Difference between federated tables and native tables?
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Federated tables and native tables in BigQuery serve
different purposes for accessing and analyzing data. Here's a detailed explanation of their differences, use cases, and characteristics:
🔹 Native Tables
Definition: Native tables are physically stored within BigQuery. The data is loaded into BigQuery storage from external sources such as CSVs, JSON files, or other datasets.
Performance: Queries on native tables are generally faster and more optimized, since BigQuery can manage partitioning, clustering, and caching directly.
Storage Cost: You are charged for storing the data in BigQuery, in addition to query costs.
Features Supported:
Supports advanced features like partitioning, clustering, materialized views, and time-travel.
Query caching is available, which improves performance and reduces costs for repeated queries.
🔹 Federated Tables
Definition: Federated tables allow BigQuery to query external data sources directly, without loading them into BigQuery storage.
Supported Sources:
Google Cloud Storage (GCS)
Google Sheets
Cloud SQL
Bigtable
Performance: Generally slower compared to native tables because the data must be accessed externally at query time.
Storage Cost: No storage cost in BigQuery, since the data isn't stored there. You only pay for the amount of data read during queries.
Limitations:
No support for partitioning or clustering.
Limited SQL feature support.
Higher latency and potential for slower queries due to external data retrieval.
✅ When to Use
Use native tables when performance, advanced features, and lower latency are important.
Use federated tables for ad hoc analysis, accessing live or frequently changing data, or to avoid duplication across systems.
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