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A company is migrating its data warehouse to BigQuery. They have petabytes of historical data stored in various on-premises databases and flat files. The migration requires ensuring data quality and consistency, transforming data into a BigQuery-compatible schema, and orchestrating complex ETL workflows. Which Google Cloud service is most suitable for building and managing these complex data migration and transformation pipelines?
- ACloud Dataproc for running managed Apache Spark and Hadoop clusters.
- BCloud Functions for event-driven data loading.
- CCloud Data Fusion for its fully managed, GUI-driven ETL/ELT capabilities.
- DData Catalog for metadata management and data discovery.
Show answer & explanationAnswer & explanation
Correct answer: C. Cloud Data Fusion for its fully managed, GUI-driven ETL/ELT capabilities.
Cloud Data Fusion is a fully managed, cloud-native data integration service built on open-source CDAP. It provides a graphical interface for building and managing complex ETL/ELT pipelines, making it ideal for data migration, transformation, and ensuring data quality for a BigQuery data warehouse, especially when dealing with heterogeneous sources and complex transformations.
Why the other options are wrong
- A. Cloud Dataproc manages Spark/Hadoop clusters, which can run ETL jobs, but Data Fusion provides a higher-level, more managed, and GUI-driven approach specifically for data integration pipelines, simplifying development and management.
- B. Cloud Functions are for short-lived, event-driven tasks and are not suitable for orchestrating complex, long-running ETL workflows involving petabytes of data.
- D. Data Catalog is for metadata management and data discovery, not for building and executing data transformation pipelines itself.
Cloud Data Fusion
A fully managed, cloud-native data integration service built on open-source CDAP, providing a graphical interface for building and managing ETL/ELT pipelines.
- Offers a visual, code-free interface for pipeline development.
- Supports a wide range of data sources and sinks.
- Automates data integration tasks, including data cleansing and transformation.
- Ideal for data warehousing and data lake initiatives.
Memory trick: Fusion visually weaves complex data streams.