Online or onsite, instructor-led live Apache Beam training courses demonstrate through interactive hands-on practice how to implement the Apache Beam SDKs in a Java or Python application that defines a data processing pipeline for decomposing a big data set into smaller chunks for independent, parallel processing.
Apache Beam training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Zapopan onsite live Apache Beam trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
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Elevate your ambitions with exceptional office space located in Guadalajara’s vibrant financial hub. Work efficiently in modern, light-filled interiors behind the building’s striking mirrored-glass façade, and enjoy stunning views of the verdant fairways of Guadalajara Country Club.
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Apache Beam is an open source, unified programming model for defining and executing parallel data processing pipelines. It's power lies in its ability to run both batch and streaming pipelines, with execution being carried out by one of Beam's supported distributed processing back-ends: Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Beam is useful for ETL (Extract, Transform, and Load) tasks such as moving data between different storage media and data sources, transforming data into a more desirable format, and loading data onto a new system.
In this instructor-led, live training (onsite or remote), participants will learn how to implement the Apache Beam SDKs in a Java or Python application that defines a data processing pipeline for decomposing a big data set into smaller chunks for independent, parallel processing.
By the end of this training, participants will be able to:
Install and configure Apache Beam.
Use a single programming model to carry out both batch and stream processing from withing their Java or Python application.
Execute pipelines across multiple environments.
Format of the Course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
This course will be available Scala in the future. Please contact us to arrange.
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