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.
Apache Spark is an open-source, distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries, and streaming. Spark can run on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, and S3.
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Website Key Features
Speed
Spark runs programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.
Ease of Use
Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells.
Generality
Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.
Runs Everywhere
Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, and S3.
Advanced Analytics
Spark supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.
Additional information
Community
Apache Spark has a large and active community that contributes to its development and provides support through various channels.
Integration
Spark integrates seamlessly with a wide range of data sources and other big data tools, making it a versatile choice for data processing tasks.
Scalability
Designed to scale from a single server to thousands of machines, Spark is capable of handling data at the petabyte scale.
Fault Tolerance
Spark provides fault tolerance through its resilient distributed datasets (RDDs), which allow it to recover data automatically in case of a failure.
Ecosystem
The Spark ecosystem includes a variety of tools and libraries for different data processing needs, making it a comprehensive solution for big data challenges.
HTTP headers
Security headers report is a very important part of user data protection. Learn more about http headers for spark.apache.org