Build Streaming Data Pipelines on Google Cloud

Google Cloud introduces streaming use cases before examining the products that support them. Ingestion, processing and serving form the main architectural chain. It is a continuation for learners who want to compare a continuously arriving event stream with a scheduled batch workflow.

What you will learn

• Compare streaming use cases and reference architectures.
• Explain ingestion with Pub/Sub or Kafka.
• Connect Dataflow processing with analytical serving.

Who this suits

Intermediate learners prepared to examine cloud streaming architectures and the associated product exercises.

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.

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Put it into practice

Map a fictional event stream and define how duplicate, delayed and malformed events should be treated before choosing services.

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.

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