Build Batch Data Pipelines on Google Cloud

Overview

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

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.

Syllabus

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.

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.

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.

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.