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Data Federation is the technique of querying and integrating data from multiple, distributed sources (databases, data lakes, APIs, etc.) as if they were a single source — without physically moving or copying the data.
🛠️ How It Works (High-Level)
Instead of ETL-ing all data into one place:
- A federated engine sits on top of the data sources
- When a query comes in, it:
- Breaks it into subqueries
- Sends subqueries to each data source
- Aggregates the results
- Returns a unified response
💼 When to Use It
| Use Case | Why Data Federation Helps |
|---|---|
| Data is spread across services/databases | Avoids centralization or duplication |
| Need fast insights across systems | Real-time querying without pre-aggregation |
| Can't move data due to compliance | Leaves data in-place (good for GDPR etc.) |
✅ Pros
- No need to duplicate data
- Real-time / near real-time access
- Flexible for ad-hoc queries
- Good for hybrid/multi-cloud environments
⚠️ Cons
- Performance depends on underlying sources
- Complexity in query planning and optimization
- Limited joins across incompatible systems
- Latency can be high if sources are slow or far apart
🔍 Examples of Data Federation Tools
| Tool / Platform | Description |
|---|---|
| Presto / Trino | Open-source distributed SQL query engine |
| Google BigQuery Federation | Query Cloud SQL, GCS, Sheets from BigQuery |
| Athena Federated Queries | Query multiple AWS sources |
| Denodo | Enterprise data virtualization platform |
| Starburst | Commercial Presto/Trino offering |
References
- ChatGPT