Build Batch Data Pipelines on Google Cloud

Google Cloud treats a batch pipeline as a system with scheduling, quality and operational needs. The course moves from choosing batch processing to building and monitoring the workflow. It suits learners who want to explain why a pipeline’s timing and failure behavior matter alongside its transformations.

What you will learn

• Decide when batch processing fits a use case.
• Design ingestion and transformation stages.
• Connect data-quality controls with orchestration and monitoring.

Who this suits

Intermediate learners ready to work with cloud data pipelines; review the initial labs and platform setup before enrolling.

Before you enroll

Check the software versions, cloud-account requirements and lab access terms before enrolling. Running cloud resources can incur separate charges. Use fictional or public sample data, and evaluate reliability, permissions and data quality alongside whether a pipeline runs.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Design a fictional overnight pipeline and explain how it handles a late source file, invalid records and a repeated run.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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