01
Dimensional modeling
Star schemas and semantic layers that make self-service analytics actually self-service.
Data
Warehouses built for questions, not just storage.
Dimensional modeling, Snowflake and Databricks implementation, legacy migration, and performance tuning — warehouses your analysts trust and your CFO can afford.
What we build
02
Snowflake & Databricks
Platform implementation with cost governance and workload isolation from day one.
03
Legacy DWH migration
Phased moves off Teradata, Oracle, and SQL Server estates with reconciliation testing.
04
Performance tuning
Query and cost optimization that routinely halves warehouse spend.
How we deliver
Step 01 — Assess
Use-case discovery, data and platform readiness, and a business case with measurable outcomes for data warehousing.
Step 02 — Build
Senior-led delivery in weekly increments — architecture, security, and quality gates baked into every sprint.
Step 03 — Operate
Production monitoring, SLAs, and continuous improvement through our managed services team in Chennai.
Tools & platforms
SnowflakeDatabricksdbtSQL ServerOracleFivetranPower BI
In depth
Modern data warehousing: from nightly batch to governed lakehouse
The warehouse is still where the business keeps score. What has changed is the architecture: cloud-native engines, ELT over ETL, and lakehouse patterns that hold raw history alongside conformed marts. We design and migrate warehouses that deliver fast, consistent answers — at a cost finance can live with.
Our builds use Snowflake, Microsoft Fabric, BigQuery, and Redshift, modeled with dimensional and data-vault techniques that survive source-system churn. Migrations from on-prem appliances follow a proven path: inventory, automated conversion where safe, parallel-run validation, and cutover with rollback — reporting continuity guaranteed.
Governance is built in: role-based access, column-level security for PII, and cost controls that stop runaway queries. The result feeds BI and analytics today and ML workloads tomorrow, from one governed foundation.
Modeled to last
Dimensional and vault modeling that absorbs source changes without rework.
Validated migrations
Parallel-run reconciliation proves the new warehouse matches the old — before cutover.
Cost-governed compute
Warehouses sized, monitored, and auto-suspended — no surprise bills.
Frequently asked questions
Have a different question? Talk to an engineer, not a salesperson.
How long does a legacy migration take?
A typical mid-size estate migrates in 6–9 months, phased by subject area so the business never loses reporting.
Kimball or Data Vault?
Depends on volatility and audit needs — we model each subject area pragmatically rather than applying one methodology everywhere.
Will costs drop after migration?
Usually — elastic compute plus tuning cuts total cost for most estates; we baseline before and report the delta.