PDE Designing Data Processing Systems Practice Question
You are designing a data processing architecture on Google Cloud. You need to ingest data from multiple sources, including streaming events and batch files, and process them to produce a unified dataset for analytics. The solution must support both real-time and historical processing with the same codebase, and be able to handle late-arriving data. Which two Google Cloud services should you use together to achieve this? (Choose two.)
⚠ Common exam trap
The trap here is assuming that any data processing service can handle both batch and streaming with the same code, or that orchestration services like Cloud Composer can replace a stream processing engine.
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
✓
Cloud Pub/Sub
To achieve unified batch and stream processing with the same codebase and handle late data, you need a processing engine that supports Apache Beam, such as Cloud Dataflow, and a scalable ingestion service for streaming data, such as Cloud Pub/Sub. Dataflow's Beam model allows you to write one pipeline that works for both batch and streaming, with built-in support for event-time windowing and late data. Pub/Sub provides reliable, scalable ingestion for streaming events.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Composer
Why it's wrong here
Cloud Composer is a workflow orchestration service. It can schedule and manage batch jobs, but it is not designed for real-time stream processing or handling late-arriving data. It does not provide a unified programming model for batch and streaming. Therefore, it does not meet the core processing requirements of this scenario.
- ✓
Cloud Pub/Sub
Why this is correct
Cloud Pub/Sub is a scalable, serverless messaging service for ingesting streaming data. It can handle high-throughput event streams and integrate seamlessly with Dataflow. Pub/Sub ensures reliable delivery and can buffer messages, which is essential for ingesting streaming events into a unified pipeline that also processes batch data.
- ✓
Cloud Dataflow
Why this is correct
Cloud Dataflow is a fully managed service for executing Apache Beam pipelines. Beam provides a unified programming model that works for both batch and streaming data, allowing you to use the same code for real-time and historical processing. Dataflow supports event-time processing and handling of late data through triggers and allowed lateness, making it ideal for this scenario.
- ✗
Cloud Dataproc
Why it's wrong here
Cloud Dataproc is a managed Hadoop and Spark service. While it can process batch and micro-batch data, it does not provide a unified codebase for both streaming and batch with the same API as Apache Beam. It also requires cluster management, which adds operational overhead. Dataproc is not the best fit for a unified real-time and batch processing architecture.
- ✗
BigQuery
Why it's wrong here
BigQuery is a serverless data warehouse for analytics. It can store and analyze the unified dataset, but it is not a data processing service for ingesting and transforming streaming and batch data. While BigQuery can be a sink for the processed data, it does not provide the processing capabilities required to handle late-arriving data and unify codebases.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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