Stream data from SQL Server via Change Tracking to MongoDB
Move data from SQL Server via Change Tracking to MongoDB in minutes using Estuary. Stream, batch, or continuously sync data with control over latency from sub-second to batch.
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- 200+Of connectors
- 5500+Active users
- <100msEnd-to-end latency
- 7+GB/secSingle dataflow
How to integrate SQL Server via Change Tracking with MongoDB in 3 simple steps
Connect SQL Server via Change Tracking as your data source
Set up a source connector for SQL Server via Change Tracking 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.
Configure MongoDB 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 MongoDB.
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 via Change Tracking connector details
The SQL Server Change Tracking connector continuously captures updates from Microsoft SQL Server tables into Estuary collections using native Change Tracking. It delivers real-time replication with lower source database overhead by tracking primary key changes rather than full row logs. This option is ideal when all tables have primary keys and when computed columns need to be captured, which traditional CDC cannot support.
- Uses SQL Server Change Tracking for real-time change capture
- Lower storage overhead compared to log-based CDC
- Supports computed columns and computed primary keys
- Requires primary keys and CT enabled at database and table levels
- Configurable retention period to prevent re-sync gaps
- Works across self-hosted SQL Server, Azure SQL, Amazon RDS, and Google Cloud SQL

MongoDB connector details
- Merge-based materializations to sync only what's changed
- Low-latency delivery from streaming and batch sources
- Automatic schema alignment so your destination matches your pipeline's evolving data
- Flexible deployment models, including BYOC and hybrid for enterprise governance
- Unified streaming + batch outputs in a single tool
- End-to-end security and compliance for sensitive data workloads
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 via Change Tracking to MongoDB pricing estimate
Estimated monthly cost to move 800 GB from SQL Server via Change Tracking to MongoDB is approximately $1,000.
Data moved
Choose how much data you want to move from SQL Server via Change Tracking to MongoDB each month.
GB
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Why pay more?
Move the same data for a fraction of the cost.



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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 via Change Tracking to MongoDB 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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