Apache DataFu

Apache DataFu is a suite of libraries designed for large-scale data processing in the Hadoop ecosystem. It provides robust, well-tested utilities for data mining, statistics, and incremental data processing. The project includes three main components: DataFu Pig, DataFu Spark, and DataFu Hourglass, each tailored to specific data processing needs.

Features

DataFu Pig

• User-defined functions (UDFs) and macros for Apache Pig
• Functions for statistics, sessionization, link analysis, set operations, and more
• Included in Cloudera CDH and Apache Bigtop distributions
• Used in production at LinkedIn since 2010

DataFu Spark

• Utilities and UDFs for Apache Spark
• Deduplication with ordering (e.g., keeping the most recent record)
• Skewed joins for large datasets
• Efficient distinct counting
• Cross-language support: call Python from Scala and vice versa
• Used in production at PayPal since 2017

DataFu Hourglass

• Incremental processing framework for Hadoop MapReduce
• Optimized for sliding window computations (e.g., daily/weekly tracking)
• Reduces redundant computation, saving 50–95% in resources
• Used in production at LinkedIn

Capabilities

• Scalable Data Processing: Handles large-scale datasets efficiently across Hadoop, Pig, and Spark environments.
• Incremental Computation: Hourglass enables efficient sliding window analytics without reprocessing entire datasets.
• Advanced Analytics: Provides statistical functions, sessionization, link analysis (e.g., PageRank), and more.
• Cross-Platform Integration: Supports integration with Spark and Pig, and allows interoperability between Scala and Python.
• Production-Ready: All libraries are unit-tested and proven in enterprise environments like LinkedIn and PayPal.

Benefits

• Efficiency: Reduces computational overhead through incremental processing and optimized joins.
• Flexibility: Offers a wide range of functions for different analytical needs across multiple platforms.
• Reliability: Built with stability and testing in mind, ensuring consistent performance in production.
• Open Source: Freely available under the Apache 2.0 license, with active community contributions.
• Enterprise Proven: Trusted by major companies for mission-critical data workflows.