DataFu makes it easier to solve data problems using Hadoop and higher level languages based on it.
Data{{`Fu provides a collection of Hadoop Map}}Reduce jobs and functions in higher level languages based on it to perform data analysis. It provides functions for common statistics tasks (e.g. quantiles, sampling), Page
Rank, stream sessionization, and set and bag operations. Data
Fu also provides Hadoop jobs for incremental data processing in Map
`Reduce.
Data{{`Fu began two years ago as set of UDFs developed internally at Linked}}In, coming from our desire to solve common problems with reusable components. Recognizing that the community could benefit from such a library, we added documentation, an extensive suite of unit tests, and open sourced the code. Since then there have been steady contributions to Data
Fu as we encountered common problems not yet solved by it. Others outside Linked
In have contributed as well. More recently we recognized the challenges with efficient incremental processing of data in Hadoop and have contributed a set of Hadoop Map
`Reduce jobs as a solution.
Data{{`Fu began as a project at Linked}}In, but it has shown itself to be useful to other organizations and developers as well as they have faced similar problems. We would like to share Data
`Fu with the ASF and begin developing a community of developers and users within Apache.
There is a strong need for well tested libraries that help developers solve common data problems in Hadoop and higher level languages such as Pig, Hive, Crunch, Scalding, etc.
Our intent with this incubator proposal is to start building a diverse developer community around Data{{`Fu following the Apache meritocracy model. Since Data}}Fu was initially open sourced in 2011, it has received contributions from both within and outside Linked
`In. We plan to continue support for new contributors and work with those who contribute significantly to the project to make them committers.
Data{{`Fu has been building a community of developers for two years. It began with contributors from Linked}}`In and has received contributions from developers at Cloudera since very early on. It has been included included in Cloudera’s Hadoop Distribution and Apache Bigtop. We hope to extend our contributor base significantly and invite all those who are interested in solving large-scale data processing problems to participate.
Data{{`Fu has a strong base of developers at Linked}}In. Matthew Hayes initiated the project in 2011, and aside from continued contributions to Data
Fu has also contributed the sub-project Hourglass for incremental Map
Reduce processing. Separate from Data
Fu he has also open sourced the White Elephant project. Sam Shah contributed a significant portion of the original code and continues to contribute to the project. William Vaughan has been contributing regularly to Data
Fu for the past two years. Evion Kim has been contributing to Data
Fu for the past year. Xiangrui Meng recently contributed implementations of scalable sampling algorithms based on research from a paper he published. Chris Lloyd has provided some important bug fixes and unit tests. Mitul Tiwari has also contributed to Data
Fu. Mathieu Bastian has been developing Map
Reduce jobs that we hope to include in Data
`Fu. In addition he also leads the open source Gephi project.
The ASF is the natural choice to host the Data{{`Fu project as its goal of encouraging community-driven open-source projects fits with our vision for Data}}Fu. Additionally, other projects Data
`Fu integrates with, such as Apache Pig and Apache Hadoop, and in the future Apache Hive and Apache Crunch, are hosted by the ASF and we will benefit and provide benefit by close proximity to them.
The core developers have been contributing to Data{{`Fu for the past two years. There is very little risk of Data}}Fu being abandoned given its widespread use within Linked
`In.
Data{{`Fu was started as an open source project in 2011 and has remained so for two years. Matt initiated the project, and additionally is the creator of the open source White Elephant project. He has also contributed patches to Apache Pig. Most recently he has released Hourglass as a sub-project of Data}}Fu. Sam contributed much of the original code and continues to contribute to the project. Will has been contributing to Data
`Fu since it was first open sourced. Evion has been contributing for the past year. Mathieu leads the open source Gephi project. Jakob has been actively involved with the ASF as a full-time Hadoop committer and PMC member.
The current core developers are all from Linked{{`In. Data}}`Fu has also received contributions from other corporations such as Cloudera. Two of these developers are among the Initial Committers listed below. We hope to establish a developer community that includes contributors from several other corporations and we are actively encouraging new contributors via presentations and blog posts.
The current core developers are salaried employees of Linked{{`In, however they are not paid specifically to work on Data}}Fu. Contributions to Data
Fu arise from the developers solving problems they encounter in their various projects. The purpose of Data
`Fu is to share these solutions so that others may benefit and build a community of developers striving to solve common problems together. Furthermore, once the project has a community built around it, we expect to get committers, developers and contributions from outside the current core developers.
DataFu is deeply integrated with Apache products. It began as a library of user-defined functions for Apache Pig. It has grown to also include Hadoop jobs for incremental data processing and in the future will include code for other higher level languages built on top of Apache Hadoop.
While we respect the reputation of the Apache brand and have no doubts that it will attract contributors and users, our interest is primarily to give DataFu a solid home as an open source project following an established development model.
Information on DataFu can be found at:
https://github.com/LinkedIn/DataFu/blob/master/README.md
The initial source is available at:
https://github.com/LinkedIn/DataFu
The initial source has the following external dependencies that are either included in the final DataFu library or required in order to use it:
In addition, the following external libraries are used either in building, developing, or testing the project:
Data{{`Fu has user-defined functions that use MD5 and SHA provided by Java’s java.security.Message}}`Digest.
Data{{`Fu-private for private PMC discussions (with moderated subscriptions) Data}}Fu-dev Data
`Fu-commits
Git is the preferred source control system: git://git.apache.org/DataFu
JIRA Data{{`Fu (Data}}`Fu)
The existing code already has unit tests, so we would like a Hudson instance to run them whenever a new patch is submitted. This can be added after project creation.
Jakob Homan (Apache Member)
We are requesting the Incubator to sponsor this project.