Your regional team
Six hubs. Four continents.
One delivery team.
Don’t see your region? We’ll route the right partner from anywhere.
Data, Analytics & AI
Data Lakehouse & Warehouse
A modern lakehouse, engineered to compound.
Lakehouse architectures on Databricks, Snowflake, and BigQuery — designed for governed scale, FinOps discipline, and downstream AI workloads.
The case
A lakehouse is more
than a storage decision.
Most lakehouse rebuilds stall at the same point — eighteen months in, the platform is technically sound but operationally thin. Catalogs are partial, lineage is aspirational, governance was a phase-two ticket that got descoped, and the AI workloads it was built to feed quietly went elsewhere.
We engineer for the operating posture from week one. Open formats, governed catalogs, FinOps tagging, and a semantic layer that downstream BI, ML, and AI copilots all consume from one place — built so the next decade of workloads compound on the same spine.
What we build
Governed scale,
engineered from day one.
Reference architecture
Bronze / Silver / Gold patterns on open formats (Iceberg, Delta) with governed catalogs and lineage built in — not retrofitted under audit pressure.
Migration & modernization
Lift-and-shift, replatform, or strangler migration from legacy warehouses to a composable lakehouse — without a freeze on the analytics roadmap.
Semantic & metrics layer
A governed metrics layer that feeds BI, ML features, and AI copilots from one source of truth — versioned, tested, and documented.
FinOps & query optimization
Workload tagging, query rewriting, materialized-view strategy, and per-workload unit economics that survive scale.
Streaming & real-time
Kafka, Kinesis, or platform-native streaming for sub-second materialization where it actually drives a business outcome.
Catalog, lineage & observability
Unity Catalog, OpenMetadata, or platform-native cataloging with column-level lineage and data-product observability.
Reference architecture
The lakehouse stack,
engineered in layers.
Each layer is independent enough to evolve and integrated enough to compound. We adapt the specifics to your cloud, regulator, and team posture.
Bronze — raw landing
Layer 01Schema-on-read landing in open formats with replayable history and tenant-aware partitioning.
Silver — curated
Layer 02Conformed entities, slow-changing dimensions, and quality contracts enforced before promotion.
Gold — analytical & AI-ready
Layer 03Semantic models, governed marts, and feature views that BI, ML, and AI copilots consume in lockstep.
Governance plane
Layer 04Catalog, lineage, classification, and access policy that travel with every dataset and metric.
Operations plane
Layer 05Workload tagging, FinOps observability, alerting, and SLOs treated as first-class platform concerns.
Stacks we work with
The platform choice
is made per estate, not by template.
We carry senior partnerships with Databricks, Snowflake, and Google Cloud — and consciously stay platform-agnostic. The selection is made against your data gravity, regulator posture, and existing skills, not a partnership tier.
Lakehouse & warehouse
Where the data physically lives. The choice is driven by data gravity, regulatory posture, and the SQL fluency of your existing teams — not by which vendor sponsored the most recent conference.
Transformation & modeling
The seam between raw data and analytical truth. Picked for testability, version control, and how naturally the analyst-engineers on your team will pick it up.
Ingestion
How data lands. Driven by source SLAs, change-data-capture needs, and cost-per-million-rows when volumes get serious. Different tools win in different rows of the catalog.
Catalog & governance
What turns a lakehouse from a swamp. Selected for column-level lineage, tag-based access policy, and regulator-readable audit trails — engineered in from week one, never retrofitted under audit pressure.
Quality & observability
How we know the data is right before it reaches a decision. Bias toward declarative contracts in CI and continuous monitoring in production — alarms that fire before the dashboard does.
Activation
Where governed data leaves the lakehouse to drive a workflow — CRM enrichment, lifecycle marketing, finance reconciliation. Reverse-ETL or semantic layer, depending on the consumer.
Outcomes we engineer for
What a hardened
lakehouse pays back.
Numbers below are typical of what we measure on the first 12 months post-cutover. Your numbers will differ — but the categories of leverage are consistent.
70%+
Time-to-insight
Reduction in median time from new question to answered, post-semantic-layer rollout.
30–45%
Compute spend
Cost reduction inside year one through workload tagging, FinOps tuning, and architectural rework.
5×
AI/ML reuse
More features, semantic models, and pipelines reused across teams when the lakehouse is governed end-to-end.
Audit-ready
From day one
Column-level lineage, classification, and access policy in place before the regulator asks.
Where this applies
Any data-rich
operational estate.
Lakehouse architectures are sector-agnostic — the regulatory and data-gravity nuances vary, but the engineering posture doesn't.
- Banking & Capital Markets
- Insurance & Reinsurance
- Healthcare Providers & Payers
- Pharma & Life Sciences
- Manufacturing
- Energy & Utilities
- Retail & Luxury
- Consumer Goods
- Hospitality & Travel
- Telecom & Media
- Logistics & Mobility
- Public Sector
- B2B SaaS
- Higher Education
Related in Data, Analytics & AI
Adjacent capabilities
in this practice.
Data Strategy & Consulting
Capability audits, target-state architectures, and a sequenced investment thesis tied to operating KPIs.
Advanced AI & Analytics
Forecasting, segmentation, and anomaly detection wired into operational workflows.
SAP Data & Analytics
Datasphere, BTP, and SAP Analytics Cloud — engineered into a federated data fabric across SAP and non-SAP.
Start the conversation
From legacy warehouse
to a lakehouse that compounds.
Whether you're modernizing a fragmented estate or scaling a working lakehouse to its next order of magnitude, we'll help you find the lever that pays back fastest.