Service — Data Migration & Modernisation

Legacy Architecture
Is a Risk. Modernisation
Is a Strategy.

CALIGO migrates legacy data warehouses, on-premise platforms, and fragmented data estates to modern cloud architectures — with zero-downtime delivery, automated reconciliation at every stage, and a target architecture designed to last.

Legacy to Cloud MigrationOracle to AzurePlatform ModernisationZero-Downtime MigrationData ReconciliationCloud ArchitectureModernisation Roadmap
Migration Commitments
Data Loss ToleranceZero
Reconciliation ApproachAutomated at every stage
Production CutoverParallel run with fallback
Downtime TargetZero for critical systems
Architecture GuaranteeFit-for-purpose target state
Change ManagementStructured knowledge transfer
0
Acceptable data loss
100%
Reconciliation coverage
60%
Design time saved via accelerators
3
Cloud platforms supported
The Problem

Legacy Platforms Compound In Cost and Risk Over Time.

Legacy on-premise data warehouses — Oracle, Teradata, IBM — were built for a different era. They are expensive to maintain, difficult to scale, incapable of supporting modern analytical workloads, and increasingly misaligned with the regulatory requirements that demand real-time lineage, automated quality controls, and cloud-native data structures.

The cost of staying is rising faster than the cost of moving. Licence renewal costs compound annually. Maintenance engineering is increasingly scarce. And every new use case — real-time analytics, ML pipelines, regulatory layer automation — becomes harder to build on an architecture that was not designed for it.

Legacy Licences Are Compounding in Cost
Oracle, Teradata, and IBM licences renew at increasing cost — while the capabilities they deliver fall further behind cloud-native alternatives. The total cost of ownership grows each year.
Migration Risk Has Blocked Previous Attempts
Data migration is high-stakes — a failed cutover during a regulatory period could be catastrophic. Previous migration attempts were abandoned because the risk profile was unacceptable.
Legacy Architecture Blocks New Capabilities
ML pipelines, real-time analytics, and regulatory reporting automation are impossible or prohibitively expensive on the existing architecture — blocking use cases that cloud-native platforms deliver natively.
Institutional Knowledge Is the Only Documentation
The legacy platform is understood by two or three engineers who have been there since it was built. If they leave, the organisation loses the ability to maintain or migrate the platform safely.
No Target Architecture Defined
Migration discussions stall because there is no clear and agreed target state. Technology decisions are made by default — the path of least resistance — rather than strategic architecture design.
What We Deliver

Migration & Modernisation. End to End.

CALIGO manages the full migration lifecycle — from target architecture design through cutover and post-migration stabilisation — with automated reconciliation and zero-downtime delivery.

Target Architecture Design
We design the target state architecture — cloud platform selection, storage layer design, integration patterns, and governance framework — before a single byte is moved. Architecture decisions made wrong compound throughout the migration.
Cloud platform selection
Medallion / lakehouse architecture
Integration & ingestion design
Governance framework design
Legacy Source Assessment
Full inventory of source tables, transformations, business logic, and downstream dependencies — documented, classified by criticality, and mapped to the target architecture. No surprises during migration.
Table & transformation inventory
Business logic extraction
Dependency mapping
Migration classification & sequencing
Automated Reconciliation Framework
We build automated reconciliation between legacy and target systems — row counts, aggregates, and business-rule checks running continuously throughout migration. Zero-tolerance for data loss.
Row count & aggregate reconciliation
Business-rule validation
Continuous reconciliation during migration
Post-cutover equivalence testing
Parallel Run & Cutover
The legacy and target systems run in parallel — both producing outputs — until equivalence is confirmed. Cutover is a managed event, not a gamble. Fallback capability is maintained until confidence is established.
Parallel run planning & execution
Equivalence confirmation criteria
Managed cutover procedure
Fallback capability maintained
Oracle & On-Premise to Azure Migration
Deep expertise in migrating Oracle, Teradata, and on-premise SQL Server data warehouses to Azure Synapse, Databricks, or Snowflake — including PL/SQL transformation conversion and performance optimisation.
Oracle to Azure Synapse migration
Teradata to Databricks migration
PL/SQL to dbt conversion
Performance tuning on target platform
Post-Migration Stabilisation
Six to twelve weeks of hypercare after cutover — intensive monitoring, rapid incident response, and iterative optimisation until the target platform is proven stable and the team is confident.
Hypercare monitoring & response
Performance benchmarking
User acceptance support
DataOps handover or transition
Migration Methodology

Zero Data Loss. Zero Downtime. Full Confidence.

Every migration is structured around three non-negotiable commitments — automated reconciliation throughout, zero downtime for critical systems, and a target architecture that is fit for the next 10 years, not just the next 2.

Migration Architecture — Parallel Run Pattern
Legacy (Source)
Oracle / Teradata
On-Premise SQL
Legacy ETL
Source Applications
↓ Extraction · CDC · Reconciliation ↓
Transition Layer (Parallel Run)
Automated Reconciliation
Equivalence Validation
Fallback Trigger
↓ Managed Cutover ↓
Target (Cloud)
Azure Synapse
Databricks
Snowflake
Azure Data Lake
dbt + ADF
01
Discovery & Assessment
Full inventory of legacy tables, transformations, business logic, and dependencies. Migration complexity classification and sequencing — highest-risk systems migrated last.
4–6 Weeks
02
Target Architecture Design
Cloud platform selection, storage layer design, integration patterns, and governance framework — using CALIGO's industry data models to accelerate target model design by 50%+.
Weeks 3–8
03
Reconciliation Framework Build
Automated reconciliation between legacy and target — row counts, aggregates, and business-rule checks — built before migration begins. Zero-tolerance approach from the start.
Weeks 4–8
04
Phased Migration Execution
Migration executed in waves — non-critical data first, building team confidence and validating the reconciliation framework before mission-critical and regulatory data moves.
Wave-Based
05
Parallel Run & Equivalence
Legacy and target run simultaneously, producing identical outputs. Equivalence confirmed across all critical data elements before cutover is approved.
4–8 Weeks
06
Cutover & Hypercare
Managed cutover on a defined date. Hypercare monitoring for 6–12 weeks post-cutover — rapid incident response and iterative optimisation until the team is confident and stable.
Post-Go-Live
Use Cases

Migrations Completed. With Zero Tolerance for Data Loss.

Every migration is underpinned by automated reconciliation — zero data loss is the standard, not the aspiration.

Banking · Core Banking Migration
Core Banking Data Migration — Zero Data Loss, Zero Downtime
A retail bank migrated its core banking data warehouse from Oracle to Azure Synapse — 18 months of transaction history, 47 operational reports, zero downtime during business hours.
100% data reconciliation confirmed; zero reports impacted
Insurance · Platform Modernisation
Insurance Platform — Teradata to Databricks Lakehouse
An insurer migrated its legacy Teradata DWH to a Databricks Lakehouse — retaining Solvency II and IFRS 17 regulatory layer compliance throughout, with automated reconciliation validating equivalence at every stage.
Migration completed with zero regulatory submission disruption
Telecom · Cloud Migration
Telecom DWH to Azure — 6TB, 8 Weeks
A telecom operator migrated 6TB of subscriber and CDR data from an on-premise SQL Server warehouse to Azure — with parallel run validation and managed cutover during a low-traffic window.
Migration completed 2 weeks ahead of schedule; no data loss
Banking · PL/SQL Conversion
Oracle PL/SQL to dbt — 400 Transformations Converted
A bank's Oracle data warehouse contained 400+ PL/SQL transformation procedures with no documentation. CALIGO extracted, documented, and converted all transformations to dbt — with performance improvement on the target platform.
Performance on target platform 40% faster than legacy Oracle
Legacy Architecture Is a Risk. Modernisation Is the Strategy.
Talk to CALIGO about your migration — target architecture, risk assessment, and a delivery approach that eliminates the uncertainty from a high-stakes platform change.