Building Batch Data Pipelines on Google Cloud

• The course is composed of 6 modules and is estimated to take approximately 17 hours, with some sources mentioning up to 20 hours total.
• Most learners complete it in about 2 weeks, studying around 10 hours per week.
• It’s self-paced, enabling you to progress faster or slower based on your schedule.

• The course is rated Intermediate and requires some related experience, rather than being suitable for absolute beginners.
• Prerequisites include experience with data modeling, ETL processes, and familiarity with programming languages like Python or Java.

• The course features practical, hands-on labs, particularly in modules on Dataproc, Dataflow, Data Fusion, and Composer.
• As noted in external coverage, these labs simulate real-world batch pipeline workflows on Google Cloud, offering direct experience.
• Learners build pipelines using technologies such as Hadoop on Dataproc, serverless Dataflow, and workflow orchestration via Composer or Data Fusion.

Strengths:

• Provides a solid overview of GCP’s batch data tools and services.
• Lab-based learning helps learners practice without incurring GCP costs.

Limitations:

• Sometimes seen as biased toward Google’s ecosystem—methods and tools drive content more than theoretical depth.
• The certificate is useful, but many learners highlight that successful learning depends on additional hands-on project work beyond the course.

You’ll learn:

• ETL paradigms (EL, ELT, ETL) and when to apply each
• Running Spark on Dataproc and optimizing jobs using Cloud Storage
• Building serverless pipelines with Dataflow (Apache Beam)
• Orchestrating pipelines with Data Fusion and Cloud Composer (Airflow)
• This course is best suited for data engineers, GCP developers, or cloud professionals looking to deepen their data pipeline architecture skills on Google Cloud.