Google Professional Machine Learning Engineer

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You are tasked with designing a data analytics solution for a global retail company that generates petabytes of transaction data daily. The solution must support complex SQL queries for real-time analytics, be cost-effective, and scale seamlessly with the company's growth. Which Google Cloud service is the BEST choice for storing and querying this large dataset efficiently, and why? Choose the most appropriate option.

Real Exam

Bigtable: A NoSQL database service optimized for large-scale, low-latency workloads. While it handles massive amounts of data, it does not support SQL queries directly, making it unsuitable for the requirement of complex SQL queries.

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Dataproc: A fully-managed service for running Apache Spark and Apache Hadoop clusters. It is designed for data processing and analytics but lacks the native SQL query support needed for real-time analytics on large datasets.

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BigQuery: A fully-managed, serverless data warehouse service designed for storing and querying massive datasets quickly using SQL. It supports real-time data analysis, can handle petabytes of data, and scales automatically, making it ideal for big data analytics in a cost-effective manner.

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Cloud Spanner: A globally distributed, horizontally scalable database service that supports SQL. While it offers high availability and strong consistency, it is optimized for transactional workloads rather than large-scale analytics, potentially leading to higher costs for analytics use cases.

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