Dataflow Autoscaling Pipelines

Google Cloud 2018    |    Intermediate
  • 10 Videos | 42m 12s
  • Includes Assessment
  • Earns a Badge
Likes 20 Likes 20
Apache Beam, Cloud Dataflow, and Cloud Dataprep can be used to create data pipelines. Explore how areas of Beam, Apache Beam SDK, Cloud Dataflow, and Cloud Dataprep assist in pipeline management.

WHAT YOU WILL LEARN

  • define Apache Beam concepts and SDKs
    describe the Python SDK and its connection with data pipelines
    describe the Java SDK and its connection with data pipelines
    initialize Cloud Dataprep
    demonstrate how to ingest data into a pipeline
  • create recipes in a Cloud Dataprep pipeline
    work with the import/export process and demonstrate how to run Dataflow jobs in Cloud Dataprep
    describe MapReduce and the benefits of Cloud Dataflow over MapReduce
    outline serverless architecture and some of the GCP products supporting data analytics
    describe the use of Apache Beam, Cloud Dataflow, and Cloud Dataprep in GCP to create and manage pipelines

IN THIS COURSE

  • Playable
    1. 
    Apache Beam SDK
    2m 37s
    UP NEXT
  • Playable
    2. 
    Data Pipelines Using Beam Python SDK
    1m 25s
  • Locked
    3. 
    Data Pipelines Using Beam Java SDK
    1m 44s
  • Locked
    4. 
    Cloud Dataprep Initialization
    4m 25s
  • Locked
    5. 
    Ingestion of Data into a Pipeline
    3m 36s
  • Locked
    6. 
    Recipe Configuration with Cloud Dataprep
    6m 29s
  • Locked
    7. 
    Import/Export Flows and Dataflow Jobs
    4m 14s
  • Locked
    8. 
    MapReduce Process versus Cloud Dataflow
    2m 35s
  • Locked
    9. 
    Serverless Architecture and Data Analytics
    3m 3s
  • Locked
    10. 
    Exercise: Using Beam, Dataflow, and Dataprep
    7m 34s

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