Google Cloud Professional Data Engineer Exam

What will help businesses to automate data cleanup processes efficiently?

1. Create a Dataflow pipeline

2. Perform cleanup in Dataproc jobs

3. Utilize Google Sheets for organization

4. Implement Cloud Functions scripts

The correct answer is: Create a Dataflow pipeline

Creating a Dataflow pipeline is an efficient approach for automating data cleanup processes as it allows for the processing of large datasets with scalability and ease. Dataflow is designed specifically for building data processing workflows that require real-time or batch data processing. It leverages Apache Beam, which allows developers to write code that can be executed on a managed service, handling various data cleanup tasks such as filtering, transforming, and aggregating data seamlessly. With Dataflow, businesses can set up pipelines that continuously monitor and clean incoming data as it flows into the storage systems, eliminating the need for manual intervention. This leads to a more efficient and reliable data cleanup process, reducing the time and resources spent on maintenance and ensuring that the data is accurate and up-to-date for analysis and reporting. While the other options may provide solutions for data cleanup, they do not offer the same level of automation and scalability that Dataflow provides. For example, Dataproc can be useful for executing batch processing jobs but requires more management overhead to handle cluster setup and scaling. Google Sheets offers organizational capabilities but lacks the ability to process large volumes of data efficiently. Implementing Cloud Functions scripts can automate specific tasks but may not be ideal for larger, ongoing data cleanup processes that require continuous data flow