Database, Data Engineering, Cloud & AI consulting since 2007
Migration & modernization
Move databases between versions, engines and platforms with a tested plan, minimal downtime and a clear way back if anything goes wrong.
Organizations migrate databases to leave unsupported versions, reduce licensing costs, move to the cloud, or consolidate platforms after growth and acquisitions. The technology to copy data is mature; what makes migrations succeed is careful assessment, realistic testing and disciplined cutover.
Experts Infotech has migrated databases between SQL Server versions, from commercial to open-source engines, and from data centers to AWS, Azure and Google Cloud. We treat the application, the data and the people who depend on them as one project - not just a data copy.
Every migration plan includes measurable success criteria, a rehearsed cutover runbook, data validation and a rollback option, so business owners can make go/no-go decisions with confidence.
Same-engine upgrades are the simplest; cross-engine migrations need code and data type conversion.
Upgrades from SQL Server 2016 and earlier to 2022 or 2025, consolidation, edition changes, and moves to Azure SQL Managed Instance, Azure SQL Database, Amazon RDS or cloud VMs - using backup/restore, log shipping, availability groups or the Managed Instance link for near-zero downtime.
Conversion of T-SQL procedures to PL/pgSQL, data type and collation mapping, identity and sequence handling, and case-sensitivity review. Babelfish for Aurora PostgreSQL can reduce application changes by accepting T-SQL and the SQL Server wire protocol.
Schema and code conversion, character set and collation alignment (utf8mb4), date and time behavior, and application query changes. SQL Server Migration Assistant (SSMA) accelerates MySQL to SQL Server projects.
Assessment of PL/SQL packages, sequences, synonyms and NUMBER precision; conversion with SSMA for Oracle, AWS DMS Schema Conversion or ora2pg; and performance testing of converted code before cutover.
Rehost, replatform or refactor decisions per workload; sizing from measured performance; network, identity and security design; and migration waves that limit business risk.
Moving reporting and analytics from legacy warehouses to Snowflake, Databricks, Microsoft Fabric or Amazon Redshift, including ETL redesign with SSIS, Azure Data Factory or AWS Glue.
A repeatable process that surfaces risks early and keeps the business informed.
Inventory databases, dependencies, code objects, data volumes, performance baselines, compliance needs and downtime tolerance. Identify incompatibilities and estimate effort.
Convert tables, indexes, constraints, views, procedures, functions and triggers using tools such as SSMA or AWS DMS Schema Conversion, then review and hand-fix what tools cannot convert.
Map data types and character sets, cleanse problem data, and build repeatable full-load processes with native tools, SSIS or custom ETL.
Keep source and target synchronized with CDC - for example AWS DMS ongoing replication, native replication or log-based tools - so the final cutover window is minutes, not hours.
Row counts, checksums and business-level reconciliation; functional, integration and performance testing of the application on the new platform.
A rehearsed, timed runbook covering freeze, final sync, validation, connection string or DNS changes, and business sign-off.
Defined rollback triggers and a tested path back - such as reverse replication or retaining the source read-only - until the new platform is proven.
Close monitoring of performance, errors and costs after go-live, query tuning, and decommissioning of the old environment.
AWS Database Migration Service supports homogeneous and heterogeneous migrations with full load plus ongoing CDC, and works well for moving databases into Amazon RDS, Aurora, Redshift or S3. We still validate data independently, pre-create the converted schema, and plan for large objects, triggers and sequences that DMS does not migrate on its own.
Start with a migration assessment. We will identify risks, estimate effort and recommend the lowest-risk path for your databases.
