Skip to content

New GenAI & AI agent development

Data & Analytics

Data Migration Services

We plan, map and move data between databases, ERPs and cloud platforms, with reconciliation reports and rehearsed cutovers, so you switch systems on schedule without losing or corrupting records.

  • Source-to-target mapping
  • Secure transfer
  • Reconciliation checks
  • Rehearsed cutover
Years of delivery
8+
Years of delivery
Projects shipped
350+
Projects shipped
Clients across US, UK, AU & SG
200+
Clients across US, UK, AU & SG

What’s included

Everything You Need to Move Your Data Safely and On Time

We plan, move and verify your data across systems, with minimal downtime and records you can trust on day one.

  • Source System Analysis & Migration Scoping

    We profile your source data, volumes and dependencies, then fix scope and risks, so the migration is planned with facts.

    • Data profiling report
    • Migration scope

  • Migration Strategy & Cutover Plan

    We choose big-bang, phased or parallel-run migration and map a cutover timeline that protects daily operations.

    • Migration plan
    • Cutover schedule
    • Rollback plan

  • Data Mapping & Transformation Rules

    We map every source field to its target and define transformation logic, so nothing is lost or misinterpreted in the move.

    • Mapping specification
    • Transformation rules

  • ETL Migration Scripts & Automation

    We build repeatable migration jobs for databases, ERP and CRM data, so test runs and final runs behave identically.

    • Migration scripts
    • Automated jobs

  • Cloud & Database Platform Migration

    We move on-premise databases and files to AWS, Azure or GCP using proven tooling, with minimal downtime for your users.

    • Cloud target setup
    • Replication setup

  • Reconciliation & Data Validation Testing

    We compare record counts, totals and samples between systems across trial loads, so you sign off with evidence.

    • Reconciliation report
    • UAT support

  • Secure Transfer & Sensitive Data Handling

    Encrypted transfer, masked test data and access logging protect customer and financial records throughout the migration.

    • Encryption setup
    • Masking rules

  • Go-Live Cutover & Post-Migration Hypercare

    We run the final cutover and stay on hand afterward to resolve issues quickly, so your team starts confidently.

    • Go-live runbook
    • Hypercare support

Need something specific?

Why Kramate

Why Business Owners Choose Kramate for Moving Your Data Safely

We practise the move before the real one, so go-live day holds no surprises.

  • We Practise the Move First

    Instead of

    finding problems on the real moving day

    With Kramate

    We run trial moves with your data until the results are clean and the team is happy.

  • Proof That Nothing Went Missing

    Instead of: taking it on trust that all data arrived

    With Kramate: We compare counts and totals before and after, and give you a signed list to approve.

  • A Way Back If It Goes Wrong

    Instead of: no plan for a failed switch-over

    With Kramate: We prepare a way back to the old system before we switch, and agree it with you.

  • Private Records Stay Locked Up

    Instead of: customer details sitting in open files

    With Kramate: We use scrambled copies for testing, and only named people can reach the real records.

  • We Stay Close After Go-Live

    Instead of: being left alone when problems show up

    With Kramate: For a period after the switch, we stay on hand to fix issues quickly.

Our approach

How We Run Your Data Migration, Step by Step

We move your data to its new home and prove nothing was lost.

7 clear steps 7 deliverables you keep

  1. Old System Study

    We study your current systems, count records and flag duplicates, broken entries and old data you can drop.

    You receive: Scope summary

    You confirm what must move

  2. Move & Cutover Plan

    We choose how to move, in one go or in stages, and write a cutover plan with a way back.

    You receive: Cutover plan

    You approve the move date

  3. Field Mapping

    We match each old field to its new place and write the rules for changing formats.

    You receive: Mapping sheet

    You check the field matches

  4. Trial Moves

    We run test moves with scripts we can repeat, fixing problems each time before the real move.

    You receive: Trial results

    You review the test records

  5. Check & Reconcile

    We compare counts and totals between old and new, line by line, and list any differences.

    You receive: Reconciliation report

    You sign off the matched totals

  6. Safe Handling

    We encrypt data in transit and limit who can see sensitive records during the move.

    You receive: Security notes

    You flag which records are sensitive

  7. Cutover & Aftercare

    We run the final move, switch you over and stay close while your team starts using it.

    You receive: Live new system

    You stay available on cutover day

Tools & technologies

Technologies We Use for Data Migration

We use proven migration and validation tools so every record is checked before cutover.

  • Microsoft SQL Server: Microsoft relational database common in finance and ERP systems.
  • Oracle Database: Enterprise relational database often found in long-running core systems.
  • MySQL: Widely used database for web applications.
  • PostgreSQL: Dependable database for complex business data.
  • MongoDB: Flexible document database for fast-changing data.
  • AWS: Amazon cloud hosting and services.
  • Azure: Microsoft cloud hosting and services.
  • Google Cloud: Google infrastructure for data, AI and applications.
  • Snowflake: Cloud data warehouse that scales storage and queries separately.
  • AWS Database Migration Service: Moves databases to AWS with little downtime for your users.
  • Azure Data Factory: Microsoft service for building and scheduling data movement pipelines.
  • Talend: Data integration and quality tooling for moving and cleaning records.
  • Airbyte: Open-source connectors that sync data from apps and databases.
  • Python: Readable language for business logic, data and automation.
  • DBeaver: Database client used to inspect and compare data across systems.
  • Great Expectations: Runs automated checks so bad data is caught before it reaches reports.
  • Git: Version control that keeps every change to code and models traceable.

Expert guide

Data Migration Services: A Complete Guide for Businesses

Data migration services move information from one system to another without losing, corrupting or exposing it: from an old database to a new one, from on-premises servers to the cloud, from one application to its replacement, or from several silos into a warehouse. The move itself is rarely the hard part. The hard part is that the new system must behave correctly on day one, using data created over many years.

Careful data migration services treat the move as a rehearsed, measurable event, not a single copy job.

This guide covers migration types, a phase-by-phase plan, how to prove the data arrived intact, cutover strategies, security and residency duties in the USA, UK, Australia and Singapore, cost drivers and how to pick a partner who plans for rollback.

What data migration services cover

Common migration types
TypeWhat movesMain risk
Database Tables and stored logic between database engines or versions Data type differences and lost business logic
Application Records from a retired system into its replacement Mismatched data models and missing history
Cloud Servers and datasets from on-premises to a cloud platform Downtime, cost surprises and access settings
Warehouse or lakehouse Analytical data and pipelines between platforms Broken reports and different calculation results
Storage and files Documents and archives Permissions and metadata lost in transit

Migrations often accompany bigger programs such as cloud migration or legacy software modernization, where data is one workstream among several.

A phase-by-phase plan for data migration services

  1. Inventory and scope. List every source, owner, volume, dependency and downstream consumer, including the spreadsheets and reports nobody documented.
  2. Profile the data. Measure duplicates, missing values, odd formats and orphaned records before deciding what to move.
  3. Decide what not to migrate. Archive or delete obsolete records. Moving less data lowers cost, risk and your future storage footprint.
  4. Map and transform. Define how each source field becomes a target field, including code translations and rules for defaults.
  5. Build and rehearse. Run the full migration repeatedly in a test environment, timing each run.
  6. Reconcile. Compare source and target at several levels (see below) and fix differences.
  7. Cut over. Switch users to the new system at a planned moment, with rollback ready.
  8. Stabilize and decommission. Watch the new system closely, then retire the old one on a schedule, keeping an archive where records must be retained.

Each rehearsal should be faster and cleaner than the last. If the third rehearsal still surprises the team, the cutover date should move.

Proving the data arrived intact

Trust in a migration comes from evidence, gathered in layers:

  • Counts: records per table or entity match, with every rejected record explained.
  • Totals and checksums: sums of money fields and hash comparisons on key columns.
  • Sampled deep checks: staff compare real examples side by side, including awkward edge cases.
  • Business-rule tests: open invoices, balances and stock levels agree with the source.
  • Report parity: key reports produce the same results from the new system.

Automate these checks so they run on every rehearsal. Tools like Great Expectations (opens in a new tab) and dbt tests (opens in a new tab) can express many of them as repeatable tests. When migrating analytics platforms, vendor documentation such as Snowflake’s (opens in a new tab) and BigQuery’s (opens in a new tab) describes loading and validation options worth reviewing early.

Cutover strategies and rollback

Cutover approaches compared
ApproachHow it worksBest forTrade-off
Big bang Everything switches in one planned window Smaller systems with a tolerable downtime window Highest risk if something fails
Phased Move by region, product or business unit Large estates and risk-averse teams Two systems live for a while
Parallel run Both systems operate and results are compared Finance and compliance-critical data Double the operational effort
Continuous sync then switch Replicate changes until the final moment Systems that cannot stop for long Needs careful change capture

Security during the move, and leaving less behind

Migration creates temporary copies of your most sensitive data, often in staging areas with relaxed settings. Treat them like production: encrypt data in transit and at rest, restrict access to named people, mask personal data in test environments where possible, log all access and delete temporary copies when the project ends. The NIST Privacy Framework (opens in a new tab) offers a useful structure for assessing privacy risk across the move.

Decommissioning is part of the project. Retiring old servers and deleting obsolete copies reduces attack surface and energy use, as long as retention duties are met first.

Industry use cases

  • Healthcare: moving appointment and billing records between practice systems while preserving audit history and masking patient data in test copies.
  • Financial services: moving ledgers to a new platform with a parallel run until balances agree.
  • Retail: consolidating product and order data from a legacy platform into a new commerce stack and warehouse.
  • Education: merging student records from several campus systems into one, resolving duplicate identities first.

These are illustrative scenarios, not Kramate client results.

Residency, transfers and regional law: USA, UK, Australia and Singapore

Migrating to a new host or cloud region can change where personal data is stored and who can reach it. Check the destination before you start and confirm details with legal counsel.

Cutover windows are usually at weekends or off-peak hours, so agree staffing across time zones. Kramate provides four to six hours of overlap with US Eastern time and a full working day with the UK, Australia and Singapore.

Cost factors and how to plan a budget

  • Number of sources and the state of their documentation.
  • Data volume and quality problems found during profiling.
  • Transformation complexity and the number of rehearsals.
  • Downtime tolerance: low-downtime methods need more engineering.
  • Compliance and audit evidence required.
  • Training, parallel running and post-cutover support.

Fixed price works once profiling is done and scope is stable. Before that, an hourly or small fixed-price assessment is safer. Keep a contingency reserve for data surprises, which nearly every migration finds.

How to choose a data migration services partner

  1. They profile first and quote after seeing your data.
  2. They plan multiple rehearsals and show reconciliation reports.
  3. They describe rollback in concrete terms.
  4. They handle security and residency proactively, including test data.
  5. They involve your business users in validation, not only IT.
  6. Documentation and mapping specs are yours at the end.

How Kramate delivers migrations

After a free consultation you receive a detailed estimate within 24 hours. A dedicated project manager reports daily on Slack or Teams, and each two-week round ends with a review of rehearsal results. Work happens under an NDA, with encrypted devices and least-privilege access, and all intellectual property, including mapping documents and scripts, is yours. See our delivery approach for migrations for the full process.

Frequently asked questions

How do you plan a data migration?

Inventory sources, profile data quality, decide what to leave behind, map fields, rehearse repeatedly, reconcile results, then cut over with a rollback plan. Planning rehearsals and validation matters more than the copy itself.

Why do data migrations fail?

Typical causes are unprofiled data, undocumented dependencies, too few rehearsals, no business validation and no rollback plan. Most failures are discovered late, which is why early profiling and repeatable tests matter.

Big bang or phased migration: which is safer?

Phased approaches limit the impact of a failure but keep two systems running longer. Big bang suits smaller, simpler estates with an acceptable downtime window. Critical financial data often justifies a parallel run.

How do you validate data after migration?

Layer the checks: record counts, totals and checksums, sampled side-by-side comparisons, business-rule tests and report parity. Automate them, so every rehearsal produces the same evidence and the final cutover is not a leap of faith.

Does moving data to the cloud trigger privacy obligations?

It can, because hosting location and who can access the data both change. Cross-border transfer rules, security duties and contract terms with the provider all matter, so review them with legal counsel before the move begins.

Can we migrate with little or no downtime?

Often yes, using continuous replication and a short final switch. It needs more engineering and testing, so weigh it against the cost of a planned outage. Our database administration services can support the operational side.

Sources and further reading

FAQ

Frequently asked questions

Cost depends on data volume, the number of source and target systems, how different their structures are and how much cleaning is needed. Downtime limits, rehearsal rounds and compliance needs also add effort. A like-for-like move is simpler than a restructure. We give a no-obligation estimate within 24 hours.

We profile the source, map fields, build migration scripts, run trial loads, reconcile results and then cut over. The schedule depends on data quality, how many trial rounds are needed and your allowed downtime window. Business sign-off on reconciliation often sets the final date.

You own them. Kramate signs an NDA before detailed discussions, and mapping specifications, scripts and reconciliation reports transfer to you. Data moves inside your controlled environments, with access limited and logged. We do not keep copies after the project ends.

Yes. We plan staged loads and a short, rehearsed cutover so daily operations continue as long as possible. Changes made during the move are captured and synchronised. A rollback plan is prepared in case something fails at switch-over.

We reconcile record counts, totals and sampled records between source and target after each trial load. Differences are logged, explained and fixed before the final run. You receive a reconciliation report to sign off, so the decision rests on evidence.

Prepare access to source and target systems, a list of data owners, known data problems and your downtime limits. Decide what history to keep and what to archive. Cleaning obvious junk early saves time. We handle the profiling and mapping.

Common mistakes include skipping data profiling, migrating everything including obsolete records, underestimating testing and having no rollback plan. Another is leaving business users out of validation. We run rehearsals, involve data owners in sign-off and keep a tested way back.

Taking new projects

Let’s talk about your Data Migration Services needs

Tell us about your project and get a free consultation and estimate within 24 hours.

  1. We reply within 4 business hours A real consultant reads your brief — no bot, no queue.
  2. 30-minute discovery call We dig into goals, constraints and budget. NDA signed on request.
  3. Proposal in 24 hours Scope, timeline, team and price — clear enough to decide on.
hello@kramate.com Chat on WhatsApp

NDA on request You own the IP No obligation Free estimate

Your information is confidential. We’re happy to sign an NDA.

Book a free call Chat on WhatsApp