Google BigQuery vs Google Cloud SQL: What are the differences?
Developers describe Google BigQuery as "Analyze terabytes of data in seconds". Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.. On the other hand, Google Cloud SQL is detailed as "Store and manage data using a fully-managed, relational MySQL database". MySQL databases deployed in the cloud without a fuss. Google Cloud Platform provides you with powerful databases that run fast, don’t run out of space and give your application the redundant, reliable storage it needs.
Google BigQuery belongs to "Big Data as a Service" category of the tech stack, while Google Cloud SQL can be primarily classified under "SQL Database as a Service".
Some of the features offered by Google BigQuery are:
- All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.
- Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.
- Affordable big data- The first Terabyte of data processed each month is free.
On the other hand, Google Cloud SQL provides the following key features:
- Familiar Infrastructure
- Flexible Charging
- Security, Availability, Durability
"High Performance" is the top reason why over 17 developers like Google BigQuery, while over 12 developers mention "Fully managed" as the leading cause for choosing Google Cloud SQL.
According to the StackShare community, Google BigQuery has a broader approval, being mentioned in 160 company stacks & 41 developers stacks; compared to Google Cloud SQL, which is listed in 73 company stacks and 28 developer stacks.
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