Move Legacy Data Warehouses To Databricks Faster With Zero Migration Risk
BrickPath helps teams migrate legacy EDW and ETL estates to a governed Databricks Lakehouse using automated discovery, dialect-aware code conversion, continuous reconciliation and wave-based migration.
Legacy Estate Discovery
Automatically inventory tables, views, stored procedures, ETL jobs and BI dependencies, then score workloads by migration complexity.
AI-led Code Conversion at scale
Translate legacy DDL, DML, stored procedures and UDFs into Databricks SQL or PySpark using dialect-aware parsing and AI-assisted conversion.
ETL & Pipeline Modernization
Refactor legacy data pipelines into scalable Databricks workloads while preserving business logic and downstream dependencies.
Automated Data Reconciliation
Validate row counts, columns, aggregates and business rules between legacy and Databricks environments before workloads are cut over.
Unity Catalog Governance
Land migrated assets into Unity Catalog with lineage, access controls, auditing and a consistent governance model from day one.
Phased Cutover
Migrate workloads in controlled waves, run old and new environments in parallel, then retire legacy infrastructure once parity is established.
A Faster, Controlled & De-Risked Path From Legacy EDW To Databricks
BrickPath assesses the legacy estate, builds the governed Databricks foundation, converts and validates workloads, then moves them through controlled migration waves into production.
Inventory tables, views, stored procedures, ETL jobs and BI reports, map dependencies, profile workloads and score complexity to define migration waves, target architecture and business case.
Stand up Databricks, Unity Catalog, security, networking and CI/CD, then convert legacy SQL, procedures and pipelines into Databricks SQL or PySpark with side-by-side review.
Reconcile legacy and Databricks data at row, column and aggregate levels, benchmark performance, complete UAT and move validated workloads into production through controlled waves.
Monitor cost and performance after migration, tune workloads, enable users with runbooks and training, and progressively retire the legacy platform once stability is established.
Discover & Design
Discover & Design
Build & Convert
Build & Convert
Validate & Go Live
Validate & Go Live
Optimize & Decommission
Optimize & Decommission
Increase Migration Speed Without Compromising Data Accuracy
BrickPath uses automation where it is safe and explicit review and validation where business-critical logic requires more control.
Human-Reviewed Conversion
Automatically convert repeatable patterns while routing complex or unsupported logic for engineering review.
Continuous Reconciliation
Treat source-to-target validation as a migration gate, not a post-cutover check.
Wave-Based Cutover
Start with lower-risk workloads, prove the approach and move critical dependencies only after the pattern is validated.
Governance From Day One
Apply Unity Catalog, lineage and access controls as workloads land rather than retrofitting governance later.
See what changes when Agentic intelligence becomes part of the operating model
Predictive Supply & Manufacturing Intelligence with ML-Based Forecasts, Digital Twins & IoT Analytics at Scale
Kasmo designed a Databricks-native platform that brings ERP, supplier, inventory and plant data together for real-time visibility, demand forecasting, predictive quality and manufacturing intelligence.
Reducing Research Preparation Time with Agentic AI Grounded in Scientific Data
Kasmo used Claude for Life Sciences to ground specialized Agents in Benchling and scientific sources for literature review, protocol drafting and bioinformatics work, with scientist review built into the workflow.
AI Agents for Faster Site & Investigator Selection with Evidence-Backed Recommendations
Kasmo combined trial, site, investigator and public data in Databricks, then applied human-reviewed AI Agents to rank candidates and show the evidence behind each recommendation.
Migrating Critical Sales & Pricing Analytics from Legacy SQL to an AI-Ready Lakehouse
Kasmo modernized a complex SQL Server and SSIS environment onto Databricks, using AI-assisted conversion, reconciliation and governed pipelines while preserving the business logic teams already relied on.
Enabling Faster, Better-Informed Field Decisions with AI-Powered Analytics
Kasmo designed a Databricks-native commercial intelligence solution that brings Veeva Vault CRM, claims, prescribing and payer data together for shared KPIs, HCP/HCO analysis and AI-assisted decision support.
