Data Modernization Services

Data modernization replaces legacy warehouses, manual ETL scripts and overnight batch jobs with a cloud-native, governed stack. The core components are an ingestion layer (Airbyte or Fivetran), a cloud warehouse (Snowflake or Databricks), a transformation layer (dbt), and a semantic layer that ensures consistent metric definitions across every BI tool. The result is data your teams trust and pipelines your engineers can maintain.

A legacy warehouse migration typically takes 12–20 weeks depending on data volume, number of source systems, and downstream consumers to revalidate. We always run the new stack in parallel with the legacy environment until parity is confirmed, so your existing reports keep working throughout the migration.

Snowflake is the right choice if your primary use case is SQL analytics, BI reporting, and data sharing. Databricks is stronger when ML and data science workloads need to run on the same data as analytics, or when you need Delta Lake's unified batch and streaming architecture. Most organisations start with Snowflake and add Databricks when ML use cases become central.

dbt (data build tool) is the transformation layer that sits between your cloud warehouse and your BI tools. It lets analysts write modular SQL transformations with version control, testing, documentation, and lineage built in. The result is a semantic layer where every metric is defined once and consistent across every dashboard, eliminating the 'why does finance see a different number than marketing?' problem.

We run the modern stack in parallel with your legacy environment during the migration window. Each downstream consumer — Tableau dashboard, Power BI report, or API endpoint — is validated against the new stack before cutover. Legacy is only decommissioned once parity is confirmed. For critical reports, we maintain dual-write until the team is confident in the new numbers.