PDE Ingesting and Processing the Data Practice Question
A retail company wants to analyze point-of-sale transaction data stored in Cloud SQL for PostgreSQL. They need to run complex analytical queries joining this data with data in BigQuery. The data changes frequently, and they want near-real-time access without impacting the production Cloud SQL instance. Which approach should they use?
⚠ Common exam trap
The trap here is choosing federated queries because they seem to provide direct access, but they can impact production and do not synchronize data continuously.
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
✓
Set up a Datastream stream from Cloud SQL to BigQuery
Datastream is a serverless change data capture service that replicates data from Cloud SQL for PostgreSQL to BigQuery in near-real-time without impacting the source database's performance. It reads the write-ahead log and streams changes, enabling up-to-date analytics. Other options either introduce latency, impact production, or require manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export Cloud SQL data to Cloud Storage daily and load into BigQuery
Why it's wrong here
Daily exports introduce significant latency and do not provide near-real-time access. They also require manual or scheduled jobs and may not capture all changes, leading to stale data. This approach does not meet the near-real-time requirement and adds operational overhead for managing exports and loads. It also doesn't leverage CDC.
- ✗
Use BigQuery federated queries to query Cloud SQL directly
Why it's wrong here
Federated queries allow BigQuery to query external data sources like Cloud SQL, but they run the query on the external system, which can impact production performance and do not provide near-real-time synchronization. They are also limited in the amount of data they can handle and are not designed for frequent, high-volume analytical joins. This approach would not meet the requirement to avoid impacting production.
- ✗
Use Cloud Data Fusion to replicate Cloud SQL to BigQuery with a batch pipeline
Why it's wrong here
Cloud Data Fusion can perform batch replication, but batch pipelines run on a schedule and do not provide near-real-time updates. They also may require reading full tables, impacting the source. While Data Fusion supports CDC with additional configuration, the scenario calls for a straightforward near-real-time solution, and Datastream is the purpose-built service for this.
- ✓
Set up a Datastream stream from Cloud SQL to BigQuery
Why this is correct
Datastream provides change data capture (CDC) from Cloud SQL for PostgreSQL to BigQuery, replicating changes in near-real-time. It reads from the source's replication log without impacting production query performance and writes to BigQuery with low latency. This enables complex analytical joins in BigQuery while keeping the production database isolated.
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.