Data Platform Modernization
One governed platform for analytics and AI.
We design and build modern lakehouse platforms that serve BI, machine learning and generative AI from the same governed data, organized into bronze, silver and gold layers, provisioned as code and ready for many teams.
Sound familiar?
The problems we solve
Data in silos
Warehouses, lakes and spreadsheets that each tell a slightly different story.
Platform built by accident
Workspaces, permissions and environments created by hand with no standards.
Unpredictable spend
No tagging, budgets or workload sizing, so costs surprise finance every month.
Not AI-ready
Data that cannot be found, trusted or accessed safely by data science teams.
Outcomes
What you get
- A single source of truth for analytics and AI
- Clear separation of raw, refined and business-ready data
- Infrastructure as code, with repeatable environments
- Predictable, attributable cloud spend
Approach
How we deliver it
- 1
Choose with evidence
Vendor-neutral platform assessment against your cloud, skills and workloads.
- 2
Lay the foundation
Networking, security, identity, environments and CI/CD as code.
- 3
Model the layers
Bronze, silver and gold standards, naming and ownership.
- 4
Enable teams
Templates, guardrails and training so new teams onboard in days.
Deliverables
What we hand over
Working software, tests and documentation in your repositories and cloud accounts.
Technologies
- Databricks
- Snowflake
- Microsoft Fabric
- Google BigQuery
- Delta Lake
- Apache Iceberg
- Unity Catalog
- Terraform
- Platform architecture
- Reference architecture, environment strategy, networking and security design.
- Medallion data model
- Bronze, silver and gold layers with naming, ownership and quality standards.
- Infrastructure as code
- Terraform modules for workspaces, storage, compute and permissions.
- Cost management
- Workload sizing, tagging, budgets and dashboards so spend stays predictable.
- Platform enablement
- Templates, CI/CD and training so your teams can ship safely on their own.
Use cases
Where this makes a difference
Enterprise lakehouse foundation
A multi-domain platform with shared governance and self-service workspaces.
Data mesh enablement
Domain-owned data products on a common platform with central guardrails.
Cost optimization
Workload right-sizing and storage tiering on an existing platform.
FAQ
Common questions
Which platform should we choose?
It depends on your cloud, skills and workloads. We run a short, vendor-neutral assessment and recommend with reasons.
Do we need a lakehouse or a warehouse?
If you plan to run machine learning or generative AI alongside BI, a lakehouse usually fits better. For pure reporting, a cloud warehouse can be simpler.
Can you work with our existing platform?
Yes. Many engagements start by standardizing and hardening a platform that already exists.
Related services
All services →Data Engineering
Reliable batch and streaming pipelines that move data from every source into your platform, tested and observable from day one.
Batch and streaming pipelines you can trust
ETL & Data Integration
Integrate every system and modernize legacy ETL (Informatica, SSIS, DataStage, Talend) into maintainable, cloud-native ELT pipelines.
Modernize ETL to cloud-native ELT
Big Data Migration
Move Hadoop, Teradata, Netezza, Exadata and on-premises warehouses to a cloud lakehouse safely, with parallel runs and zero-surprise cutovers.
Hadoop and legacy warehouses to the cloud
Tell us where your data is today.
A 45-minute conversation with a senior engineer. You leave with an honest view of what to build first, whether or not we work together.