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A large retail chain is undergoing a digital transformation to enhance its customer experience by personalizing recommendations and streamlining its supply chain. They need to analyze massive datasets from online sales, in-store purchases, and inventory levels in real-time to make informed business decisions. Which Google Cloud solution combination would best support their need for real-time analytics and business intelligence on petabyte-scale data?
- ACloud SQL and Dataflow
- BCloud Storage and Compute Engine
- CBigQuery and Looker
- DCloud Spanner and Pub/Sub
Show answer & explanationAnswer & explanation
Correct answer: C. BigQuery and Looker
BigQuery is a highly scalable, serverless data warehouse ideal for petabyte-scale analytics, while Looker provides a modern business intelligence platform for data exploration and visualization. This combination perfectly addresses the need for real-time analytics and informed business decisions on large datasets.
Why the other options are wrong
- A. Cloud SQL is a relational database and Dataflow is for stream/batch processing, but they don't offer the combined BI and petabyte-scale data warehousing of BigQuery and Looker.
- B. Cloud Storage is for object storage and Compute Engine is for virtual machines; this combination is not optimized for real-time, petabyte-scale analytics and BI.
- D. Cloud Spanner is a globally distributed database and Pub/Sub is a messaging service; neither is primarily a BI or petabyte-scale data warehousing solution.
BigQuery & Looker for BI
BigQuery is a serverless data warehouse for large-scale analytics, and Looker is a business intelligence platform for data exploration and visualization.
- BigQuery handles petabytes of data.
- Looker provides interactive dashboards and reports.
- Together, they enable real-time insights and data-driven decisions.
Memory trick: BigQuery + Looker = Big Insights, Clearer Picture.