NP Classroom - Thimphu

near Le Méridien , Chorten Lam, Thimphu, Bhutan, 11001

Set in Thimphu, this classroom is well located in Chorten Lam with all amenities and WiFi.

For Sales En...

NP Classroom - Paro

near Le Méridien Riverfront, thimphu hwy, Shaba, Paro, Bhutan, 12001

Set in Paro, this classroom is well located near Paro-Thimphu Highway around 4 km from the airport, and 7 km from Rinpung ...

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Unified Batch and Stream Processing with Apache Beam

14 Hours
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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Testimonials (2)

Sufficient hands on, trainer is knowledgable

Chris Tan
Course - A Practical Introduction to Stream Processing

Recalling/reviewing keypoints of the topics discussed.

Paolo Angelo Gaton - SMS Global Technologies Inc.
Course - Building Stream Processing Applications with Kafka Streams
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