[
](https://spark.apache.org/)
# Awesome Spark [](https://github.com/sindresorhus/awesome)
A curated list of awesome [Apache Spark](https://spark.apache.org/) packages and resources.
_Apache Spark is an open-source cluster-computing framework. Originally developed at the [University of California](https://www.universityofcalifornia.edu/), [Berkeley's AMPLab](https://amplab.cs.berkeley.edu/), the Spark codebase was later donated to the [Apache Software Foundation](https://www.apache.org/), which has maintained it since. Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance_ ([Wikipedia 2017](#wikipedia-2017)).
Users of Apache Spark may choose between different the Python, R, Scala and Java programming languages to interface with the Apache Spark APIs.
## Contents
- [Packages](#packages)
- [Language Bindings](#language-bindings)
- [Notebooks and IDEs](#notebooks-and-ides)
- [General Purpose Libraries](#general-purpose-libraries)
- [SQL Data Sources](#sql-data-sources)
- [Storage](#storage)
- [Bioinformatics](#bioinformatics)
- [GIS](#gis)
- [Time Series Analytics](#time-series-analytics)
- [Graph Processing](#graph-processing)
- [Machine Learning Extension](#machine-learning-extension)
- [Middleware](#middleware)
- [Utilities](#utilities)
- [Natural Language Processing](#natural-language-processing)
- [Streaming](#streaming)
- [Interfaces](#interfaces)
- [Testing](#testing)
- [Web Archives](#web-archives)
- [Workflow Management](#workflow-management)
- [Resources](#resources)
- [Books](#books)
- [Papers](#papers)
- [MOOCS](#moocs)
- [Workshops](#workshops)
- [Projects Using Spark](#projects-using-spark)
- [Docker Images](#docker-images)
- [Miscellaneous](#miscellaneous)
## Packages
### Language Bindings
* [Flambo](https://github.com/yieldbot/flambo) 
- Clojure DSL.
* [Mobius](https://github.com/Microsoft/Mobius) 
- C# bindings (Deprecated in favor of .NET for Apache Spark).
* [.NET for Apache Spark](https://github.com/dotnet/spark) 
- .NET bindings.
* [sparklyr](https://github.com/rstudio/sparklyr) 
- An alternative R backend, using [`dplyr`](https://github.com/hadley/dplyr).
* [sparkle](https://github.com/tweag/sparkle) 
- Haskell on Apache Spark.
### Notebooks and IDEs
* [almond](https://almond.sh/) 
- A scala kernel for [Jupyter](https://jupyter.org/).
* [Apache Zeppelin](https://zeppelin.incubator.apache.org/) 
- Web-based notebook that enables interactive data analytics with plugable backends, integrated plotting, and extensive Spark support out-of-the-box.
* [Polynote](https://polynote.org/) 
- Polynote: an IDE-inspired polyglot notebook. It supports mixing multiple languages in one notebook, and sharing data between them seamlessly. It encourages reproducible notebooks with its immutable data model. Originating from [Netflix](https://medium.com/netflix-techblog/open-sourcing-polynote-an-ide-inspired-polyglot-notebook-7f929d3f447).
* [Spark Notebook](https://github.com/andypetrella/spark-notebook) 
- Scalable and stable Scala and Spark focused notebook bridging the gap between JVM and Data Scientists (incl. extendable, typesafe and reactive charts).
* [sparkmagic](https://github.com/jupyter-incubator/sparkmagic) 
- [Jupyter](https://jupyter.org/) magics and kernels for working with remote Spark clusters, for interactively working with remote Spark clusters through [Livy](https://github.com/cloudera/livy), in Jupyter notebooks.
### General Purpose Libraries
* [Succinct](http://succinct.cs.berkeley.edu/) 
- Support for efficient queries on compressed data.
* [itachi](https://github.com/yaooqinn/itachi) 
- A library that brings useful functions from modern database management systems to Apache Spark.
* [spark-daria](https://github.com/mrpowers/spark-daria) 
- A Scala library with essential Spark functions and extensions to make you more productive.
* [quinn](https://github.com/mrpowers/quinn) 
- A native PySpark implementation of spark-daria.
* [Apache DataFu](https://github.com/apache/datafu/tree/master/datafu-spark) 
- A library of general purpose functions and UDF's.
* [Joblib Apache Spark Backend](https://github.com/joblib/joblib-spark) 
- [`joblib`](https://github.com/joblib/joblib) backend for running tasks on Spark clusters.
### SQL Data Sources
SparkSQL has [serveral built-in Data Sources](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html#manually-specifying-options) for files. These include `csv`, `json`, `parquet`, `orc`, and `avro`. It also supports JDBC databases as well as Apache Hive. Additional data sources can be added by including the packages listed below, or writing your own.
* [Spark CSV](https://github.com/databricks/spark-csv) 
- CSV reader and writer (obsolete since Spark 2.0 [[SPARK-12833]](https://issues.apache.org/jira/browse/SPARK-12833)).
* [Spark Avro](https://github.com/databricks/spark-avro) 
- [Apache Avro](https://avro.apache.org/) reader and writer (obselete since Spark 2.4 [[SPARK-24768]](https://issues.apache.org/jira/browse/SPARK-24768)).
* [Spark XML](https://github.com/databricks/spark-xml) 
- XML parser and writer.
* [Spark Cassandra Connector](https://github.com/datastax/spark-cassandra-connector) 
- Cassandra support including data source and API and support for arbitrary queries.
* [Spark Riak Connector](https://github.com/basho/spark-riak-connector) 
- Riak TS & Riak KV connector.
* [Mongo-Spark](https://github.com/mongodb/mongo-spark) 
- Official MongoDB connector.
* [OrientDB-Spark](https://github.com/orientechnologies/spark-orientdb) 
- Official OrientDB connector.
### Storage
* [Delta Lake](https://github.com/delta-io/delta) 
- Storage layer with ACID transactions.
* [lakeFS](https://docs.lakefs.io/integrations/spark.html) 
- Integration with the lakeFS atomic versioned storage layer.
### Bioinformatics
* [ADAM](https://github.com/bigdatagenomics/adam) 
- Set of tools designed to analyse genomics data.
* [Hail](https://github.com/hail-is/hail) 
- Genetic analysis framework.
### GIS
* [Magellan](https://github.com/harsha2010/magellan) 
- Geospatial analytics using Spark.
* [Apache Sedona](https://github.com/apache/incubator-sedona) 
- Cluster computing system for processing large-scale spatial data.
### Time Series Analytics
* [Spark-Timeseries](https://github.com/cloudera/spark-timeseries) 
- Scala / Java / Python library for interacting with time series data on Apache Spark.
* [flint](https://github.com/twosigma/flint) 
- A time series library for Apache Spark.
### Graph Processing
* [Mazerunner](https://github.com/neo4j-contrib/neo4j-mazerunner) 
- Graph analytics platform on top of Neo4j and GraphX.
* [GraphFrames](https://github.com/graphframes/graphframes) 
- Data frame based graph API.
* [neo4j-spark-connector](https://github.com/neo4j-contrib/neo4j-spark-connector) 
- Bolt protocol based, Neo4j Connector with RDD, DataFrame and GraphX / GraphFrames support.
* [SparklingGraph](http://sparkling.ml) 
- Library extending GraphX features with multiple functionalities useful in graph analytics (measures, generators, link prediction etc.).
### Machine Learning Extension
* [Clustering4Ever](https://github.com/Clustering4Ever/Clustering4Ever) 
Scala and Spark API to benchmark and analyse clustering algorithms on any vectorization you can generate.
* [dbscan-on-spark](https://github.com/irvingc/dbscan-on-spark) 
- An Implementation of the DBSCAN clustering algorithm on top of Apache Spark by [irvingc](https://github.com/irvingc) and based on the paper from He, Yaobin, et al. [MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data](https://www.researchgate.net/profile/Yaobin_He/publication/260523383_MR-DBSCAN_a_scalable_MapReduce-based_DBSCAN_algorithm_for_heavily_skewed_data/links/0046353a1763ee2bdf000000.pdf).
* [Apache SystemML](https://systemml.apache.org/) 
- Declarative machine learning framework on top of Spark.
* [Mahout Spark Bindings](https://mahout.apache.org/users/sparkbindings/home.html) \[status unknown\] - linear algebra DSL and optimizer with R-like syntax.
* [spark-sklearn](https://github.com/databricks/spark-sklearn) 
- Scikit-learn integration with distributed model training.
* [KeystoneML](http://keystone-ml.org/) - Type safe machine learning pipelines with RDDs.
* [JPMML-Spark](https://github.com/jpmml/jpmml-spark) 
- PMML transformer library for Spark ML.
* [Distributed Keras](https://github.com/cerndb/dist-keras) 
- Distributed deep learning framework with PySpark and Keras.
* [ModelDB](https://mitdbg.github.io/modeldb) 
- A system to manage machine learning models for `spark.ml` and [`scikit-learn`](https://github.com/scikit-learn/scikit-learn) 
.
* [Sparkling Water](https://github.com/h2oai/sparkling-water) 
- [H2O](http://www.h2o.ai/) interoperability layer.
* [BigDL](https://github.com/intel-analytics/BigDL) 
- Distributed Deep Learning library.
* [MLeap](https://github.com/combust/mleap) 
- Execution engine and serialization format which supports deployment of `o.a.s.ml` models without dependency on `SparkSession`.
* [Microsoft ML for Apache Spark](https://github.com/Azure/mmlspark) 
- A distributed ml library with support for LightGBM, Vowpal Wabbit, OpenCV, Deep Learning, Cognitive Services, and Model Deployment.
* [MLflow](https://mlflow.org/docs/latest/python_api/mlflow.spark.html#module-mlflow.spark) 
- Machine learning orchestration platform.
### Middleware
* [Livy](https://github.com/apache/incubator-livy) 
- REST server with extensive language support (Python, R, Scala), ability to maintain interactive sessions and object sharing.
* [spark-jobserver](https://github.com/spark-jobserver/spark-jobserver) 
- Simple Spark as a Service which supports objects sharing using so called named objects. JVM only.
* [Mist](https://github.com/Hydrospheredata/mist) 
- Service for exposing Spark analytical jobs and machine learning models as realtime, batch or reactive web services.
* [Apache Toree](https://github.com/apache/incubator-toree) 
- IPython protocol based middleware for interactive applications.
* [Apache Kyuubi](https://github.com/apache/incubator-kyuubi) 
- A distributed multi-tenant JDBC server for large-scale data processing and analytics, built on top of Apache Spark.
### Monitoring
* [Data Mechanics Delight](https://github.com/datamechanics/delight) 
- Cross-platform monitoring tool (Spark UI / Spark History Server replacement).
### Utilities
* [silex](https://github.com/willb/silex) 
- Collection of tools varying from ML extensions to additional RDD methods.
* [sparkly](https://github.com/Tubular/sparkly) 
- Helpers & syntactic sugar for PySpark.
* [pyspark-stubs](https://github.com/zero323/pyspark-stubs) 
- Static type annotations for PySpark (obsolete since Spark 3.1. See [SPARK-32681](https://issues.apache.org/jira/browse/SPARK-32681)).
* [Flintrock](https://github.com/nchammas/flintrock) 
- A command-line tool for launching Spark clusters on EC2.
* [Optimus](https://github.com/ironmussa/Optimus/) 
- Data Cleansing and Exploration utilities with the goal of simplifying data cleaning.
### Natural Language Processing
* [spark-corenlp](https://github.com/databricks/spark-corenlp) 
- DataFrame wrapper for [Stanford CoreNLP](https://stanfordnlp.github.io/CoreNLP/).
* [spark-nlp](https://github.com/JohnSnowLabs/spark-nlp) 
- Natural language processing library built on top of Apache Spark ML.
### Streaming
* [Apache Bahir](https://bahir.apache.org/) 
- Collection of the streaming connectors excluded from Spark 2.0 (Akka, MQTT, Twitter. ZeroMQ).
### Interfaces
* [Apache Beam](https://beam.apache.org/) 
- Unified data processing engine supporting both batch and streaming applications. Apache Spark is one of the supported execution environments.
* [Blaze](https://github.com/blaze/blaze) 
- Interface for querying larger than memory datasets using Pandas-like syntax. It supports both Spark `DataFrames` and `RDDs`.
* [Koalas](https://github.com/databricks/koalas) 
- Pandas DataFrame API on top of Apache Spark.
### Testing
* [deequ](https://github.com/awslabs/deequ) 
- Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
* [spark-testing-base](https://github.com/holdenk/spark-testing-base) 
- Collection of base test classes.
* [spark-fast-tests](https://github.com/MrPowers/spark-fast-tests) 
- A lightweight and fast testing framework.
### Web Archives
* [Archives Unleashed Toolkit](https://github.com/archivesunleashed/aut) 
- Open-source toolkit for analyzing web archives.
### Workflow Management
* [Cromwell](https://github.com/broadinstitute/cromwell#spark-backend) 
- Workflow management system with [Spark backend](https://github.com/broadinstitute/cromwell#spark-backend).
## Resources
### Books
* [Learning Spark, 2nd Edition](https://www.oreilly.com/library/view/learning-spark-2nd/9781492050032/) - Introduction to Spark API with Spark 3.0 covered. Good source of knowledge about basic concepts.
* [Advanced Analytics with Spark](http://shop.oreilly.com/product/0636920035091.do) - Useful collection of Spark processing patterns. Accompanying GitHub repository: [sryza/aas](https://github.com/sryza/aas).
* [Mastering Apache Spark](https://jaceklaskowski.gitbooks.io/mastering-apache-spark/) - Interesting compilation of notes by [Jacek Laskowski](https://github.com/jaceklaskowski). Focused on different aspects of Spark internals.
* [Spark Gotchas](https://github.com/awesome-spark/spark-gotchas) - Subjective compilation of tips, tricks and common programming mistakes.
* [Spark in Action](https://www.manning.com/books/spark-in-action) - New book in the Manning's "in action" family with +400 pages. Starts gently, step-by-step and covers large number of topics. Free excerpt on how to [setup Eclipse for Spark application development](http://freecontent.manning.com/how-to-start-developing-spark-applications-in-eclipse/) and how to bootstrap a new application using the provided Maven Archetype. You can find the accompanying GitHub repo [here](https://github.com/spark-in-action/first-edition).
### Papers
* [Large-Scale Intelligent Microservices](https://arxiv.org/pdf/2009.08044.pdf) - Microsoft paper that presents an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives.
* [Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing](https://people.csail.mit.edu/matei/papers/2012/nsdi_spark.pdf) - Paper introducing a core distributed memory abstraction.
* [Spark SQL: Relational Data Processing in Spark](https://amplab.cs.berkeley.edu/wp-content/uploads/2015/03/SparkSQLSigmod2015.pdf) - Paper introducing relational underpinnings, code generation and Catalyst optimizer.
* [Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark](https://cs.stanford.edu/~matei/papers/2018/sigmod_structured_streaming.pdf) - Structured Streaming is a new high-level streaming API, it is a declarative API based on automatically incrementalizing a static relational query.
### MOOCS
* [Data Science and Engineering with Apache Spark (edX XSeries)](https://www.edx.org/xseries/data-science-engineering-apache-spark) - Series of five courses ([Introduction to Apache Spark](https://www.edx.org/course/introduction-apache-spark-uc-berkeleyx-cs105x), [Distributed Machine Learning with Apache Spark](https://www.edx.org/course/distributed-machine-learning-apache-uc-berkeleyx-cs120x), [Big Data Analysis with Apache Spark](https://www.edx.org/course/big-data-analysis-apache-spark-uc-berkeleyx-cs110x), [Advanced Apache Spark for Data Science and Data Engineering](https://www.edx.org/course/advanced-apache-spark-data-science-data-uc-berkeleyx-cs115x), [Advanced Distributed Machine Learning with Apache Spark](https://www.edx.org/course/advanced-distributed-machine-learning-uc-berkeleyx-cs125x)) covering different aspects of software engineering and data science. Python oriented.
* [Big Data Analysis with Scala and Spark (Coursera)](https://www.coursera.org/learn/big-data-analysys) - Scala oriented introductory course. Part of [Functional Programming in Scala Specialization](https://www.coursera.org/specializations/scala).
### Workshops
* [AMP Camp](http://ampcamp.berkeley.edu) - Periodical training event organized by the [UC Berkeley AMPLab](https://amplab.cs.berkeley.edu/). A source of useful exercise and recorded workshops covering different tools from the [Berkeley Data Analytics Stack](https://amplab.cs.berkeley.edu/software/).
### Projects Using Spark
* [Oryx 2](https://github.com/OryxProject/oryx) - [Lambda architecture](http://lambda-architecture.net/) platform built on Apache Spark and [Apache Kafka](http://kafka.apache.org/) with specialization for real-time large scale machine learning.
* [Photon ML](https://github.com/linkedin/photon-ml) - A machine learning library supporting classical Generalized Mixed Model and Generalized Additive Mixed Effect Model.
* [PredictionIO](https://prediction.io/) - Machine Learning server for developers and data scientists to build and deploy predictive applications in a fraction of the time.
* [Crossdata](https://github.com/Stratio/Crossdata) - Data integration platform with extended DataSource API and multi-user environment.
### Docker Images
- [jupyter/docker-stacks/pyspark-notebook](https://github.com/jupyter/docker-stacks/tree/master/pyspark-notebook) - PySpark with Jupyter Notebook and Mesos client.
- [sequenceiq/docker-spark](https://github.com/sequenceiq/docker-spark) - Yarn images from [SequenceIQ](http://www.sequenceiq.com/).
- [datamechanics/spark](https://hub.docker.com/r/datamechanics/spark) - An easy to setup Docker image for Apache Spark from [Data Mechanics](https://www.datamechanics.co/).
### Miscellaneous
- [Spark with Scala Gitter channel](https://gitter.im/spark-scala/Lobby) - "_A place to discuss and ask questions about using Scala for Spark programming_" started by [@deanwampler](https://github.com/deanwampler).
- [Apache Spark User List](http://apache-spark-user-list.1001560.n3.nabble.com/) and [Apache Spark Developers List](http://apache-spark-developers-list.1001551.n3.nabble.com/) - Mailing lists dedicated to usage questions and development topics respectively.
## References
Wikipedia. 2017. Apache Spark Wikipedia, the Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Apache_Spark&oldid=781182753.
## License
This work (Awesome Spark, by https://github.com/awesome-spark/awesome-spark), identified by Maciej Szymkiewicz, is free of known copyright restrictions.
Apache Spark, Spark, Apache, and the Spark logo are trademarks of
The Apache Software Foundation. This compilation is not endorsed by The Apache Software Foundation.
Inspired by [sindresorhus/awesome](https://github.com/sindresorhus/awesome).