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Build Batch Data Pipelines on Google Cloud

IntermediateSkill Level

Updated 05/2026

Explore streaming data architectures on Google Cloud with Pub/Sub, Managed Kafka, Dataflow, and BigQuery for real-time data processing.

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2 hr 6 min

50 Exercises

2,700 XP

Statement of Accomplishment

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Course Description

This course teaches you to design, build, and operate batch data pipelines on Google Cloud. Topics include large-scale data transformations with Dataflow and Serverless Spark, batch data validation and cleansing, schema evolution, error handling, and pipeline orchestration with Cloud Composer.

Prerequisites

There are no prerequisites for this course

1

When to choose batch data pipelines

You will learn the critical role of a data engineer in developing and maintaining batch data pipelines, understand their core components and lifecycle, and analyze common challenges in batch data processing. You'll also identify key Google Cloud services that address these challenges.

Batch data pipelines and their use cases

Processing and common challenges

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2

Design and build batch data pipelines

You will design scalable batch data pipelines for high-volume data ingestion and transformation. You'll also optimize batch jobs for high throughput and cost-efficiency using various resource management and performance tuning techniques.

Design batch pipelines

Large scale data transformations

Dataflow and Serverless for Apache Spark

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Data connections and orchestration

Execute an Apache Spark pipeline

Optimize batch pipeline performance

Use Serverless for Apache Spark to Load BigQuery

Build a Simple Batch Pipeline with Dataflow Job Builder UI

3

Control data quality in batch data pipelines

You will develop data validation rules and cleansing logic to ensure data quality within batch pipelines. You'll also implement strategies for managing schema evolution and performing data deduplication in large datasets.

Batch data validation and cleansing

Log and analyze errors

Schema evolution for batch pipelines

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Data integrity and duplication

Deduplication with Serverless for Apache Spark

Deduplication with Dataflow

Validate Data Quality for a Batch Data Pipeline using Serverless for Apache Spark

4

Orchestrate and monitor batch data pipelines

You will orchestrate complex batch data pipeline workflows for efficient scheduling and lineage tracking. You'll also implement robust error handling, monitoring, and observability for batch data pipelines.

Orchestration for batch processing

Cloud Composer

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Unified observability

Alerts and troubleshooting

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Visual pipeline management

Building Batch Pipelines in Cloud Data Fusion

Congratulations: Course summary

Build Batch Data Pipelines on Google Cloud

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