BigDataX Presents: Build data-parallel processing pipelines using Apache Beam

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BigDataX Presents: Build data-parallel processing pipelines using Apache Beam

== Description ==
About Apache Beam - Apache Beam is an open source, unified model for defining both batch and streaming data-parallel processing pipelines. Using one of the open source Beam SDKs, you build a program that defines the pipeline. The pipeline is then executed by one of Beam’s supported distributed processing back-ends, which include Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow.

== About the Speakers ==
Reza Rokni: In his 6 years at Google as an Enterprise Solution Archetect, Google Cloud Platform, Reza has been lucky enough to work with developers from many industries from Gaming to Banking, applying Google's Data Analytics technologies to new domains. Currently as developer advocate for Google Cloud Dataflow and Apache Beam, he gets to have fun talking about stream processing all day long, or should that be stream processing unbounded.

Tim Sell: Tim is a data engineer and consultant currently working as a Strategic Cloud Engineer at Google Singapore. He’s been working in data analytics since 2008 and helps customers build data analytics foundations, pipelines and applications on Google Cloud. He’s a fan of Dataflow and Apache Beam and is also keen contributor to the Kubeflow Machine Learning platform.

Henry Suryawirawan: Henry is a Strategic Cloud Engineer who helps Google's most strategic clients to deploy GCP solutions from inception to production. He comes from Application Development background and is highly experienced in Agile, DevOps, Continuous Delivery, and Cloud. Henry has a diverse experience working in consulting, startup, government, banking and insurance domains. Before Google, Henry worked at ThoughtWorks helping Singapore GovTech to build and deliver several Singapore Smart Nation initiative projects.

== Date & Location ==
9th May, Thursday 7pm – 9pm
Google Singapore
70 Pasir Panjang Rd, #03-71, Singapore[masked]

== Target audience ==
Everyone interested in Data Engineering and Data Science, who want to learn about one of the newer and exciting Apache projects focused on batch & streaming processing. We try to give everyone the same foundations at the start of the meet up and go more expert along the way, covering both business value as well as digging deeper technically.

== Prerequisites ==
Its recommended to have an understanding of Batch and Streaming processing.

== Schedule ==
7.00 PM - 7.30pm: Reception and Registration
7.30 PM - 9.00pm: Build Data pipelines using Apache Beam
9:00 PM onwards: Networking

== Price ==
Free! (Limited seats)