Live Data Migration to AWS Without Interrupting the Business
US enterprise client, $100M+ annual revenue. Multi-terabyte production data migrated from self-hosted infrastructure to AWS with no downtime for live operations.
A large US business was moving a production data estate from self-hosted infrastructure to AWS. The estate contained several terabytes of operational data across relational and NoSQL systems. It powered live business processes, so a conventional maintenance window or stop-and-copy migration was not an option.
The target platform used Amazon Aurora for relational workloads, Amazon DocumentDB and DynamoDB for document and key-value data, and ElastiCache for low-latency access patterns. The objective was to create a managed, scalable foundation without sacrificing data correctness or continuity of service.
Moving historical data is only part of a live migration. While a bulk transfer is running, the source system keeps accepting writes. Every new record, update, and delete must reach the target in the right order, without duplication or silent loss.
The migration also had to account for different data models and consistency characteristics across relational and NoSQL stores. A final row-count comparison would not have been sufficient: it could confirm volume while missing changed records, sequencing errors, or data that arrived out of order during the live cutover.
- Designed the migration from self-hosted systems into AWS-managed services: Aurora, DocumentDB, DynamoDB, and ElastiCache.
- Combined an initial offline bulk migration with online change data capture (CDC), so production activity continued while the target was brought up to date.
- Handled relational and non-relational data as separate migration streams, with transformations and loading strategies appropriate to each target service.
- Used AWS services and serverless components to run the migration workflow with operational scalability and without introducing a new long-lived migration platform to manage.
- Validated the offline transfer using completeness and consistency checks across source and target datasets.
- Added real-time validation to the CDC path using sequence numbers, allowing the team to detect gaps, duplicates, or out-of-order changes as live data flowed to AWS.
- Reconciled the source and target before cutover, then transitioned workloads only after the live replication and validation signals showed the target was current.
| Area | Outcome |
|---|---|
| Migration scope | Several terabytes of live relational and NoSQL production data |
| Business continuity | No downtime during the live migration |
| Target platform | AWS managed services: Aurora, DocumentDB, DynamoDB, and ElastiCache |
| Data integrity | Offline and real-time validation, including sequence-aware CDC checks |
| Operating model | Serverless migration components designed to scale with the workload |
The client moved from self-hosted infrastructure to an AWS-based data platform while keeping the business operational throughout the migration. The validation design made correctness observable during the move, rather than treating it as a post-cutover assumption.
Live data migrations are production engineering work, not a file transfer. A reliable migration needs a plan for data modeling, backfill, ongoing changes, validation, cutover, and rollback before the first dataset moves.
Norviq AI can support migrations where systems must remain live, including:
- Self-hosted relational databases to AWS Aurora or other managed database platforms
- Document and key-value workloads to DocumentDB and DynamoDB
- CDC-based migrations where an initial backfill must be reconciled with ongoing writes
- Data correctness validation for both historical and in-flight changes
- Migration planning, cutover runbooks, and post-cutover reconciliation
CRM migrations between Salesforce, HubSpot, Pipedrive, Microsoft Dynamics 365, Zoho CRM, and other platforms have a different surface area but the same core discipline: map the data model before moving it, preserve relationships and history, clean the records that should not follow the business, validate the target, and make the cutover reversible.
Typical CRM migration work includes contact, company, deal, activity, ownership, custom-object, and integration migration; deduplication and field normalization; record-count and relationship validation; and staged cutovers that keep sales and support teams working. This applies whether consolidating systems after an acquisition, replacing an underused CRM, or preparing a CRM for new sales and support workflows.
Before choosing tools or setting a cutover date, establish the facts: source and target systems, data volume and change rate, correctness requirements, dependencies, acceptable downtime, and rollback path.
Norviq AI provides a senior-engineer-led migration risk review that produces a practical migration plan, validation strategy, and implementation scope.
Plan a Migration Before the Cutover Date.
Start with a senior-engineer-led migration risk review: a practical migration plan, validation strategy, and implementation scope.