Stream data from SQL Server Batch to Amazon Aurora for Postgres
Move data from SQL Server Batch to Amazon Aurora for Postgres in minutes using Estuary. Stream, batch, or continuously sync data with control over latency from sub-second to batch.
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- 200+Connectors
- 5500+Active users
- <100msEnd-to-end latency
- 7+GB/secSingle dataflow
How to integrate SQL Server Batch with Amazon Aurora for Postgres in 3 simple steps
- 1
Connect SQL Server Batch as your data source
Set up a source connector for SQL Server Batch in minutes. Estuary supports streaming (including CDC where available) and batch data capture through events, incremental syncs, or snapshots — without custom pipelines, agents, or manual configuration.
- 2
Configure Amazon Aurora for Postgres as your destination connector
Estuary supports intelligent schema handling, with schema inference and evolution tools that help align source and destination structures over time. It supports both batch and streaming data movement, reliably delivering data to Amazon Aurora for Postgres.
- 3
Deploy and Monitor Your End-to-End Data Pipeline
Launch your pipeline and monitor it from a single UI. Estuary guarantees exactly-once delivery, handles backfills and replays, and scales with your data — without engineering overhead.

SQL Server Batch connector details
The SQL Server Batch Query connector captures data from SQL Server by periodically executing queries and loading the results into Estuary collections. It is designed for scheduled batch ingestion rather than real-time CDC, making it useful when you need to capture database views, run custom SQL queries, use a read replica, or connect to a SQL Server instance that does not support CDC or Change Tracking.
- Scheduled query-based capture with configurable polling intervals
- Full-refresh and incremental capture using cursor-based queries
- Capture from SQL Server tables and database views
- Custom SQL queries for filtering, aggregations, or capturing specific subsets of data
- Read replica support for reducing load on the primary database
- Flexible cursor options, including update timestamps, creation timestamps, and auto-incrementing IDs
The connector supports self-hosted SQL Server as well as managed deployments such as Amazon RDS and Azure SQL Database.

Amazon Aurora for Postgres connector details
The Amazon Aurora for PostgreSQL materialization connector in Estuary delivers data from Estuary collections into tables in an Aurora PostgreSQL-compatible database. It supports continuous materialization from real-time or batch pipelines and uses standard merge-based updates by default to keep destination tables synchronized.
- Materializes Estuary collections directly into Amazon Aurora for PostgreSQL tables
- Standard merge-based updates by default to keep existing rows synchronized
- Optional delta updates for workloads where changes should be applied incrementally
- Optional hard deletes so records deleted at the source can also be deleted from the destination
- Automatic table creation for tables managed by the connector
- AWS IAM or username/password authentication
- Direct connectivity or SSH tunneling for secure access to Aurora instances
- Configurable SSL modes for encrypted database connections
- Custom SQL during table creation for indexes or other table-level configuration
- Configurable destination schemas and table names for individual bindings
Estuary creates and manages the destination tables used by the materialization. You can configure a default PostgreSQL schema, override it for individual bindings, and optionally run additional SQL when a table is created—for example, to create indexes automatically.
Estuary in action
See how to build end-to-end pipelines using no-code connectors in minutes. Estuary does the rest.
Spend 2-5x less
Estuary customers not only do 4x more. They also spend 2-5x less on ETL and ELT. Estuary's unique ability to mix and match streaming and batch loading has also helped customers save as much as 40% on data warehouse compute costs.

SQL Server Batch to Amazon Aurora for Postgres pricing estimate
Estimated monthly cost to move 800 GB from SQL Server Batch to Amazon Aurora for Postgres is approximately $1,000.
Data moved
Choose how much data you want to move from SQL Server Batch to Amazon Aurora for Postgres each month.
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Frequently Asked Questions
Is this integration suitable for production workloads?
Yes. Estuary pipelines are designed for production use, with exactly-once delivery semantics, automated backfills, and continuous operation at scale.
Can I control where my data runs and is processed?
Yes. Estuary offers multiple deployment options, including fully managed SaaS, private deployments, and bring-your-own-cloud (BYOC). This allows teams to control where their data plane runs and meet security, compliance, and networking requirements. Learn more about Estuary's security and deployment options.
Can I build this SQL Server Batch to Amazon Aurora for Postgres integration manually?
Yes, it's possible to build a manual pipeline using custom scripts, scheduled jobs, or open-source tools. However, manual approaches typically require ongoing maintenance, custom error handling, schema management, and operational overhead. Estuary simplifies this by providing a managed pipeline with built-in reliability, scaling, and monitoring.
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