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Build Batch Data Pipelines on Google Cloud

Master building and optimizing batch data pipelines on Google Cloud in this intermediate data engineering course. Learn advanced data transformation, workflow orchestration, and data pipeline optimization using Dataflow for Apache Beam and Dataproc Serverless for Apache Spark. Gain hands-on experience in data quality, monitoring, and alerting to ensure reliable and...

  • 4.6 out of 5 rating
  • Last updated : 23/07/2026
  • English

Available as Instructor Led Training, Live Online & In Person at your Offices or Ours.

Duration:

1.00 hours

0.8 CPD hours

Overview:

Master building and optimizing batch data pipelines on Google Cloud in this intermediate data engineering course. Learn advanced data transformation, workflow orchestration, and data pipeline optimization using Dataflow for Apache Beam and Dataproc Serverless for Apache Spark. Gain hands-on experience in data quality, monitoring, and alerting to ensure reliable and scalable ETL/ELT pipelines. Ideal for learners with basic knowledge of SQL, Python, data warehousing, and Google Cloud Platform (GCP).

Description:

Master building and optimizing batch data pipelines on Google Cloud in this intermediate data engineering course. Learn advanced data transformation, workflow orchestration, and data pipeline optimization using Dataflow for Apache Beam and Dataproc Serverless for Apache Spark. Gain hands-on experience in data quality, monitoring, and alerting to ensure reliable and scalable ETL/ELT pipelines. Ideal for learners with basic knowledge of SQL, Python, data warehousing, and Google Cloud Platform (GCP).
DURATION 1.00 Hour

No scheduled classes available at this time.

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Training Insurance Included!

When you organise training, we understand that there is a risk that some people may fall ill, become unavailable.

To mitigate the risk we include training insurance for each delegate enrolled on our public schedule, they are welcome to sit on the same Public class within 6 months at no charge, if the case arises.