Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. It uses in-memory caching, and optimized query execution for fast analytic queries against data of any size.
It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. 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.
Apache Spark is built on an advanced distributed SQL engine for large-scale data.
This is free and open source software.
Features include:
- Fast – with in-memory caching, and optimized query execution, Spark can run fast analytic queries against data of any size.
- Batch/streaming data – unify the processing of your data in batches and real-time streaming, using your preferred language: Python, SQL, Scala, Java or R.
- SQL analytics – execute fast, distributed ANSI SQL queries for dashboarding and ad-hoc reporting. Runs faster than most data warehouses.
- Data science at scale – perform Exploratory Data Analysis (EDA) on petabyte-scale data without having to resort to downsampling.
- Run multiple workloads, including interactive queries, real-time analytics, machine learning, and graph processing.
- Machine learning – train machine learning algorithms on a laptop and use the same code to scale to fault-tolerant clusters of thousands of machines.
Website: spark.apache.org
Support:
Developer: Apache Software Foundation
License: Apache License 2.0
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