Question: Cloud Dataflow is a tool for developing and executing a wide range of data processing patterns on very large datasets. Which of these examples aligns with what Cloud Dataflow can do?
- Process queries written in structured query language (SQL).
- Perform the transformations in “extract, transform, and load (ETL)."
- Scale without downtime.
- Develop apps faster and easier with cloud backend services.
Explanation
Cloud Dataflow is used to build and run data processing pipelines for both batch and streaming data. It applies transforms to data as it moves through a pipeline. This fits the processing stage of a big data workflow, where raw data is cleaned, reshaped, grouped, or enriched. The service uses the Apache Beam model, which supports reusable processing logic across different execution patterns.
Why the other options are incorrect
SQL query processing describes BigQuery, not Cloud Dataflow.
Scaling without downtime aligns more closely with managed database or container scaling value, not Dataflow’s main processing role.
Cloud backend services describes Firebase, not Cloud Dataflow.
Source for verification
https://cloud.google.com/dataflow/docs/overview
https://cloud.google.com/dataflow/docs/concepts/beam-programming-model
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