, Spark Streaming for real-time processing, MLlib for machine learning, GraphX for graph computation, Spark Connect, and pandas on Spark. It supports Python, SQL, Scala, Java, and R, and integrates with frameworks for data science, SQL analytics, BI, storage, and infrastructure. Adaptive Query Execution adjusts execution plans at runtime, and Spark SQL handles both structured tables and unstructured data such as JSON.
Apache Spark is an open-source project distributed under the Apache License, available for download and installation via pip or Docker. It is intended for data engineers, data scientists, and machine learning practitioners working on single machines or distributed clusters.
12 alternatives to Apache Spark
Ranked by how well each tool replaces Apache Spark: shared features, audience, price and popularity.
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Covers 1 of 13 key features.
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