GCP · objective · 20% of the exam
Design data processing systems
Exam objectives are the official skill domains Professional Data Engineer tests. Below are the official GCP docs our questions on this objective cite — review them, then practise.
Sign in to track your progress on this objective.
Official references for this objective
-
Google Cloud — Dataflow: streaming analytics | Google Cloud
complex data transformations using Apache Beam's unified model, all on serverless Google Cloud infrastructure
-
Google Cloud — Bigtable: fast, flexible NoSQL | Google Cloud
Bigtable Data Boost enables users to run analytical queries, batch ETL processes, train ML models, or export data faster without affecting transactional workloads
-
Google Cloud — Pub/Sub for Application & Data Integration | Google Cloud
Dataflow supports reliable, expressive, exactly-once processing of Pub/Sub streams. No provisioning, auto-everything Pub/Sub does not have shards or partitions.
-
Google Cloud — Spanner: Always-on, virtually unlimited scale database | Google Cloud
Scalability Vertical (use a bigger machine) Horizontal (add more machines) Horizontal
-
Google Cloud — Cloud Data Fusion | Google Cloud
Visual point-and-click interface enabling code-free deployment of ETL/ELT data pipelines
-
Google Cloud — Cloud SQL for MySQL, PostgreSQL, and SQL Server | Google Cloud
Enterprise Plus edition offers a 99.99% availability SLA for the most demanding workloads