PDE Ingesting and Processing the Data Practice Question
A company wants to move data from an on-premises MySQL database to BigQuery for analytics. They need to capture all changes (inserts, updates, deletes) in near real-time and also perform an initial historical load. Which approach meets these requirements with minimal operational overhead?
⚠ Common exam trap
PDE often tests the difference between batch and streaming ingestion; candidates incorrectly choose Dataflow or custom scripts for CDC, not realizing Datastream is the fully managed, serverless CDC service designed for minimal operational overhead.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Datastream to backfill historical data and then stream CDC changes to BigQuery
Datastream is correct because it provides serverless change data capture (CDC) from MySQL to BigQuery, supporting both historical backfill and continuous replication of inserts, updates, and deletes with minimal operational overhead. It reads the MySQL binary log (binlog) to stream changes in near real-time and can write directly to BigQuery or Cloud Storage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a Dataflow pipeline with a JDBC source to read the entire table periodically
Why it's wrong here
Periodic JDBC reads capture only snapshots, missing deletes and interim updates, and add replication lag; they cannot deliver near real-time change capture. Dataflow with JDBC suits one-off or scheduled bulk extracts, not continuous CDC. The scenario needs change data capture plus historical load, which Datastream provides.
- ✓
Use Datastream to backfill historical data and then stream CDC changes to BigQuery
Why this is correct
Datastream provides serverless change data capture from MySQL, streaming inserts, updates and deletes into BigQuery in near real-time, while its backfill capability performs the initial historical load. This satisfies both requirements without managing replication infrastructure, minimising operational overhead.
- ✗
Use a one-time export to CSV and load into BigQuery, then set up a cron job to export incremental changes
Why it's wrong here
CSV exports plus cron jobs cannot capture deletes and updates reliably, and near real-time change capture is impossible with periodic batch exports. It is tempting because it uses familiar tooling with low setup cost, and would be correct for one-off historical migrations where ongoing change replication is not required.
- ✗
Use Cloud SQL as an intermediary and enable binary logging, then stream to Pub/Sub via a custom connector
Why it's wrong here
Cloud SQL as an intermediary adds an extra managed instance and a custom connector to build and maintain, contradicting the minimal operational overhead requirement. It is tempting because binary logging enables change capture, and would be correct if the source were already Cloud SQL rather than an on-premises MySQL database.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.