Data platform development and business intelligence.
Data platform development for companies across the US and Canada. We migrate scattered and legacy data onto modern cloud platforms, build the pipelines that keep it current, and deliver analytics dashboards and BI your team will actually use.
- What is data platform development?
- Building the system that collects data from all your tools, cleans and models it in one place, and serves it to dashboards, reports and AI, with governance so the numbers can be trusted.
- Who it's for
- Data, finance and operations leaders whose reports are slow or disputed, companies moving off legacy warehouses, and teams preparing their data for AI.
- What it costs to get started
- A single-domain migration or first platform workload typically runs $80,000–$220,000 over 8–14 weeks.
Who it's for
Built for teams like yours.
Growing companies
Data spread across a CRM, spreadsheets and ad platforms, and no single report everyone agrees on.
Mid-market and enterprise
Legacy warehouses and nightly ETL jobs that are slow, costly and understood by too few people.
Regulated industries
Finance, healthcare and logistics teams that need lineage, access control and audit trails.
Launching a startup?
We set up analytics and a lightweight data stack early, so product, marketing and finance numbers are trustworthy from your first customers onward.
Outcomes
What changes for your business.
One version of the truth
Shared definitions, so every team reports the same numbers.
Fresher data
Pipelines that update on the schedule your decisions need.
Migration you can verify
Parallel runs and reconciliation before anything old is switched off.
Ready for AI
Clean, governed data that AI and machine learning can use.
What's included
Everything to ship it properly.
Data migration services
Move from on-premise warehouses and legacy ETL to the cloud, domain by domain, with row-level reconciliation.
Lakehouse and warehouse builds
Snowflake, Databricks, Azure Synapse and BigQuery, modeled for the questions your analysts ask.
Data pipelines
Batch and real-time ingestion with tested transformations and alerting when something breaks.
Analytics dashboards and BI
Business intelligence consulting and Power BI, Tableau or Looker dashboards built around real decisions.
Data governance
Cataloging, lineage, quality checks and access control with Purview, Unity Catalog or similar tools.
AI-ready data layer
Feature stores, vector databases and serving layers for machine learning and generative AI.
Tools and platforms
- Snowflake
- Databricks
- Azure Synapse
- BigQuery
- Apache Spark
- Kafka
- dbt
- Airflow
- Power BI
- Tableau
- Looker
- Purview
Pricing
What it costs.
Data platform migration cost by estate size. Typical ranges for the US market in 2026.
| Estate size | Timeline | Typical cost |
|---|---|---|
| Single domain migration (proof of pattern) | 8–14 weeks | $80,000–$220,000 |
| Mid-market estate (4–7 domains) | 6–14 months | $250,000–$1.2M |
| Large enterprise estate with regulatory reporting | 18–30 months | $1.5M–$6M |
Process
How we work.
A weekly rhythm with a named senior lead, working software to review, and a clear next step.
- 01
Inventory
Profile sources, map lineage and usage, and score data quality to see where value is trapped.
- 02
Architecture
Choose the target platform, data model and governance approach, and plan migration by domain.
- 03
Migrate and build
Stand up pipelines and models, run old and new side by side, and reconcile until the numbers match.
- 04
Cut over and activate
Move consumers to the new platform, retire the old one, and build the dashboards and AI use cases on top.
In depth
Modernize your data without losing a record
Years of business logic and history live inside legacy data systems. Starting over isn't an option, but staying on infrastructure that can't support modern analytics or AI isn't either. The hard part is moving forward without breaking what works.
We migrate domain by domain. The old and new platforms run in parallel, automated checks compare results row by row, and no legacy system is switched off until the numbers match over several cycles. Along the way we fix the definitions and data quality issues that made reports disagree in the first place.
The platform is a foundation, not the finish line. Once the data is trustworthy, dashboards, forecasting and AI become much faster to build. For our phase-by-phase approach, read the data platform migration framework.
Related work
Real client projects.
Guides
Further reading.
Data Platform Migration: A Practical Framework for Enterprise Teams
A phase-by-phase framework for migrating from legacy warehouses and ad-hoc pipelines to a modern, AI-ready data platform — including sequencing, validation, and cost control.
Read the guide Big DataWhat a Modern, AI-Ready Data Platform Actually Looks Like
If your data is scattered across 12 tools and three warehouses, no AI model will save you. The foundation comes first — and it's simpler than vendors want you to think.
Read the guide Big DataData Lakehouse vs Warehouse in 2026: Which Architecture Is Right for Your Stack?
Snowflake, Databricks, Microsoft Fabric, BigQuery — the architecture choice you make in 2026 dictates how AI-ready your business is for the next decade.
Read the guideFAQ
Big Data questions.
How much does a data platform migration cost?
Typical US ranges: $80,000–$220,000 for a single-domain migration over 8–14 weeks, $250,000–$1.2M for a mid-market estate over 6–14 months, and $1.5M–$6M for a large enterprise estate with regulatory reporting.
How do you protect data integrity during migration?
We run the old and new platforms in parallel with automated row-level reconciliation. No legacy system is decommissioned until the reconciliation shows zero variance over multiple cycles.
Can you move us from on-premise to the cloud?
Yes. We migrate on-premise and hybrid estates to Azure, AWS or Google Cloud, with audit trails and staged cut-overs.
Will we be locked into one vendor?
We design for portability where it matters: open formats such as Delta and Iceberg, dbt for transformations and standard orchestrators. Platform choice follows your existing footprint and costs.
Do you build the dashboards and AI on top?
Yes. Business intelligence, forecasting and AI use cases are usually the reason for the platform, and our AI and development teams build them on the same foundation.
Start here
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- 150+Projects shipped
- 40+Enterprise clients
- 99.99%Uptime delivered
- 5+Years delivering results