CloudGuild · Blog · Cheat sheets · Lessons · Certifications
Google Professional Data Engineer Exam Question Walkthrough: Low Latency Data Processing
Explore a GCP-PDE exam question on building a low latency data processing pipeline. Understand the right architecture and why it works.
A common stumbling block for candidates is selecting the correct architecture for real-time data processing. In this post, we will analyze a question on building a data processing pipeline that requires low latency and high throughput using Google Cloud services.
The question
A company is planning to build a data processing pipeline that requires low latency and high throughput. They need to ensure that data is processed as soon as it arrives. Which of the following architectures would best meet these needs using Google Cloud services?
A. Cloud Pub/Sub -> Dataflow -> BigQuery
B. Cloud Storage -> Dataproc -> BigQuery
C. Cloud Functions -> Cloud Run -> Firestore
D. Cloud SQL -> Dataflow -> Cloud Storage
Think before you scroll
Before you choose an answer, consider the requirements: low latency and high throughput. Think about how each architecture handles real-time data ingestion and processing. The right choice must efficiently handle data as it arrives.
The answer
The correct option is A. Cloud Pub/Sub -> Dataflow -> BigQuery. This architecture is specifically designed for real-time data streams, ensuring that data is processed immediately as it arrives, which meets the company's needs for low latency and high throughput.
Why the other options lose
B. Cloud Storage -> Dataproc -> BigQuery: This option introduces higher latency. Cloud Storage is not optimized for real-time data ingestion. Dataproc is designed for batch processing, which does not meet the requirement for immediate data processing.
C. Cloud Functions -> Cloud Run -> Firestore: While this option uses serverless components, it is not optimized for high throughput in data processing scenarios. Firestore is primarily a NoSQL document database and is not designed for real-time analytics like BigQuery.
D. Cloud SQL -> Dataflow -> Cloud Storage: This option also introduces latency. Cloud SQL is a relational database that is not suited for high throughput data ingestion. Although Dataflow can process data, the reliance on Cloud SQL limits its ability to handle real-time data effectively.
The concept behind it
The key principle here is the architecture's ability to process data in real-time. Using Cloud Pub/Sub allows for immediate message ingestion, while Dataflow processes that data in real-time. BigQuery then enables fast analytical queries. This combination is essential for systems requiring low latency and high throughput.
Exam trap to remember
Remember, when aiming for low latency and high throughput, choose architectures that prioritize real-time data processing, like Cloud Pub/Sub paired with Dataflow.