● Covered by 1 source · 1 reportMedium impact1 positive
Google Cloud introduces cross-cloud caching for BigQuery to reduce data transfer costs
Key points
- Cross-cloud caching for BigQuery is now in preview.
- Caches frequently accessed data locally in Google Cloud.
- Reduces cross-cloud data transfer costs and accelerates queries.
- Supports Apache Iceberg columnar compression.
New Cross-Cloud Caching Feature
Google Cloud has announced a preview of cross-cloud caching for its BigQuery service. This new feature is designed to transparently accelerate cross-cloud queries and reduce remote data transfer costs by caching frequently accessed data locally within Google Cloud.
Enhancing the Borderless Lakehouse
The introduction of cross-cloud caching builds upon Google Cloud's borderless Lakehouse initiative, which allows organizations to query and activate data in place across multiple clouds. The borderless Lakehouse uses the Apache Iceberg REST catalog specification and offers Partner Cross-Cloud Interconnect for high-bandwidth private links between cloud providers.
Cost and Performance Benefits
By combining cross-cloud caching with standard Iceberg columnar compression, Google Cloud states that users may only need to transfer under 5% of the data processed across clouds. This reduction in data transfer is intended to lower the Total Cost of Ownership (TCO) for cross-cloud analytics and AI workloads at an enterprise scale. Additionally, BigQuery cross-cloud connections are also available in preview for querying non-Iceberg data in other clouds.
Operational Simplicity
The cross-cloud caching feature is designed to operate without requiring manual configuration or storage management from the user. It aims to meet enterprise performance and security requirements while simplifying the process of accelerating queries across cloud environments.
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Reporting from
Google Cloud has launched a preview of cross-cloud caching for BigQuery, allowing frequently accessed data from other clouds to be cached locally in Google Cloud. This feature aims to reduce data transfer costs and accelerate cross-cloud queries for users of the borderless Lakehouse architecture.