Enterprise data warehouses and lakehouses built for analytics at scale.
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§ Signs
Our data warehouse development services give your business one governed place for analytics. We implement enterprise data warehouses and data lakehouse architecture on Snowflake, Databricks, Microsoft Fabric and Azure Synapse, model data with star schemas or Data Vault, and test it with data quality frameworks. For existing platforms, we deliver data warehouse performance optimization and pipeline cost optimization, including Snowflake and Databricks platform optimization. Whether you need a new warehouse, a data lakehouse implementation or a faster, cheaper version of the one you have, we design it around the questions your teams need to answer.
A warehouse earns its place when questions cross systems and history matters.
Monthly numbers are assembled by hand from exports, and only one person knows how.
CRM, ERP, finance and product data live apart, so cross-system questions take days.
Analytics queries run against the production database and slow it down for customers.
Finance and sales report different figures for the same period.
You can see today's state, but not how a customer, price or account looked last quarter.
§ What we deliver
Choose an area to see the services in it.
Where we build.
Azure Synapse Analytics§ Proof
Featured case study
This sports intelligence platform is designed to deliver real-time sports data, advanced analytics, and a community-driven prediction ecosystem.
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§ Approach
We map the questions, KPIs, sources and users the warehouse must serve.
You receive Requirements and source-to-target map
Platform, warehouse or lakehouse layers, modeling approach and security are designed.
You receive Warehouse architecture and data model
We design the dimensional or Data Vault model with the business owners, starting from the questions the warehouse must answer.
You receive Logical model and bus matrix
Pipelines and models are built subject area by subject area, with tests.
You receive Tested subject areas released in iterations
We tune queries, storage and compute, and set guardrails for cost.
You receive Performance and cost optimization report
Monitoring, data quality alerts and a roadmap for the next sources keep the warehouse useful after launch.
You receive Monitoring, quality alerts and a source roadmap
§ Tech stack
Microsoft FabricAzure Synapse Analytics
SQL Server
dbt
Power BIAzure Data Factory§ Engagement models
A defined scope, timeline and deliverables, from design through go-live.
An agreed price for a clearly scoped implementation, such as a migration or a new warehouse.
Our engineers work under your contract, or as a subcontractor to consulting firms and system integrators.
Data engineers who join your team and work in your tools and processes.
Consultants and developers working remotely, with overlap in your working hours.
Architecture reviews, platform choices and design guidance for your own team.
Monitoring, incident handling and ongoing fixes for pipelines and databases in production.
A short, fixed-scope engagement to test an approach on your data or plan a migration.
§ FAQs
A warehouse suits structured, reporting-heavy workloads; a lakehouse adds open file formats and suits mixed data, streaming and machine learning. We implement both, and many platforms combine them.
Snowflake, Databricks, Microsoft Fabric, Azure Synapse Analytics and SQL Server.
Yes. Data warehouse performance optimization and data pipeline performance and cost optimization cover query tuning, model and storage design, compute sizing and scheduling, including Snowflake and Databricks platform optimization.
Star schema and dimensional modeling for reporting, and Data Vault where many sources and full history matter, built and tested in dbt.
Yes. Legacy data warehouse modernization is covered by our cloud data migration services.
We deliver one subject area first, such as sales or finance. That usually reaches users within weeks, not months. Each further subject area reuses the shared dimensions, so later areas move faster.
Yes, where the business needs it. We use streaming or frequent micro-batch loads for the tables that need to be fresh and keep the rest on cheaper scheduled loads.
Tests on keys, relationships and business rules run with every load, reconciliations compare totals with source systems, and failures alert the table's owner.
You do. The code is in your repository, the platform is in your cloud account, and the documentation is written for your team.
A defined first subject area can be fixed price. Ongoing expansion suits time and materials or dedicated engineers. We estimate after a discovery call and state the assumptions.
§ In detail
The questions, measures and owners the warehouse serves, agreed before modeling starts.
Platform choice, layers, environments and security, written up as a short design document.
Star schemas or Data Vault, conformed dimensions and slowly changing dimensions where history matters.
Ingestion, dbt transformations and orchestration, all in version control.
Tests and reconciliations that run with every load and alert the right owner.
Subject-area marts and semantic models that Power BI and other tools can use directly.
Short sessions with business owners to agree what each measure and dimension means.
Sales, finance or operations first, so value arrives in weeks and the model is proven before it grows.
Your team reviews and contributes code, so they can extend the warehouse after we step back.
Every table has a named owner and documented sources.
General ledger, consolidations and regulatory reporting with full history.
Sales, inventory, returns and marketing joined for margin and customer analysis.
Operational and activity data with identifiable fields restricted, designed around HIPAA and state privacy rules.
Subscriptions, product usage and support data combined for revenue and retention reporting.
§ Contact
Tell us what your teams need to report on and where the data lives today.
