Modernizing a complex enterprise data estate with Databricks, Microsoft Fabric and Snowflake
A large scale modernization program that brought together data platform transformation, data engineering, analytics modernization and the migration of more than 15,000 enterprise reports.
The organization had data across multiple platforms and a reporting estate that had become difficult to manage.
The existing environment had evolved over several years. Different business teams had built reports and data processes for their own requirements, creating duplication and making it difficult to maintain a consistent view of enterprise data.
What we found
- More than 15,000 reports across business functions.
- Multiple data warehouses and data processing environments.
- Duplicate reports with overlapping business logic.
- Complex dependencies between reports, datasets and source systems.
- High platform and support costs.
- Slow report refresh and processing for critical workloads.
- Inconsistent definitions and limited visibility into data ownership.
What the business needed
- A modern and scalable data architecture.
- A clear role for Databricks, Microsoft Fabric and Snowflake.
- A controlled approach to migrate 15,000 plus reports.
- Lower platform and reporting operating costs.
- Better data quality, security and governance.
- Faster and more consistent enterprise reporting.
- A foundation that could support future analytics and AI programs.
We redesigned the data estate before moving workloads to the new platforms.
The program combined assessment, architecture, engineering, migration and optimization. Instead of moving every workload as it existed, the team first identified what should be migrated, modernized, consolidated or retired.
Modernization approach
The target architecture was designed around business workloads rather than technology preference. Databricks, Microsoft Fabric and Snowflake were used where their capabilities best matched the workload.
- Built a complete inventory of data sources, pipelines, datasets and reports.
- Assessed workload complexity, usage, business criticality and cost.
- Defined the target data architecture and platform responsibilities.
- Created repeatable migration patterns for engineering and reporting.
- Introduced validation and business sign off before production cutover.
- Optimized workloads after migration to improve cost and performance.
One enterprise data foundation with the right platform for each workload
The modernization connected enterprise sources to modern data platforms and a governed analytics layer. The architecture was designed to support both current reporting requirements and future data workloads.
A migration factory that could operate at enterprise scale
The report migration was organized into controlled waves. Each report passed through assessment, rationalization, dependency mapping, migration, validation and production release.
Inventory
Captured report owners, users, data sources, refresh schedules and dependencies.
Rationalize
Identified duplicate, unused and low value reports for retirement or consolidation.
Assess
Scored complexity, business criticality, usage and migration readiness.
Modernize
Redesigned models and reporting logic where direct migration was not appropriate.
Validate
Compared migrated results with the legacy environment and completed business validation.
Release
Moved validated reports into production through controlled migration waves.
The modernization delivered measurable improvements across cost, performance and reporting.
Lower operating cost
Platform consolidation, workload optimization and report rationalization reduced the cost of running the analytics environment.
Faster report processing
Modernized data pipelines and optimized workloads improved average processing time for reporting workloads.
Faster access to insights
Improved data availability and reporting performance helped business users access trusted information faster.
Reports transformed
The report portfolio was brought through a structured assessment and migration process.
Fewer duplicate reports
Report rationalization reduced duplicated reporting assets and simplified the analytics landscape.
Modernization framework
A repeatable framework was established for future data platform and analytics migrations.
More than a platform migration
The program changed how the organization managed data and analytics by combining modern platforms with standardized engineering, governance and reporting practices.
Lower Technology Cost
Reduced platform overhead by consolidating workloads, retiring unnecessary reporting assets and optimizing data processing.
Better Reporting Performance
Modern data pipelines and optimized models improved report processing and reduced delays for business users.
Better Data Control
Common governance, security, lineage and ownership practices improved confidence in enterprise data.
Scalable Data Foundation
The modern architecture created a stronger base for new analytics, data products and future transformation programs.
Build a modern data foundation for the next stage of growth.
From platform assessment and architecture to migration, analytics and optimization, the modernization journey can be delivered as one structured transformation program.
Discuss your data modernization program