Professional Data EngineerDesigning data processing systemsEasy
A data analytics team needs to perform complex, ad-hoc queries on petabytes of historical sales data. The queries often involve large joins and aggregations across multiple tables. The solution must be fully managed, highly scalable, and cost-effective, with pricing based on actual query usage rather than provisioned compute resources. Which Google Cloud data warehousing service is most appropriate for this scenario?
- ABigQuery
- BDataproc
- CCloud Spanner
- DCloud SQL
Show answer & explanationAnswer & explanation
Correct answer: A. BigQuery
BigQuery is a fully managed, serverless enterprise data warehouse designed for petabyte-scale analytics. Its columnar storage and distributed query engine are optimized for complex analytical queries (joins, aggregations) and its pricing model is based on data scanned during queries, making it cost-effective for ad-hoc analysis.
Why the other options are wrong
- B. Dataproc is a managed Apache Hadoop and Spark service, requiring cluster management and typically priced by instance hours, not ideally suited for ad-hoc serverless analytics.
- C. Cloud Spanner is a globally distributed, strongly consistent relational database, primarily for transactional workloads requiring high availability and consistency, not cost-effective ad-hoc analytics.
- D. Cloud SQL is a regional relational database for transactional workloads, not petabyte-scale analytical queries.
BigQuery
Google Cloud's fully managed, serverless, and highly scalable enterprise data warehouse for analytics, designed for petabyte-scale data and complex SQL queries.
- Serverless architecture, no infrastructure to manage.
- Columnar storage optimized for analytical queries.
- Scales automatically to petabytes of data and thousands of queries.
- Cost-effective with pricing based on data scanned per query.
Memory trick: Big Query for Big Questions.