BigQuery Omni enables you to run BigQuery analytics directly on data stored in AWS S3 buckets without having to move or copy the data. This provides several benefits:
Reduced Data Movement Costs: Eliminates the need to egress data from AWS, potentially saving significant costs.
Real-Time Analytics: Allows you to query data in AWS S3 in real-time, providing up-to-date insights.
Simplified Architecture: Reduces the complexity of managing data pipelines and ETL processes.
Here's a breakdown of the steps involved in using BigQuery Omni:
Set up a BigQuery Omni connection: This involves configuring the connection between your Google Cloud project and your AWS S3 bucket. This connection establishes the secure link for BigQuery to access the data in AWS S3.
Create external tables: BigQuery external tables are a way to query data residing in external storage systems, such as AWS S3, without having to import the data into BigQuery. This enables you to directly query the data in its original location.
Query the data using BigQuery: Once the external tables are created, you can use standard SQL queries to analyze the data stored in both Cloud Storage and AWS S3, just as if it were native BigQuery data.
Why other options are not suitable:
Option A: BigLake tables are designed for storing large volumes of structured data within BigQuery itself, not for directly querying data in external storage systems.
Option C and D: While the Storage Transfer Service is useful for moving data between cloud providers, it introduces unnecessary data movement and latency if the goal is to simply query the data in its original location.
Key Points:
BigQuery Omni extends BigQuery's capabilities to analyze data stored in other cloud providers, such as AWS.
External tables provide a way to query data in external storage systems without having to import it into BigQuery.
By leveraging BigQuery Omni and external tables, you can efficiently and cost-effectively query data stored in multiple cloud environments using a single tool, BigQuery.