Data Migration Services

Timeline depends on data volume, the number of source and target systems, and how much schema transformation is required. A small migration — a single database or application with a straightforward schema — typically takes two to four weeks from assessment to go-live. A medium-complexity migration involving multiple systems, meaningful data cleansing, or ETL pipeline rebuilding typically takes six to twelve weeks. An enterprise migration spanning ERP, CRM, and data warehouse systems with regulatory validation requirements is delivered in phases over three to six months. We provide a realistic, data-driven timeline during the free assessment rather than a generic estimate.

Our default approach is a zero-downtime, parallel-run migration: the target system is populated and kept synchronised with the source through continuous or scheduled replication while the source system remains fully operational. We validate the migrated data in the target environment, then cut over traffic incrementally with a tested rollback path retained until the source system is safely decommissioned. For smaller migrations where a brief, scheduled maintenance window is acceptable to the business, we can also plan a shorter cutover window — the approach is agreed with you before migration begins, not decided by default.

Yes, data cleansing is a standard phase of our migration process, not an optional add-on. Before data moves to the target system we identify and resolve duplicate records, inconsistent formatting, orphaned references, and invalid or incomplete values. Cleansing is scoped during the assessment phase based on the actual condition of your source data — a migration is also the highest-leverage moment to fix long-standing data quality issues, since resolving them after go-live is significantly more expensive.

Data is encrypted in transit and at rest throughout the migration using the same or stronger controls than the source and target systems require. We use replication and validation tooling that never leaves a data set in an unencrypted or unmonitored intermediate state, and we scope access to the migration environment to the engineers actively working on it. Every migration includes checksums or row-level validation to confirm the migrated data matches the source exactly, and a tested rollback plan is in place before the first record moves.

Yes. For healthcare clients we operate under a business associate agreement (BAA) and design the migration — encryption, access logging, and data handling — to HIPAA requirements. For financial services and SaaS clients operating under SOC 2 or PCI-DSS, we map the relevant controls to the migration process and produce the audit evidence needed to satisfy compliance review. Compliance requirements are established during the assessment phase and built into the migration plan from the start, not retrofitted afterward.

A small migration typically involves a single database or application, a moderate data volume, and minimal schema transformation — for example, migrating one PostgreSQL database to a managed cloud instance. A medium migration involves multiple connected systems, meaningful schema mapping or data cleansing, and an ETL pipeline that needs to be rebuilt or reconfigured — for example, consolidating several regional databases into one cloud data warehouse. An enterprise migration spans ERP, CRM, and data warehouse systems simultaneously, carries regulatory validation requirements, and is delivered in phases against a multi-month roadmap. We scope your engagement into the right band during the free assessment rather than applying a one-size estimate.

Yes. Heterogeneous migrations — moving between different database engines such as Oracle to PostgreSQL, SQL Server to MySQL, or a relational database to MongoDB — require schema conversion and data type mapping in addition to the data transfer itself. We handle schema conversion, stored procedure and trigger translation where applicable, and validation of the converted schema against the original before any production data moves. Heterogeneous migrations typically take longer than same-engine migrations because of this conversion work, which we scope explicitly during assessment.

Every migration we run has a tested rollback plan established before the first record moves — the source system is never decommissioned until the target has been validated under real usage and the rollback window has closed. If a discrepancy is found during validation, we do not proceed to cutover; we investigate and re-run the affected batch until the data matches. This validation-gated approach means a migration issue results in a delayed cutover, not data loss or extended downtime.

Data migration is the process of moving data from one system to another - a legacy database to a modern one, an on-prem application to the cloud, a spreadsheet estate into an ERP - with the schema, referential integrity, and business meaning preserved through the move. It differs from a bulk data transfer in that migration is planned, validated, and reversible: schema is mapped and cleansed before records move, rows are reconciled after cutover, and a rollback path is proven before go-live. Every non-trivial migration is really three problems - understanding what the source actually contains, deciding what the target should look like, and executing the cutover without the business noticing.

The right tool set depends on source and target. For database migration we use AWS DMS, Azure Database Migration Service, or GCP Database Migration Service for online replication and cutover. For ETL pipelines we use Fivetran, Airbyte, Stitch, or custom Python/Airflow jobs where the sources need bespoke handling. For large-scale batch transfers we use AWS Snowball, Azure Data Box, or Google Transfer Appliance where network throughput is a constraint. For validation we build reconciliation harnesses in Python against the target using row-level checksums, aggregate parity checks, and business-rule tests. Tool choice is per-engagement; the reconciliation approach is standard.

Validation runs in three tiers. Row-level: checksums or hashes on every migrated record compared source-to-target so anything that changed byte-for-byte flags. Aggregate: totals, counts, sums, and distinct-value counts computed on both sides and compared - catches encoding or truncation errors that hash checks miss. Business-rule: known invariants (orders sum to invoices, inventory adjustments balance, patient counts match visit counts) tested as SQL assertions on the target. Every migration ships with the reconciliation output; go-live only happens once all three tiers pass. Rollback plans reference the same tiers so a bad cutover is caught in minutes, not weeks.