Building Batch Data Pipelines on Google Cloud
Data teams are drowning in manual pipeline maintenance—this course cuts through the noise by teaching you Google Cloud’s native batch processing tools. You’ll move from scattered scripts to enterprise-grade pipelines that scale automatically, handle failures gracefully, and cost a fraction of legacy systems.
AIU.ac Verdict: Ideal for data engineers and cloud architects who need to build reliable, scalable batch workflows without reinventing the wheel. The 2h 20m duration is tight—you’ll get hands-on labs but limited time for deep customisation scenarios.
What This Course Covers
You’ll work directly with Google Cloud Dataflow, the managed Apache Beam service that abstracts infrastructure complexity. The course covers pipeline design patterns, data transformation logic, error handling, and cost optimisation—all grounded in real-world ETL scenarios. Expect practical labs using Cloud Storage, BigQuery, and Pub/Sub integration points.
The curriculum bridges the gap between local development and production deployment. You’ll learn scheduling strategies (Cloud Scheduler), monitoring pipelines in real time, and troubleshooting common bottlenecks. By the end, you’ll understand when to choose batch over streaming, how to structure transforms for reusability, and how Google’s infrastructure handles your workloads at scale.
Who Is This Course For?
Ideal for:
- Data Engineers: Building or migrating ETL pipelines to cloud; need hands-on Dataflow experience without months of trial-and-error.
- Cloud Architects: Designing data platforms for enterprise clients; need to understand Google Cloud’s batch processing capabilities and cost models.
- Analytics Engineers: Moving from SQL-only workflows to orchestrated pipelines; want to understand Dataflow before committing to it in production.
May not suit:
- Streaming-First Engineers: If your use case is real-time event processing, this batch-focused course won’t address Pub/Sub or Dataflow streaming deeply enough.
- Complete Cloud Beginners: Assumes familiarity with GCP basics (IAM, storage concepts, SQL). You’ll struggle without prior cloud exposure.
Frequently Asked Questions
How long does Building Batch Data Pipelines on Google Cloud take?
2 hours 20 minutes of video content. Plan 4–5 hours total including hands-on labs and sandbox exercises.
Do I need GCP experience before starting?
Yes. You should be comfortable with Cloud Storage, BigQuery basics, and IAM concepts. This isn’t an introductory GCP course.
Will I get hands-on practice?
Absolutely. Pluralsight’s sandbox environment lets you build and test pipelines live without setting up your own GCP project.
Is this course suitable for Apache Beam learners?
Yes—Dataflow runs Beam under the hood. You’ll learn Beam patterns in a managed, Google-native context, which is ideal for production work.
Course by Google Cloud on Pluralsight. Duration: 2h 20m. Last verified by AIU.ac: March 2026.
