Google Cloud Professional Data Engineer — Question 110

You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
✑ Decoupling producer from consumer
✑ Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
✑ Near real-time SQL query
✑ Maintain at least 2 years of historical data, which will be queried with SQL
Which pipeline should you use to meet these requirements?

Answer options

• A. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
• B. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
• C. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
• D. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

Correct answer: D

Explanation

The correct answer is D because it utilizes Cloud Pub/Sub for decoupling producers and consumers, and Cloud Dataflow enables near real-time processing of JSON data, transforming it into Avro format while writing to Cloud Storage and BigQuery for efficient querying and long-term storage. Options A and B do not meet the near real-time SQL querying requirement effectively, while Option C does not provide direct integration with BigQuery for SQL queries.