Apache Spark
non-cncf
First Research Pass
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First Research
Automated Gathering
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Tech Writing
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Approval
Editorial Verified
First Research Pass Completed: Initial facts, key features, and documentation links have been gathered. Detailed technical writing and synthesis are currently in queue.
Overview
A unified multi-language engine for executing large-scale data engineering, data science, and machine learning on single-node machines or clusters.
Key Features
In-memory distributed computing delivering up to 100x speed over MapReduce.
Unified APIs in Python (PySpark), SQL, Scala, Java, and R.
Built-in libraries: Spark SQL, Structured Streaming, MLlib, and GraphX.
Pluggable resource managers including Kubernetes, YARN, and Mesos.
Use Cases
Large-scale batch ETL pipelines, streaming analytics, ML feature engineering, and data lake queries.
Getting Started
Install via `pip install pyspark` and launch interactive sessions via `pyspark`.
Recent Updates
Spark Connect client-server architecture, ANSI SQL compliance, and Kubernetes scheduling improvements.
Did You Know?
Apache Spark is widely utilized across the cloud native and open source ecosystem.