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APACHE SPARK
Unified analytics engine for large-scale data processing
Apache-2.0
ABOUT
AI and data teams working with large-scale datasets often struggle with processing pipelines that need to clean, transform, and prepare data at scale for model training or inference — especially when datasets exceed what a single machine can handle. Spark provides a unified engine that processes data across clusters using familiar DataFrame and SQL APIs, with built-in MLlib for scalable machine learning and Structured Streaming for real-time data pipelines.
INSTALL
pip install pyspark
INTEGRATION GUIDE
1. Preprocess and transform terabytes of training data across clusters using PySpark DataFrames and SQL APIs
2. Build scalable feature engineering pipelines that transform raw data into ML-ready features at petabyte scale
3. Run unified batch and streaming ETL pipelines for AI data preparation with built-in fault tolerance
TAGS
big-dataanalyticsdata-processingsqlmachine-learningpythonscalastreamingetl