Unlock the power of your data in the cloud! Get hands-on with Google Cloud's core data services like BigQuery and Looker to validate your practical skills in data ingestion, analysis, and management, and earn your Associate Data Practitioner certification!
Prepare and test your skills
Prepare and test your skills
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A digital marketing agency collects massive daily volumes of unstructured and semi-structured campaign data from various external platforms. The team wants to ingest all raw data into Google Cloud as quickly as possible without creating preprocessing bottlenecks, preserving the raw logs so they can transform them on demand using SQL-based tools like Dataform.
Which data integration strategy should the agency implement?
The ELT (Extract, Load, Transform) pattern is a modern data integration architecture where data is extracted from source systems, loaded directly in its raw format into a scalable data warehouse such as BigQuery, and then transformed inside the destination engine using SQL, Dataform, or Data Manipulation Language (DML).
Compared to legacy ETL patterns, ELT maximizes the native parallel processing and storage scalability of BigQuery, minimizing data loading latency and providing flexibility for rapidly evolving analytical requirements.
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