Why Database Migrations Take Months and How to Speed Them Up

Today, where information is unequivocally the cornerstone of everything and a core competitive advantage, our databases often hold our data hostage. 

It's 2025, yet it's not uncommon for a database migration project to span many months, even when the migration is considered like-for-like (e.g., between MongoDB and Cosmos DB with MongoDB API). These long projects take everyone's attention, create uncertainty, and slow down developer productivity. 

In this blog post, we'll discuss the reasons for these delays and provide a high-level plan to help you anticipate key challenges and shorten migration time to days or even hours.

Why Do Database Migrations Take So Long?

Several factors contribute to the complexity and duration of database migrations:

• Data format differences: Data stored in different formats (e.g., key-value, relational, JSON, XML)
• Type inconsistencies: Variations in data types between source and destination
• Index variations: Different index types and capabilities
• Programmatic elements: Stored procedures and triggers that may not have direct equivalents
• Query language differences: Multiple flavors of SQL and NoSQL query languages
• Performance requirements: Scalability needs for the new database
• Reliability concerns: Ensuring the same or better reliability in the new system
• Operational considerations: Backups, monitoring, disaster recovery procedures
• Ecosystem integration: Specific cloud provider offerings (e.g., Azure AI)
• Network constraints: Handling isolated networks and connectivity
• Special features: Requirements like encryption or security features

Additionally, you must consider specific business requirements for the migration:

• Migration approach: offline vs. online vs. no-downtime
• Acceptable downtime allowance
• Interdependencies between services that use the database
• Data integrity requirements
• Rollback plan complexity

The Lock-In Challenge

Once you select a particular database, migrating away from it can be surprisingly difficult. Beyond the barriers already mentioned, migrations might bring unwanted surprises — sometimes the new database doesn't perform as expected. This happens more frequently than anyone wants to admit. Issues like a missed index, poor choice of a shard key, or differences in query optimization may only become apparent with production data and workloads.

If your architecture already allows for mirroring requests from production (ideally, all requests, but reads are typically most critical), this capability will help derisk and accelerate the migration significantly. For example, if you're using a proxy like Envoy, you can set up shadow mirroring. If you have a cache between the database and the application, it's best to disable it during testing.

The worst assumption you can make is that if things work at a small scale with no real load, they will somehow work in production. They rarely do.

A High-Level Migration Action Plan

Here's how to approach database migrations effectively:

1. Planning Phase

• Define destination configuration and capacity requirements.
• Identify necessary code changes.
• Identify dependencies between services, especially when there are shared tables/collections/namespaces.
• Develop a migration process specific to business requirements.
• Plan operational changes.
• Shortlist possible migration tools based on:
  • Native support for source and destination
  • Speed and reliability
  • Ease of use and minimal learning curve

Pro tip: If you're migrating to another cloud, ask the account team for help. They'll often provide advice and possibly credits to help you move to their data solution.

2. Preparation Phase

• Prepare the destination: Overprovision temporarily to minimize migration time.
• Set up networking: If databases are in isolated environments, establish peering/VPN connections so the migration host can access both environments.
• Build migration expertise: Become extremely comfortable with moving data around in all environments (local, test, and production). Consider this: with a 100GB database, if migration takes 10 hours, you can only test once per day. If it takes 10 minutes, you can run 100 tests. This experience will inform your tool choices and the approach.

3. Testing Phase

• Test all the code changes.
• Test the destination database, ideally with a full mirror of production traffic.
• If full mirroring isn't possible, set up real-time replication to the destination using your migration tool and mirror the reads.
• Conduct dry runs: Execute the plan 1-3 times without cutover to understand:
  • Impact on the source system
  • Potential bottlenecks
  • Migration performance expectations

4. Execution Phase

• Execute the migration plan.
• Implement post-migration configuration: monitoring, alerts, etc.
• Conduct post-migration evaluation: evaluate metrics and identify optimization opportunities.

By following these steps methodically, you can dramatically reduce migration project duration from months to days or even hours.

Key Properties of an Effective Database Migration Tool

When evaluating tools for database migrations, several critical factors stand out as particularly important:

• A good migration solution should be lightweight enough to avoid adding unnecessary overhead and heavy infrastructure provisioning while still handling substantial datasets and workloads through effective parallelization.
• Reliability is perhaps the most important quality — database migrations simply cannot fail or lose data integrity. Any good tool must operate dependably across diverse environments, whether on development machines, virtual machines, or containerized deployments. 
• In case of a sudden interruption that are all too common in production, the tool should allow you to resume the migration without the need for restarting from scratch. 
• Ideally, the tool should also support embedded data validation checks to assist with ensuring data integrity between the source and the destination.
• The learning curve is another consideration. Database migration tools that leverage simple command patterns similar to established utilities (like rsync) significantly reduce cognitive load for teams. This approach allows developers to focus on migration strategy rather than tool-specific syntax.
• The most valuable tools in this space effectively automate and coordinate initial data copy and CDC (change data capture) without requiring manual intervention at transition points. This automation eliminates the need for engineers to monitor processes and manually trigger different phases of migration.

When these properties come together in a single solution, they can transform database migrations from dreaded, high-risk operations into straightforward, routine tasks that teams can approach with confidence.

It's important to note that generic ETL tools (e.g., Fivetran, Azure Data Factory, etc) typically aren't optimized for migrations and require additional steps and configuration to ensure successful operation.

The good news is that major database vendors offer specialized tooling to help bring data onto their platforms, such as MongoDB Atlas Live Migrate or Relational Migrator.