Design data processing systems — Professional Data Engineer

The official GCP documentation our Design data processing systems practice questions are cited to. Review the primary sources, then practise.

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