Mapreduce
Showing 18 of 18 projects
A distributed database for high-performance computing with in-memory speed, ACID compliance, and ANSI SQL support.
A Scala API for Cascading that simplifies writing Hadoop MapReduce jobs with Scala integration.
A distributed computation system written in Go for parallel and cluster processing, similar to Hadoop MapReduce and Spark.
A library for writing MapReduce programs that execute on distributed platforms like Storm and Scalding using Scala/Java collection-like syntax.
Native integration library for using Elasticsearch with Hadoop, Spark, and Hive for real-time search and analytics on big data.
A library enabling MongoDB to serve as input source or output destination for Hadoop MapReduce tasks and ecosystem tools.
A collection of R packages for interacting with Hadoop ecosystems, enabling big data analysis from R.
A Clojure DSL for Apache Spark that enables distributed data processing using idiomatic Clojure.
A Python wrapper for Cascading that enables building and controlling Hadoop data processing workflows entirely in Python.
A Hadoop library for reading and processing packet capture (PCAP) files in MapReduce jobs and Hive queries.
A Groovy wrapper for the MongoDB Java driver providing a simpler, less verbose API.
A Haskell driver for MongoDB that enables database connections, queries, updates, and administrative tasks.
A MapReduce-style framework for processing fast/streaming data, implementing the MapUpdate model.
A collection of libraries for large-scale data processing in Hadoop ecosystems, including Spark, Pig, and incremental MapReduce.
A collection of interactive Jupyter notebooks for learning Hadoop, Spark, and MapReduce with hands-on tutorials and demos.
A production-grade HBase ORM library for clean, fast, and fun object-oriented data access, also compatible with Google Cloud Bigtable.
Mozilla's utility library for Hadoop, HBase, Pig, and related big data technologies.
Code samples demonstrating how to use popular applications on Amazon Elastic MapReduce (EMR).
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