Unlocking Peak Performance in Zero-Copy Federation: A Guide to Data Locality
Summary
Zero-copy federation enables Salesforce teams to access external data without duplication by leveraging Data 360’s multiple federation methods, focusing on performance through data locality. The key challenge is optimizing query pushdown and avoiding costly data transfers by aligning federation strategies, especially when joining data from multiple sources. By understanding and applying appropriate federation patterns like File Federation, Live Query, or Acceleration, Salesforce professionals can build high-performance integrations and data segmentation pipelines that scale efficiently with large datasets.
Takeaways
- Choose the right federation method based on data size and use case: Live Query, Accelerated, or File Federation.
- Ensure data locality by co-locating data and compute to minimize costly data transfers and improve query speed.
- Avoid mixing local and remote data sources in queries to prevent performance bottlenecks caused by data movement.
- Use caching (Acceleration) for infrequently changing data to enhance repeated query performance with controlled staleness.
- Align federation strategies across related datasets to maintain efficient join pushdowns and avoid massive data pulls.
The ability to access and analyze information from various sources without creating multiple copies is a fundamental goal of modern data architectures. This is the promise of zero-copy federation, a powerful capability that allows you to work with your data where it lives. To unlock its full, game-changing potential, you only need to master a few key principles, the most critical of which is data locality . Data Federation and Data Sharing in Data 360 To achieve a zero-copy architecture, there are two primary modes in Data 360: Data Federation and Data Sharing . Data Federation allows you to access data from external systems (like a data warehouse in Google BigQuery) directly in applications in Data 360. Data Sharing is the reverse, enabling external systems (like Databricks) to securely access data that is managed within Data 360. The core idea behind both is to virtualize your data and act on it without having to move it .