When should I use cloud or BigQuery?

When to use cloud BigQuery?

Typically, BigQuery is used at the end of the Big Data ETL pipeline on top of processed data or in scenarios where complex analytical queries to a relational database take several seconds. Because it has a built-in cache, BigQuery works really well in cases where the data does not change often.

What is the difference between BigQuery and cloud SQL?

Whereas BigQuery comes with applications within itself, Cloud SQL doesn't come with any applications. Cloud SQL also has more database security options than BigQuery. The storage space in Cloud SQL depends on the data warehouse being used, while that of Bigquery is equivalent to that of Google cloud storage.

What is the difference between cloud storage and BigQuery?

BigQuery is an analytical engine. You can store images, videos, logs, files, and etc in GCS(Google Cloud Storage) , but BigQuery can't. Show activity on this post. Google BigQuery belongs to "Big Data as a Service" category of the tech stack, while Google Cloud Storage can be primarily classified under "Cloud Storage".

Does BigQuery use cloud storage?

BigQuery supports querying Cloud Storage data in the following formats: Comma-separated values (CSV) JSON (newline-delimited)