Matching a workload to the right storage or database service
For the Associate Data Practitioner exam, you must match a data scenario to the right Google Cloud storage service based on data structure, access pattern, and scale rather than just familiarity. The core decision hinges on whether data is structured or unstructured, needs SQL analytics, requires transactional consistency, or must serve high-throughput operational workloads.
1 · Learn the must-know
- Cloud Storage is for unstructured/semi-structured data (files, images, backups, staging area for ingestion pipelines) using an object storage model, not a database.
- BigQuery is the choice for large-scale structured/semi-structured analytical (OLAP) workloads where you need SQL queries over massive datasets, not for low-latency transactional updates.
- Cloud SQL is a managed relational database (MySQL, PostgreSQL, SQL Server) suited for traditional OLTP workloads with moderate scale and standard SQL compatibility needs.
- Spanner is chosen when you need a globally distributed, horizontally scalable relational database with strong consistency and high availability, typically for large enterprise transactional systems exceeding Cloud SQL's scale.
- Firestore is a NoSQL document database ideal for hierarchical/semi-structured JSON-like data with real-time sync, commonly used for mobile/web app backends.
- Bigtable is a NoSQL wide-column store built for very high-throughput, low-latency workloads with massive scale (e.g., time-series, IoT, or analytical data requiring single-digit millisecond access), not for complex ad hoc SQL queries.
2 · Check your understanding
An IoT platform ingests sensor readings from millions of devices, writing tens of thousands of rows per second. Applications need single-digit millisecond lookups of the latest readings for a given device using a key composed of device ID and timestamp, but no SQL joins across entities are required. Which storage solution best fits this workload?
What you have tried across GCP ADP's objectives, not a readiness score.
Data Preparation and Ingestion~30% of the exam0 of 8 tried
Data Analysis and Presentation~27% of the exam0 of 12 tried
Data Pipeline Orchestration~18% of the exam0 of 9 tried
Data Management~25% of the exam0 of 12 tried
3 · Keep going
Ready for more? Take a weighted mock or try free practice questions.