Database Convert Tools Compared: Which One Is Right for You?

Database Convert Best Practices: Avoid Data Loss During MigrationMigrating a database — whether converting from one database engine to another, changing schemas, consolidating multiple databases, or moving to the cloud — is a high-stakes operation. Data loss, downtime, application errors, and performance regressions are real risks. This article outlines pragmatic best practices to plan, execute, validate, and recover from a database conversion with minimal risk and maximum confidence.

Why database conversion is risky

Database conversions touch the core of an application’s data layer. Common sources of problems include:

Incompatible data types or character encodings
Differences in constraints, defaults, and indexes
Divergent SQL dialects and stored procedure behavior
Hidden or undocumented application dependencies
Large volume of data and long-running operations
Concurrency and replication complexity

Avoiding data loss requires systematic planning, thorough testing, and robust rollback paths.

Pre-migration planning

1. Define scope and success criteria

Identify which databases, schemas, tables, and objects are included.
Define success metrics: data integrity (row counts, checksums), application functionality, acceptable downtime, performance targets.
Set clear rollback criteria and time limits for the migration window.

2. Inventory and dependency mapping

Catalog all objects: tables, views, indices, constraints, triggers, stored procedures, functions, jobs, and scheduled tasks.
Map application dependencies: which services and endpoints consume or update the database.
Identify data flows (ETL pipelines, replication) that must be paused or redirected.

3. Analyze schema and type compatibility

Compare data types across source and target engines; prepare mappings (e.g., TEXT → CLOB, TINYINT → SMALLINT).
Note differences in NULL handling, default values, and auto-increment semantics.
Record differences in character sets and collations; plan conversions to avoid mojibake or mismatched sorting.

4. Plan for large tables and growth

Estimate size and row counts; prioritize large tables for special handling.
Consider partitioning, chunked migration, or parallel import strategies for very large datasets.
Calculate network and I/O throughput to estimate transfer time.

5. Choose a migration strategy

Common approaches:

Dump-and-restore: export SQL/data, import on target (simple but can be slow).
Logical replication/CDC (change data capture): keeps source live during sync, ideal for minimal downtime.
Dual-write or shadow tables: write to both systems during cutover, useful when rewriting application code is feasible.
Hybrid: initial bulk load + CDC for incremental changes.

Select based on downtime tolerance, size, and complexity.

Preparation and staging

6. Create a staging environment

Build a staging system that mirrors production (schema, indexes, extensions, OS and DB engine versions where possible).
Seed staging with a representative copy of production data (anonymize if required for privacy).

7. Test conversion on staging

Run the full migration process on staging, including schema conversion, data load, and post-migration scripts.
Validate data integrity, referential constraints, and business logic (stored procedures, triggers).
Measure performance and tune indexes, queries, or configuration.

8. Automate and document the process

Script each step: schema translation, extraction, transformation, load, verification, and rollback.
Use idempotent scripts so they can be re-run safely.
Document prerequisites, runbooks, monitoring points, and escalation contacts.

Execution best practices

9. Ensure backups and point-in-time recovery

Take full, verified backups of source and target before starting.
Enable point-in-time recovery or transaction logs where possible to replay or roll back changes.

10. Freeze or limit writes when feasible

If downtime is acceptable, put the application in maintenance mode to prevent write anomalies.
If online migration is required, use CDC or dual-write and ensure all write paths are covered.

11. Chunk large table migrations

Break large tables into smaller ranges (by primary key, timestamp, or partition).
Validate each chunk before proceeding to the next.
This reduces the blast radius and allows partial rollback if a chunk fails.

12. Preserve transactional integrity

For transactional systems, ensure that related batches of rows move together in a consistent state.
Use consistent snapshots where supported (e.g., mysqldump –single-transaction, PostgreSQL pg_dump with consistent snapshot).

13. Convert schema and constraints carefully

Apply schema changes in stages: create schema, add columns with NULL allowed or defaults, backfill data, then enforce NOT NULL or add constraints.
Recreate indexes and constraints after bulk load if that’s faster; be mindful of unique constraints to avoid duplicates.

14. Handle identity/autoincrement and sequence values

Transfer sequence/identity current values and align them on the target to prevent key collisions.
For dual-write periods, coordinate how new values are generated (e.g., offset sequences, GUIDs).

Validation and verification

15. Verify row counts and checksums

Compare row counts for each table. Differences must be investigated.
Use checksums or hash-based comparisons (e.g., MD5/SHA of concatenated sorted rows or application-level checksums) to validate content.

16. Referential integrity and constraint checks

Ensure foreign keys and constraints are present and consistent. Validate orphaned rows or cascading behaviors.

17. Application functional testing

Run integration and regression tests to exercise data paths, business logic, and queries.
Perform QA with real-world-like workloads and test for edge cases.

18. Performance validation

Benchmark critical queries and common transactions on the target.
Tune indexes and DB configuration (buffer sizes, connection limits) as needed.

Cutover and post-migration

19. Plan the cutover window

Define an exact cutover procedure with timestamps, responsible people, and a go/no-go decision checklist.
Communicate expected downtime and rollback plan to stakeholders.

20. Final sync and switch

For CDC-based migrations, stop writes or apply final incremental changes and verify they are applied.
Redirect application connections to the target, using connection strings, DNS, or load balancers.

21. Monitor closely after cutover

Monitor error rates, performance metrics, slow queries, and business KPIs.
Keep a hot rollback plan (rewind DNS or re-point application to source) for a defined time window.

22. Clean up and harden

Remove dual-write code, decommission replicated links, and tidy up temporary objects.
Re-enable full monitoring, backups, and maintenance tasks on the target.

Rollback and recovery

23. Prepare rollback scripts

Have automated, tested rollback steps that restore source state or re-point applications.
Rollback can be fast (re-pointing connections) or slow (replaying backups); know which applies.

24. Decision criteria for rollback

Predefine thresholds for errors, data mismatches, or performance regressions that trigger rollback.
Assign decision authority and communication procedure.

Tools and utilities

Native tools: mysqldump, mysqlpump, pg_dump/pg_restore, pg_basebackup.
Replication/CDC: Debezium, AWS DMS, Oracle GoldenGate, PostgreSQL native replication, MySQL replication.
ETL/ELT: Airbyte, Fivetran, Talend, Singer taps.
Validation: pt-table-checksum, pt-table-sync, custom checksum scripts.
Orchestration: Ansible, Terraform (for infra), Flyway/liquibase (schema migrations), Jenkins/GitHub Actions.

Provide a shortlist based on your stack and migration type if you want recommendations.

Common pitfalls and how to avoid them

Unmapped data types → Create a comprehensive mapping table and test conversions.
Character encoding issues → Convert and test text fields; use consistent collations.
Hidden business logic in stored procedures → Inventory and test all procedural code.
Long-running migrations → Use chunking and CDC to reduce downtime.
Index and constraint rebuild time → Drop and recreate selectively after bulk load.

Checklist (at-a-glance)

Inventory database objects and dependencies
Create staging with representative data
Select migration strategy (dump, CDC, dual-write)
Script and automate migration steps
Take verified backups and enable PITR
Migrate in chunks; preserve transactional consistency
Verify with checksums, row counts, and app tests
Plan cutover, monitoring, and rollback windows
Clean up and optimize on the target

Converting a database without data loss is achievable with the right mix of planning, tooling, testing, and cautious execution. If you tell me your source and target systems (e.g., MySQL → PostgreSQL, on-prem → AWS RDS), I can produce a tailored migration plan and concrete commands/scripts to run.