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- <100ms Data pipelines
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- 2-5x less than batch ELT



Azure Blob Storage connector details
The Azure Blob Storage connector captures files and structured data from your Azure Blob containers into Flow collections for analysis and integration. It supports multiple formats like CSV, JSON, and Avro, making it ideal for ingesting batch data pipelines from cloud storage.
- Captures data from Azure Blob containers with flexible file format support (CSV, JSON, Avro, Protobuf)
- Supports OAuth2 or Connection String authentication with Azure credentials
- Optional regex filters allow selective ingestion of objects based on file patterns
- Includes advanced parsing options for compression, delimiters, encoding, and schema detection
- Deployment-ready: Works securely within Estuary’s Private and BYOC environments for compliance and governance
💡 Tip: You can optimize sync performance by enabling Ascending Keys for ordered file ingestion when applicable.


HIGH THROUGHPUT
Distributed event-driven architecture enable boundless scaling with exactly-once semantics.

DURABLE REPLICATION
Cloud storage backed CDC w/ heart beats ensures reliability, even if your destination is down.

REAL-TIME INGESTION
Capture and relay every insert, update, and delete in milliseconds.
Real-timehigh throughput
Point a connector and replicate changes from Azure Blob Storage in <100ms. Leverage high-availability, high-throughput Change Data Capture.Or choose from 100s of batch and real-time connectors to move and transform data using ELT and ETL.
- Ensure your Azure Blob Storage insights always reflect the latest data by connecting your databases to Azure Blob Storage with change data capture.
- Or connect critical SaaS apps to Azure Blob Storage with real-time data pipelines.
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Pipelines as fast as Kafka, easy as managed ELT/ETL, cheaper than building it.
Feature Comparison
Estuary | Batch ELT/ETL | DIY Python | KAFKA | |
---|---|---|---|---|
Price | $ | $$-$$$$ | $-$$$$ | $-$$$$ |
Speed | <100ms | 5min+ | Varies | <100ms |
Ease | Analysts can manage | Analysts can manage | Data Engineer | Senior Data Engineer |
Scale |

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