Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam!
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Prepare and test your skills
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A retail company ingests terabytes of raw transactional and customer activity data daily into Cloud Storage. Business analysts need an intuitive, code-free way to visually explore, clean, and profile ad-hoc datasets before reporting. Concurrently, the data engineering team needs to run automated, large-scale transformations across petabytes of historical data with minimal operational overhead and infrastructure management.
Which architecture should you recommend to meet these requirements?
An Extract, Load, Transform (ELT) architecture leverages the massive parallel processing power of BigQuery by loading raw data directly into the data warehouse and executing transformations using standard SQL. Combined with Cloud Dataprep, it provides an intelligent, visual data preparation interface for analysts that requires zero infrastructure management.
This solution cleanly aligns workload types to native Google Cloud services: visual, interactive profiling in Cloud Dataprep and ultra-fast, serverless analytical transformations in BigQuery, achieving maximum operational efficiency.
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