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From Gaurav Gupta <gau...@datatorrent.com>
Subject Re: [VOTE] Accept Apex into the Apache Incubator
Date Thu, 13 Aug 2015 19:40:09 GMT
+1 (Non-binding)

-Gaurav

> On Aug 13, 2015, at 10:22 AM, Pramod Immaneni <pramod@datatorrent.com> wrote:
> 
> +1 (Non-binding)
> 
> On Thu, Aug 13, 2015 at 7:48 AM, P. Taylor Goetz <ptgoetz@apache.org> wrote:
> 
>> Following the discussion thread [1], I would like to call a VOTE for
>> Accepting Apex as a new Apache Incubator project.
>> 
>> The proposal is available on the wiki [2] and is also attached below.
>> 
>> The VOTE will be open for at least 72 hours.
>> 
>> [ ] +1 Accept Apex into the Incubator
>> [ ] ±0 No opinion
>> [ ] -1 Do not accept Apex into the Incubator because…
>> 
>> Thanks,
>> 
>> -Taylor
>> 
>> [1] http://s.apache.org/apex_discuss
>> [2] https://wiki.apache.org/incubator/ApexProposal
>> 
>> 
>> == Abstract ==
>> Apex is an enterprise grade native YARN big data-in-motion platform that
>> unifies stream processing as well as batch processing. Apex processes big
>> data in-motion in a highly scalable, highly performant, fault tolerant,
>> stateful, secure, distributed, and an easily operable way. It provides a
>> simple API that enables users to write or re-use generic Java code, thereby
>> lowering the expertise needed to write big data applications.
>> 
>> Functional and operational specifications are separated. Apex is designed
>> in a way to enable users to write their own code (aka user defined
>> functions) as is and leave all operability to the platform. The API is very
>> simple and is designed to allow users to drop in their code as is. The
>> platform mainly deals with operability and treats functional code as a
>> black box. Operability includes fault tolerance, scalability, security,
>> ease of use, metrics api, webservices, etc. In other words there is no
>> separation of UDF (user defined functions), as all functional code is UDF.
>> This frees users to focus on functional development, and lets platform
>> provide operability support. The same code runs as is with different
>> operability attributes. The data-in-motion architecture of Apex unifies
>> stream as well as batch processing in a single platform. Since Apex is a
>> native YARN application, it leverages all the components of YARN without
>> duplication. Apex was developed with YARN in mind and has no overlapping
>> components/functionality with YARN.
>> 
>> The Apex platform is supplemented by project Malhar, which is a library of
>> operators that implement common business logic functions needed by
>> customers who want to quickly develop applications. These operators provide
>> access to HDFS, S3, NFS, FTP, and other file systems;  Kafka, ActiveMQ,
>> RabbitMQ, JMS, and other message systems; MySql, Cassandra, MongoDB, Redis,
>> HBase, CouchDB and other databases along with JDBC connectors. The Malhar
>> library also includes a host of other common business logic patterns that
>> help users to significantly reduce the time it takes to go into production.
>> Ease of integration with all other big data technologies is one of the
>> primary missions of Malhar.
>> 
>> == Proposal ==
>> The goal of this proposal is to establish the core engine of DataTorrent
>> RTS product as an Apache Software Foundation (ASF) project in order to
>> build a vibrant, diverse, and self-governed open source community around
>> the technology. DataTorrent will continue to sell management tools,
>> application building tools, easy to use big data applications, and custom
>> high end business logic operators. This proposal covers the Apex source
>> code (written in Java), Apex documentation and other materials currently
>> available on https://github.com/DataTorrent/Apex. This proposal also
>> covers the Malhar source code (written in Java), Malhar documentation, and
>> other materials currently available on
>> https://github.com/DataTorrent/Malhar. We have done a trademark check on
>> the name Apex, and have concluded that the Apex name is likely to be a
>> suitable project name.
>> 
>> == Background ==
>> DataTorrent RTS is a mature and robust product developed as a native YARN
>> application. RTS 1.0 was launched in summer of 2014; RTS 2.0 was launched
>> in Jan 2015. Both were well received by customers. RTS 3.0 was launched at
>> end of July 2015. RTS is among the first enterprise grade platform that was
>> developed from the ground up as native YARN application. DataTorrent RTS is
>> currently maintained by engineers as a closed source project. Even though
>> the engineers behind RTS are experienced software engineers and are
>> knowledge leaders in data-in-motion platforms, they have had little
>> exposure to the open source governance process. Customers are currently
>> running applications based on DataTorrent RTS in production.
>> 
>> == Rationale ==
>> Big data applications written for non-Hadoop platforms typically require
>> major rewrites  to get them to work with Hadoop. This rewriting creates a
>> significant bottleneck in terms of resources (expertise) which in turn
>> jeopardizes the viability of such an endeavour. It is hard enough to
>> acquire big data expertise, demanding additional expertise to do a major
>> code conversion makes it a very hard problem for projects to successfully
>> migrate to Hadoop. Also, due to the batch processing nature of Hadoop’s
>> MapReduce paradigm, users often have to wait tens of minutes to see results
>> and act on them due to various delays in data flow. DataTorrent’s RTS
>> data-in-motion architecture is designed to address this problem. It enables
>> even the non big data developer to write code and operate it in a scalable,
>> fault tolerant manner. The big data-in-motion architecture of DataTorrent’s
>> RTS enables ease of integration into current enterprise infrastructure.
>> This goal was achieved by keeping the API simple and empowering users to
>> put in the connector code as is (or with minimal changes).
>> 
>> Malhar is a manifestation of this reality, and we or the customer
>> engineers were able to create these connectors within a day or so if not
>> within a week. Connectors include those to integrate with message bus(es),
>> file systems, databases, other protocols, and more continue to be added.
>> Over a period of time we expect users to simply pick a connector that
>> already exists in Malhar and quickly begin integrating with their current
>> enterprise infrastructure. Within the data-in-motion architecture a stream
>> application is one with connector(s) to say Kafka, JMS, or Flume; while a
>> batch application is one with connector(s) to HDFS, HBase, FTP, NFS, S3n
>> etc. This allows usage of the platform for both stream as well as batch
>> processing with same business logic. Complete separation of user written
>> application code from all operational aspects of the system, as well as
>> support code for YARN, significantly expands the potential use cases that
>> can migrate to use Hadoop.
>> 
>> Apex will enable Hadoop eco-system to migrate a lot more use cases. It
>> will enable the Hadoop eco-system to deliver on a promise to rapidly
>> transform current IT infrastructure. Apex will help in significantly
>> increasing productization of big data projects. One of the main barometers
>> of success in the Hadoop eco-system is significant reduction of time to
>> market for big data applications migrating to Hadoop. We believe that Apex
>> will be one of the platforms that will enable users to extract value from
>> big data, by reducing time to market. This rapid innovation can be
>> optimally achieved through a vibrant, diverse, self-governed community
>> collectively innovating around Apex and the Malhar library, while at the
>> same time cross-pollinating with various other big data platforms. ASF is
>> an ideal place to meet this goal.
>> 
>> == Initial Goals ==
>> Our initial goals are to bring Apex and Malhar repositories into the ASF,
>> adapt internal engineering processes to open development, and foster a
>> collaborative development model in accordance with the "Apache Way."
>> DataTorrent plans to develop new functionality in an open, community-driven
>> way. To get there, the existing internal build, test and release processes
>> will be refactored to support open development. We already have an active
>> user community on google groups that we intend to migrate to Apache.
>> 
>> == Current Status ==
>> Currently, the project Apex code base is available under Apache 2.0
>> license (https://github.com/DataTorrent/Apex). Project Malhar code base
>> is available under Apache 2.0 license (
>> https://github.com/DataTorrent/Malhar). Project Malhar was open sourced 2
>> years ago which should make it easy for the project Malhar team to adapt to
>> an  open, collaborative, and meritocratic environment. Contributors of
>> Malhar are employees of DataTorrent or have agreed to the shift to Apache.
>> Project Apex, in contrast, was developed as a proprietary, closed-source
>> product, but the internal engineering practices adopted by the development
>> team were common to Malhar, and should lend themselves well to an open
>> environment. DataTorrent plans to execute a software grant agreement as
>> part of the launch of the incubation of Apex as an Apache project.
>> 
>> The DataTorrent team has always focused on building a robust end user
>> community of paying and non-paying customers. We think that the existing
>> community centered around the existing google groups mailing list should be
>> relatively easy to transform into an Apache-style community including both
>> users and developers.
>> 
>> === Meritocracy ===
>> Our proposed list of initial committers include the current RTS R&D team,
>> and our existing customers. This group will form a base for the broader
>> community we will invite to collaborate on the codebase. We intend to
>> radically expand the initial developer and user community by running the
>> project in accordance with the "Apache Way". Users and new contributors
>> will be treated with respect and welcomed. By participating in the
>> community and providing quality patches/support that move the project
>> forward, they will earn merit. They also will be encouraged to provide
>> non-code contributions (documentation, events, presentations, community
>> management, etc.) and will gain merit for doing so. Those with a proven
>> support and quality track record will be encouraged to become committers.
>> 
>> === Community ===
>> If Apex is accepted for incubation, the primary initial goal will be
>> transitioning the core community towards embracing the Apache Way of
>> project governance. We will solicit major existing contributors to become
>> committers on the project from the start. It should be noted that the
>> existing community is already more diverse in many ways than some top-level
>> Apache projects. We expect that we can encourage even more diversity.
>> 
>> === Core Developers ===
>> While a few core developers are skilled in working in openly governed
>> Apache communities, most of the core developers are currently NOT
>> affiliated with the ASF and would require new ICLAs before committing to
>> the project. There would also be a learning curve associated with this
>> on-boarding. Changing current development practices to be more open will be
>> an important step.
>> 
>> === Alignment ===
>> The following existing ASF projects provide related functionality as that
>> provided by Apex and should be considered when reviewing Apex proposal:
>> 
>> Apache HadoopⓇ is a distributed storage and processing framework for very
>> large datasets focusing primarily on batch processing for analytic
>> purposes. Apex is a native YARN application. The Apex and Malhar roadmap
>> includes plans to continue to leverage YARN, and help the YARN community
>> develop the ability to support long running applications. Apex uses DFS
>> interface of its core checkpoint/commit. Malhar has a large number of
>> operators that leverage HDFS and other Apache projects. Our roadmap
>> includes plans to continue to deepen the currently close integration with
>> HDFS.
>> 
>> Apache HBase offers tabular data stored in Hadoop based on the Google
>> Bigtable model. Malhar has HBase connectors to ease integration with HBase.
>> Malhar roadmap includes plans to continue to enhance integration with
>> Apache HBase.
>> 
>> Apache Kafka offers distributed and durable publish-subscribe messaging.
>> Malhar integrates Kafka with Hadoop through feature rich connectors and
>> supports ingest as well as analytical functions to incoming data. Raw data
>> can be ingested from Kafka and results can be written to Kafka. Malhar
>> roadmap includes plans to continue to enhance integration with Apache Kafka.
>> 
>> Apache Flume is a distributed, reliable, and available service for
>> efficiently collecting, aggregating, and moving large amounts of log data.
>> Malhar has Flume connectors to ease integration with Flume. These
>> connectors ensures that ingestion with Flume is fault tolerant and thus can
>> be done in real-time with the same SLA as Flume’s HDFS connectors. Malhar
>> roadmap includes plans to continue to enhance integration with Apache Flume.
>> 
>> Apache Cassandra is a highly scalable, distributed key-value store that
>> focuses on eventual consistency. Malhar has connectors to ease integration
>> with Cassandra. Malhar roadmap includes plans to continue to enhance
>> integration with Apache Cassandra.
>> 
>> Apache Accumulo is a distributed key-value store based on Google’s
>> BigTable design. Malhar has connectors to ease integration with Accumulo.
>> The Malhar roadmap includes plans to continue to enhance integration with
>> Apache Accumulo.
>> 
>> Apache Tez is aimed at building an application framework which allows for
>> a complex DAG of tasks for process data. The Apex and Malhar roadmaps
>> include plans to integrate with Apache Tez but this is not currently
>> supported.
>> 
>> Apache ActiveMQ and its sub project Apache Apollo offers a powerful
>> message queue framework. Malhar has ActiveMQ connectors that ease
>> integration with ActiveMQ.
>> 
>> Apache Spark is an engine for processing large datasets, typically in a
>> Hadoop cluster. Malhar project makes it easy for users to integrate with
>> Spark. The Malhar roadmap includes plans to continue to enhance integration
>> with Apache Spark.
>> 
>> Apache Flink is an engine for scalable batch and stream data processing.
>> Malhar project makes it easy for users to integrate with Flink. There is
>> overlap in how Flink leverages data-in-motion architecture for both stream
>> and batch processing, and it does subscribe to our thought process that
>> data-in-motion can handle both stream and batch, meanwhile a batch only
>> engine will find it harder to manage streams. We differ in terms of how we
>> handle operability, user defined code, metrics, webservices etc. Apex is
>> very operational oriented, while Flink has much more focus on functional
>> elements. Malhar and rapid availability of common business logic is another
>> differentiator. We believe both these approaches are valid and the
>> community and innovation will gain by through cross pollination. We plan to
>> integrate with Apache Flink via HDFS for now.
>> 
>> Apache Hive software facilitates querying and managing large datasets
>> residing in distributed storage. Malhar project makes it easy for users to
>> integrate with Apache Hive. The Malhar roadmap includes plans to continue
>> to enhance integration with Apache Hive.
>> 
>> Apache Pig is a platform for analyzing large data sets.  Pig consists of a
>> high-level language for expressing data analysis programs, coupled with
>> infrastructure for evaluating these programs. The Apex and Malhar roadmaps
>> include plans to integrate with Apache Pig.
>> 
>> Apache Storm is a distributed realtime computation system. Malhar makes it
>> easy for users to integrate with Apache Storm. We plan to integrate with
>> Apache Storm via HDFS for now. Malhar roadmaps include plans to continue to
>> support mechanism for integration with Apache Storm.
>> 
>> Apache Samza is a distributed stream processing framework. Malhar makes it
>> easy for users to integrate with Apache Samza. We plan to integrate with
>> Apache Samza via HDFS or Apache Kafka for now. Malhar roadmaps include
>> plans to continue to support mechanism for integration with Apache Samza.
>> 
>> Apache Slider is a YARN application to deploy existing distributed
>> applications on YARN, monitor them, and make them larger or smaller as
>> desired even when the application is running. Once Slider matures, we will
>> take a look at close integration of Apex with Slider.
>> 
>> Project Malhar and Apex are aligned to many more Apache projects and other
>> open source projects as ease of integration with other technologies is one
>> of the primary goals of this project. These include Apache Solr,
>> ElasticSearch, MongoDB, Aerospike, ZeroMQ, CouchDB, CouchBase, MemCache,
>> Redis, RabbitMQ, Apache Derby.
>> 
>> == Known Risks ==
>> Development has been sponsored mostly by a single company (DataTorrent,
>> Inc.) thus far and coordinated mainly by the core DataTorrent RTS and
>> Malhar team, with active participation from our current customers.
>> 
>> For the project to fully transition to the Apache Way governance model,
>> development must shift towards the merit-centric model of growing a
>> community of contributors balanced with the needs for extreme stability and
>> core implementation coherency.
>> 
>> The tools and development practices in place for the DataTorrent RTS and
>> Malhar products are compatible with the ASF infrastructure and thus we do
>> not anticipate any on-boarding pains. Migration from the current GitHub
>> repository is also expected to be straightforward.
>> 
>> === Orphaned products ===
>> DataTorrent is fully committed to DataTorrent Apex and Malhar and the
>> product will continue to be based on the Apex project. Moreover,
>> DataTorrent has a vested interest in making Apex succeed by driving its
>> close integration with sister ASF projects. We expect this to further
>> reduce the risk of orphaning the product.
>> 
>> === Inexperience with Open Source ===
>> DataTorrent has embraced open source software by open sourcing Malhar
>> project under Apache 2.0 license. The DataTorrent team includes veterans
>> from the Yahoo! Hadoop team. Although some of the initial committers have
>> not been developers on an entirely open source, community-driven project,
>> we expect to bring to bear the open development practices of Malhar to the
>> Apex project. Additionally, several ASF veterans agreed to mentor the
>> project and are listed in this proposal. The project will rely on their
>> guidance and collective wisdom to quickly transition the entire team of
>> initial committers towards practicing the Apache Way. DataTorrent is also
>> driving the Kafka on YARN (KOYA) initiative.
>> 
>> === Homogeneous Developers ===
>> While most of the initial committers are employed by DataTorrent, we have
>> already seen a healthy level of interest from our existing customers and
>> partners. We intend to convert that interest directly into participation
>> and will be investing in activities to recruit additional committers from
>> other companies.
>> 
>> === Reliance on Salaried Developers ===
>> Most of the contributors are paid to work in the Big Data space. While
>> they might wander from their current employers, they are unlikely to
>> venture far from their core expertises and thus will continue to be engaged
>> with the project regardless of their current employers.
>> 
>> === Relationships with Other Apache Products ===
>> As mentioned in the Alignment section, Apex may consider various degrees
>> of integration and code exchange with Apache Hadoop (YARN and HDFS), Apache
>> Kafka, Apache HBase, Apache Flume, Apache Cassandra, Apache Accumulo,
>> Apache Tez, Apache Hive, Apache Pig, Apache Storm, Apache Samza, Apache
>> Spark, Apache Slider. Given the success that the DataTorrent RTS product
>> enjoyed, we expect integration points to be inside and outside the project.
>> We look forward to collaborating with these communities as well as other
>> communities under the Apache umbrella.
>> 
>> === An Excessive Fascination with the Apache Brand ===
>> While we intend to leverage the Apache ‘branding’ when talking to other
>> projects as testament of our project’s ‘neutrality’, we have no plans for
>> making use of Apache brand in press releases nor posting billboards
>> advertising acceptance of Apex into Apache Incubator.
>> 
>> 
>> == Documentation ==
>> See documentation for the current state of the project documentation
>> available as part of the GitHub repositories -
>> https://github.com/DataTorrent/Apex; https://github.com/DataTorrent/Malhar.
>> In addition a list of demos that serve as a how to guide are available at
>> https://github.com/DataTorrent/Malhar/tree/master/demos
>> 
>> == Initial Source ==
>> DataTorrent has released the source code for Apex under Apache 2.0 License
>> at https://github.com/DataTorrent/Apex, and that of Malhar under Apache
>> 2.0 licence at https://github.com/DataTorrent/Malhar. We encourage ASF
>> community members interested in this proposal to download the source code,
>> review it and try out the software.
>> 
>> == Source and Intellectual Property Submission Plan ==
>> As soon as Apex is approved to join Apache Incubator, DataTorrent will
>> execute a Software Grant Agreement and the source code will be transitioned
>> onto ASF infrastructure. The code is already licensed under the  Apache
>> Software License, version 2.0. We know of no legal encumberments that would
>> inhibit the transfer of source code to the ASF.
>> 
>> == External Dependencies ==
>> All dependencies fall under the permissive licenses categories, or weak
>> copy left (http://www.apache.org/legal/resolved.html#category-b). We
>> intend to remove the dependencies on GPL licensed technologies on which
>> APex or Malhar depend. These technologies are optional and have been marked
>> as such.
>> 
>> Embedded dependencies (relocated):
>>   * None
>> 
>> Runtime dependencies:
>>   * activemq-client
>>   * ant
>>   * async-http-client
>>   * bval-jsr303
>>   * commons-beanutils
>>   * commons-codec
>>   * commons-lang3
>>   * commons-compiler
>>   * embassador
>>   * fastutil
>>   * guava
>>   * hadoop-common
>>   * hadoop-common-tests
>>   * hadoop-yarn-client
>>   * httpclient
>>   * jackson-core-asl
>>   * jackson-mapper-asl
>>   * javax.mail
>>   * jersey-apache-client4
>>   * jersey-client
>>   * jetty-servlet
>>   * jetty-websocket
>>   * jline
>>   * kryo
>>   * named-regexp
>>   * netlet
>>   * rhino (GPL 2.0, optional)
>>   * slf4j-api
>>   * slf4j-log4j12
>>   * validation-api
>>   * xbean-asm5-shaded
>>   * zip4j
>> 
>> Module or optional dependencies
>>   * accumulo-core
>>   * aerospike-client
>>   * amqp-client
>>   * aws-java-sdk-kinesis
>>   * cassandra-driver-core
>>   * couchbase-client
>>   * CouchbaseMock
>>   * elasticsearch
>>   * geoip-api (LGPL, optional)
>>   * hbase
>>   * hbase-client
>>   * hbase-server
>>   * hive-exec
>>   * hive-service
>>   * hiveunit
>>   * javax.mail-api
>>   * jedis
>>   * jms-api
>>   * jri (GPL, optional)
>>   * jriengine (LGPL, optional)
>>   * jruby (LGPL, optional)
>>   * jython (PSF License, optional)
>>   * jzmq (LGPL, optional)
>>   * kafka_2.10
>>   * lettuce (GPL, optional)
>>   * libthrift
>>   * Memcached-Java-Client
>>   * mongo-java-driver
>>   * mqtt-client
>>   * mysql-connector-java (GPL2, optional)
>>   * org.ektorp
>>   * rengine (LGPL, optional)
>>   * rome
>>   * solr-core
>>   * solr-solrj
>>   * spymemcached
>>   * sqlite4java
>>   * super-csv
>>   * twitter4j-core
>>   * twitter4j-stream
>>   * uadetector-resources
>>   * org.apache.servicemix.bundles.splunk
>> 
>> Build only dependencies:
>>   * None
>> 
>> Test only dependencies:
>>   * activemq-broker
>>   * activemq-kahadb-store
>>   * greenmail
>>   * hadoop-yarn-server-tests
>>   * hsqldb
>>   * janino
>>   * junit
>>   * MockFtpServer
>>   * mockito-all
>>   * testng
>> 
>> Cryptography N/A
>> 
>> == Required Resources ==
>> === Mailing lists ===
>>   * private@apex.incubator.apache.org (moderated subscriptions)
>>   * commits@apex.incubator.apache.org
>>   * dev@apex.incubator.apache.org
>> 
>> === Git Repository ===
>>   * https://git-wip-us.apache.org/repos/asf/incubator-apex-core.git
>>   * https://git-wip-us.apache.org/repos/asf/incubator-apex-malhar.git
>> 
>> === Issue Tracking ===
>>   * JIRA Project Apex (APEX_CORE) // If '_' is not allowed, use APEXCORE
>>   * JIRA Project Malhar (APEX_MALHAR) // If '_' is not allowed use
>> APEXMALHAR
>> 
>> === Other Resources ===
>>   * Means of setting up regular builds for apex-core on builds.apache.org
>>   * Means of setting up regular builds for apex-malhar on
>> builds.apache.org
>> 
>> === Rationale for Malhar and Apex having separate git and jira ===
>> We managed Malhar and Apex as two repos and two jiras on purpose. Both
>> code bases are released under Apache 2.0 and are proposed for incubation.
>> In terms of our vision to enable innovation around a native YARN
>> data-in-motion that unifies stream processing as well as batch processing
>> Malhar and Apex go hand in hand. Apex has base API that consists of java
>> api (functional), and attributes (operability). Malhar is a manifestation
>> of this api, but from user perspective, Malhar is itself an API to leverage
>> business logic. Over past three years we have found that the cadence of
>> release and api changes in Malhar is much rapid than Apex and it was
>> operationally much easier to separate them into their own repos. Two repos
>> will reflect clear separation of engine (Apex) and operators/business logic
>> (Malhar). It will allow or independent release cycles (operator change
>> independent of engine due to stable API). We however do not believe in two
>> levels of committers. We believe there should be one community that works
>> across both and innovates with ideas that Malhar and Apex combined provide
>> the value proposition. We are proposing that Apache incubation process help
>> us to foster development of one community (mailing list, committers), and a
>> yet be ok with two repos. We are proposing that this be taken up during
>> incubation. Community will learn if this works. The decision on whether to
>> split them into two projects be taken after the learning curve during
>> incubation.
>> 
>> == Initial Committers ==
>>   * Roma Ahuja (rahuja at directv dot com)
>>   * Isha Arkatkar (isha at datatorrent dot com)
>>   * Raja Ali (raji at silverspringnet dot com)
>>   * Sunaina Chaudhary ( SChaudhary at directv dot com)
>>   * Bhupesh Chawda (bhupesh at datatorrent dot com)
>>   * Chaitanya Chelobu (chaitanya at datatorrent dot com)
>>   * Bright Chen (bright at datatorrent dot com)
>>   * Pradeep Dalvi (pradeep dot dalvi at datatorrent dot com)
>>   * Sandeep Deshmukh (sandeep at datatorrent dot com)
>>   * Yogi Devendra (yogi at datatorrent dot com)
>>   * Cem Ezberci (hasan dot ezberci at ge dot com)
>>   * Timothy Farkas (tim at datatorrent dot com)
>>   * Ilya Ganelin (ilya dot ganelin at capitalone dot com)
>>   * Vitthal Gogate (vitthal_gogate at yahoo dot com)
>>   * Parag Goradia (parag dot goradia at ge dot com)
>>   * Tushar Gosavi (tushar at datatorrent dot com)
>>   * Priyanka Gugale (priyanka at datatorrent dot com)
>>   * Gaurav Gupta (gaurav at datatorrent dot com)
>>   * Sandesh Hegde (sandesh at datatorrent dot com)
>>   * Siyuan Hua ( siyuan at datatorrent dot com)
>>   * Ajith Joseph (ajoseph at silverspring dot com)
>>   * Amol Kekre ( amol at datatorrent dot com)
>>   * Chinmay Kolhatkar ( chinmay at datatorrent dot com)
>>   * Pramod Immaneni ( pramod at datatorrent dot com)
>>   * Anuj Lal ( anuj dot lal at ge dot com)
>>   * Dongsu Lee (dlee3 at directv dot com)
>>   * Vitaly Li (blossom dot valley at gmail dot com)
>>   * Dean Lockgaard (dean  at datatorrent dot com)
>>   * Rohan Mehta (rohan_mehta at apple dot com)
>>   * Adi Mishra (apmishra at directv dot com, adi dot mishra at gmail dot
>> com)
>>   * Chetan Narsude (chetan  at datatorrent dot com)
>>   * Darin Nee (dnee at silverspring dot com)
>>   * Alexander Parfenov (sasha at datatorrent dot com)
>>   * Andrew Perlitch (andy at datatorrent dot com)
>>   * Shubham Phatak (shubham at datatorrent dot com)
>>   * Ashwin Putta (ashwin at datatorrent dot com)
>>   * Rikin Shah (shah_rikin at yahoo dot com)
>>   * Luis Ramos (l dot ramos at ge dot com)
>>   * Munagala Ramanath (ram at datatorrent dot com)
>>   * Vlad Rozov (vlad dot rozov at datatorrent dot com)
>>   * Atri Sharma (atri dot jiit at gmail dot com)
>>   * Chandni Singh (chandni at datatorrent dot com)
>>   * Venkatesh Sivasubramanian (venkateshs at ge dot com)
>>   * Aniruddha Thombare (aniruddha at datatorrent dot com)
>>   * Jessica Wang (jessica at datatorrent dot com)
>>   * Thomas Weise (thomas at datatorrent dot com)
>>   * David Yan (david at datatorrent dot com)
>>   * Kevin Yang (yang dot k at ge dot com)
>>   * Brennon York (brennon dot york at capitalone dot com)
>> 
>> == Affiliations ==
>>   * Apple: Vitaly Li, Rohan Mehta
>>   * Barclays: Atri Sharma
>>   * Class Software: Justin Mclean
>>   * CapitalOne: Ilya Ganelin, Brennon York
>>   * DataTorrent: everyone else on this proposal
>>   * Datachief: Rikin Shah
>>   * DirecTV: Roma Ahuja, Sunaina Chaudhary, Dongsu Lee, Adi Mishra
>>   * E8security: Vitthal Gogate
>>   * General Electric: Cem Ezberci, Parag Goradia, Anuj Lal, Luis Ramos,
>> Venkatesh Sivasubramanian, Kevin Yang
>>   * Hortonworks: Alan Gates, Taylor Goetz, Chris Nauroth, Hitesh Shah
>>   * MapR: Ted Dunning
>>   * SilverSpring Networks: Raja Ali, Ajith Joseph, Darin Nee
>> 
>> == Sponsors ==
>> 
>> === Champion ===
>> Ted Dunning
>> 
>> === Nominated Mentors ===
>> 
>> The initial mentors are listed below:
>>   * Ted Dunning - Apache Member, MapR
>>   * Alan Gates - Apache Member, Hortonworks
>>   * Taylor Goetz - Apache Member, Hortonworks
>>   * Justin Mclean - Apache Member, Class Software
>>   * Chris Nauroth - Apache Member, Hortonworks
>>   * Hitesh Shah: Apache Member, Hortonworks
>> 
>> === Sponsoring Entity ===
>> 
>> We would like to propose Apache incubator to sponsor this project.
>> 
>> 


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