Google Cloud Professional Data Engineer Exam

When building a streaming data analytics pipeline, which Google Cloud products should you select?

1. Cloud Storage, Dataflow, BigQuery

2. Pub/Sub, Dataprep, BigQuery

3. Pub/Sub, Dataflow, BigQuery

4. Cloud Storage, Dataprep, AlloyDB

The correct answer is: Pub/Sub, Dataflow, BigQuery

The selection of Pub/Sub, Dataflow, and BigQuery for a streaming data analytics pipeline is particularly fitting due to the specific functionalities these services provide. Pub/Sub serves as a robust messaging service that enables asynchronous communication. It is designed to handle real-time data ingestion effectively, making it ideal for collecting streaming data from various sources, such as user interactions or IoT devices. This allows for a scalable and efficient way to process data as it arrives. Dataflow acts as a unified stream and batch data processing service. It enables developers to build and execute data processing pipelines that can handle real-time data streaming. With Dataflow, you can perform transformations and enrichments on the streaming data in real time, ensuring that only high-quality data reaches the next stage of your analytics pipeline. BigQuery is a highly scalable serverless data warehouse that facilitates querying vast amounts of data quickly. It is perfectly suited for analyzing the results of streaming data, allowing users to generate insights and reports based on the data processed through Pub/Sub and Dataflow. In summary, the combination of these three tools provides a cohesive and powerful solution for building a streaming data analytics pipeline. Pub/Sub takes care of the real-time messaging, Dataflow processes and transforms the data dynamically, and BigQuery