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Solution

Migrate without downtime

Most migrations do not fail on the copy. They fail at the cutover, when nobody can prove the two sides agree and there is no way back. This is the approach that removes both problems.

Talk to an engineerSee how Dsync works

Where production migrations actually go wrong

The usual approach

  • Dump and restore needs a maintenance window measured in hours or days
  • A failure mid-copy means starting the whole thing again
  • Off-the-shelf CDC is hard to orchestrate against the initial snapshot
  • Custom ETL takes months and is abandoned after the migration
  • Cutover is a leap of faith with no verification and no back-out

With Adiom

  • The application keeps serving traffic throughout
  • The copy resumes from its checkpoint instead of restarting
  • CDC takes over from the snapshot without a gap
  • One command instead of a codebase you then own
  • Evidence the two sides match, and a back-out that is just a reversal

How a migration runs

dsync $SOURCE $DESTINATION
  1. 01

    Copy

    • Parallelized across the dataset, resumable at any point
    • 10–100× faster than scripts or general-purpose ETL
    • Source impact bounded so production stays healthy
  2. 02

    Catch up

    • CDC streams changes while the copy completes
    • Lag stays in seconds, not tens of minutes
    • Both sides converge and stay converged
  3. 03

    Cut over

    • Integrity verified before you commit to the switch
    • No downtime required to make the change
    • Reverse direction if anything looks wrong

See it running

What a migration actually looks like

Not a mockup. The command line, the web UI and the integrity check, from a real run.

The dsync command-line interface during a migration, showing per-collection progress and throughput.
Command line — progress, throughput and lag per namespace, live.
The dsync web interface showing a running migration with source, destination and status.
Web UI — the same run, for people who are not in a terminal.
The dsync data integrity check comparing record counts and contents between source and destination.
Integrity check — evidence the two sides agree, before you cut over.

Supported paths

Connectors work in both directions, so most source and target pairs are supported.

Proof

“We utilized dsync to handle the migration of two production databases from Cosmos DB to MongoDB. The tool was incredibly efficient, allowing us to bypass the manual overhead of writing custom migration logic. It delivered exactly what was promised: speed, reliability, and risk elimination. I highly recommend it to any team looking to streamline their data synchronization.”
Senior EngineerMultinational retail and finance company
100 TB
HBase to MongoDB in under 48 hours, source serving live traffic
10–100×
Faster than DIY scripts and general-purpose ETL
Zero
Downtime required for the cutover

More on migrating in production

Book a free migration assessment

Thirty minutes. We review source and target, timeline, cutover constraints, and what downtime is realistic. If Dsync is the wrong tool we will say so.

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