|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Deequ||2,722||4||a day ago||31||February 15, 2022||124||apache-2.0||Scala|
|Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.|
|Spark Cassandra Connector||1,892||109||22||3 months ago||81||April 08, 2021||22||apache-2.0||Scala|
|DataStax Spark Cassandra Connector|
|Petastorm||1,593||6||10 days ago||77||February 19, 2022||169||apache-2.0||Python|
|Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.|
|Spark Py Notebooks||1,227||6 years ago||6||other||Jupyter Notebook|
|Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks|
|Mobius||939||6||4 months ago||22||January 29, 2017||88||mit||C#|
|C# and F# language binding and extensions to Apache Spark|
|Spark Movie Lens||757||2 years ago||10||other||Jupyter Notebook|
|An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset|
|Cdap||702||66||26||14 hours ago||62||November 30, 2018||73||other||Java|
|An open source framework for building data analytic applications.|
|Machinelearning||684||3 years ago||1||Python|
|Machine learning resources，including algorithm, paper, dataset, example and so on.|
|Flambo||606||24||5 years ago||26||January 16, 2018||13||epl-1.0||Clojure|
|A Clojure DSL for Apache Spark|
|Complete Life Cycle Of A Data Science Project||387||5 days ago||4||mit|
CDAP is an integrated, open source application development platform for the Hadoop ecosystem that provides developers with data and application abstractions to simplify and accelerate application development, address a broader range of real-time and batch use cases, and deploy applications into production while satisfying enterprise requirements.
CDAP is a layer of software running on top of Apache Hadoop platforms such as the Cloudera Enterprise Data Hub or the Hortonworks Data Platform. CDAP provides these essential capabilities:
CDAP exposes developer APIs (Application Programming Interfaces) for creating applications and accessing core CDAP services. CDAP defines and implements a diverse collection of services that land applications and data on existing Hadoop infrastructure such as HBase, HDFS, YARN, MapReduce, Hive, and Spark.
You can run applications ranging from simple MapReduce Jobs and complete ETL (extract, transform, and load) pipelines all the way up to complex, enterprise-scale data-intensive applications.
Developers can build and test their applications end-to-end in a full-stack, single-node installation. CDAP can be run either as a Sandbox, deployed within the Enterprise on-premises or hosted in the Cloud.
For more information, see our collection of Developers' Manual and other documentation.
To install and use CDAP, there are a few simple prerequisites:
You can get started with CDAP by building directly from the latest source code:
git clone https://github.com/caskdata/cdap.git cd cdap mvn clean package
After the build completes, you will have built all modules for CDAP.
For more build options, please refer to the build instructions.
Visit our web site for an introductory tutorial for developers that will guide you through installing CDAP and running an example application.
Now that you've had a look at the CDAP Sandbox, take a look at:
Interested in helping to improve CDAP? We welcome all contributions, whether in filing detailed bug reports, submitting pull requests for code changes and improvements, or by asking questions and assisting others on the mailing list.
For quick guide to getting your system setup to contribute to CDAP, take a look at our Contributor Quickstart Guide.
Bugs and suggestions should be made by filing an issue.
Existing issues can be browsed at the CDAP project issues.
We have a simple pull-based development model with a consensus-building phase, similar to Apache's voting process. If youd like to help make CDAP better by adding new features, enhancing existing features, or fixing bugs, here's how to do it:
Thanks for helping to improve CDAP!
CDAP User Group and Development Discussions:
The cdap-user mailing list is primarily for users using the product to develop applications. You can expect questions from users, release announcements, and any other discussions that we think will be helpful to the users.
The cdap-dev mailing list is essentially for developers actively working on the product, and should be used for all our design, architecture and technical discussions moving forward. This mailing list will also receive all JIRA and GitHub notifications.
Copyright 2014-2017 Cask Data, Inc.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Cask is a trademark of Cask Data, Inc. All rights reserved.
Apache, Apache HBase, and HBase are trademarks of The Apache Software Foundation. Used with permission. No endorsement by The Apache Software Foundation is implied by the use of these marks.