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Cluster-to-Cluster Sync. Continuous data sync between separate MongoDB clusters.

Continuously synchronize data between MongoDB clusters in the same or hybrid environments, including Atlas, private cloud, on-premises, and at the edge.

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Illustration of databases syncing

Expand innovation beyond the limits of a single cluster

Support all your multi-environment clusters by synchronizing data in Atlas with on-premises and edge data. Build innovative use cases that take full advantage of cluster-to-cluster data synchronization. Simplify data migrations, improve development lifecycles, or spin off dedicated analytics environments.

Take full control of your data synchronization

Start, stop, pause, and resume your cluster synchronization as needed. The synchronization process can be paused for hours or even days, depending on the size of your MongoDB oplog. This includes the ability to pick up where you left off in the event of a hardware failure or system error.

Illustration of databases with the option to start, stop and pause synchronization
Illustration of editable and read-only database options for cluster-to-cluster synchronization

Enable data movement with reliability

The only MongoDB solution for enabling long-running cross-cluster sync. Seamlessly sync real-time data across your clusters to create target read-only clusters.


Feature overview

"The ability to leverage Cluster-to-Cluster Sync for our MongoDB-based travel applications will greatly improve many facets of our software lifecycle and further increase our high levels of geographic availability for our customers."
Sylvain Roy
SVP of Technology Platforms and Engineering at Amadeus

Learn more about MongoDB’s Cluster-to-Cluster Sync

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FAQ

Common questions and answers about Cluster-to-Cluster Sync
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Get started with Cluster-to-Cluster Sync

Continuously synchronize data between MongoDB clusters in the same or hybrid environments including Atlas, private cloud, on-premises, and edge clusters.
Try NowRead the docs
CLUSTER-TO-CLUSTER SYNC USE CASES
  • Data migrations
  • Development lifecycles
  • Analytics clusters
  • Audits and compliance
  • Cloud stressed exit
  • Moving data to the edge