[image: Beam.png] August 2018 | Newsletter
What’s been done [image: Tick - done] Apache Beam 2.6.0 Release - The Apache Beam team is pleased to announce the release of 2.6.0 version! This is the second release under the new build system, and the process has kept improving. - You can download the release here <https://beam.apache.org/get-started/downloads/> and read the release notes <https://issues.apache.org/jira/secure/ReleaseNote.jspa?version=12343392&projectId=12319527> for more details. Beam Summit London 2018 (by: Matthias Baetens, Gris Cuevas, Viktor Kotai) - Approval from the Apache Software Foundation is underway. We are currently finding a venue and sponsors. We’ll send the call for participation soon to curate the agenda. - If you’re interested in participating in the organization of the event, reach out to the organizers. - Dates TBD be we are considering the first or last days of October. Support for Bounded SDF in all runners (by: Eugene Kirpichov) - Beam introduced recently a new type of DoFn called SplittableDoFn <https://beam.apache.org/blog/2017/08/16/splittable-do-fn.html> (SDF) to enable richer modularity in its IO connectors. - Support for SDF in bounded (batch) connectors was added for all runners. Apache Kudu IO (by: Tim Robertson) - A new IO connector for the Apache Kudu <https://beam.apache.org/blog/2017/08/16/splittable-do-fn.html> data store was added recently. - See BEAM-2661 for more details on it. IO improvements (by: Ismaël Mejía) - HBaseIO added a new transform based on SDF called readAll. - See BEAM-4020 for more details on it. What we’re working on... Interactive Runner for Beam (by: Harsh Vardhan, Sindy Li, Chamikara Jayalath, Anand Iyer, Robert Bradshaw) - Notebook-based interactive processing of Beam pipelines. - This is now ready to try out in Jupyter Notebook for BeamPython pipelines over DirectRunner! - See the design doc <http://s.apache.org/interactive-beam> for more details and watch a demo here <https://www.youtube.com/watch?v=c5CjA1e3Cqw>. - Thoughts, comments and discussions welcome :) Python 3 Support (by, in alphabetical order: Ahmet Altay, Robert Bradshaw, Charles Chen, Matthias Feys, Holden Karau, Sergei Lebedev, Robbe Sneyders, Valentyn Tymofieiev) - Major progress has been made on making Beam Python codebase Python3-compatible through futurization. - Read for more details in the proposal <https://s.apache.org/beam-python-3>. New IO connectors (by: John Rudolf Lewis, Jacob Marble) - Amazon Simple Queue Service (SQS) is in review. - Amazon Redshift is in progress. Portable Runners (by: Ankur Goenka, Eugene Kirpichov, Ben Sidhom, Axel Magnuson, Thomas Weise, Ryan Williams , Robert Bradshaw, Daniel Oliveira, Holden Karau) - Good progress on Portable Flink Runner and many of the ValidatesRunner tests are passing now. - Portable Flink Runner can now execute batch WordCount in Java, Python and Go. - Many enhancements and bug fixes in Portable Reference Runner. - See Jira https://issues.apache.org/jira/browse/BEAM-2889 for more details on progress. Dependencies (by: Yifan Zou, Chamikara Jayalath) - We added a dependencies guide for Beam and tooling to automatically create JIRAs for significantly outdated dependencies. We are working on upgrading existing dependencies. - See the Beam dependencies guide <https://beam.apache.org/contribute/dependencies/> for more details. New Members New Contributors - Rose Nguyen, Seattle, WA, USA - Beam docs contributor - Working to improve docs usability - Connell O'Callaghan, Seattle, WA, USA - Interested in growing the community - Helping with community triages and managing issues Talks & Meetups Stream Processing Meetup@LinkedIn 7/19/18 - Xinyu Liu gave a talk on building a Samza Runner for Beam - “Beam meet up, Samza!” and see it here <https://www.slideshare.net/XinyuLiu11/beam-me-up-samza>. Large Scale Landuse Classification of Satellite Images, Berlin Buzzwords@Berlin 6/11/18 - Suneel Marthi and Jose Luis Contreras gave a talk on using streaming pipelines built on Apache Flink for model training and inference. They leveraged convolutional Neural Networks (CNNs) built with Apache MXNet to train Deep Learning models for land use classification. - Read about it and watch it here <https://berlinbuzzwords.de/18/session/large-scale-landuse-classification-satellite-imagery> . Big Data in Production Meetup@Cambridge, MA 6/28/18 - Robert Bradshaw and Eila Arich-Landkof gave a talk about Apache Beam and machine learning. - Event details here <https://www.meetup.com/Deep-Learning-In-Production/events/250589154/> and watch their talks here <https://www.youtube.com/watch?v=pPnY2y-zfaI>. Resources *Awesome Beam *(by: Pablo Estrada) - Inspired by efforts in Awesome Flink and Awesome Hadoop, I’ve created the Awesome Beam <https://github.com/pabloem/awesome-beam> repo to aggregate interesting Beam things. Until Next Time! *This edition was curated by our community of contributors, committers and PMCs. It contains work done in June and July of 2018 and ongoing efforts. We hope to provide visibility to what's going on in the community, so if you have questions, feel free to ask in this thread. * -- Rose Thị Nguyễn
