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Data & Analytics that drive outcomes

From raw streams to governed metrics and ML features—built for speed, trust and cost control.

Lakehouse architecture

Why it matters

Trusted, fresh, and well-modeled data cuts decision time and powers AI use-cases—while governance and cost controls keep you safe.

  • Data products with clear owners, SLAs and contracts.
  • Discoverable assets with lineage and semantic metrics.
  • Near-real-time where it matters, batch where it pays.
  • FinOps for warehouses & pipelines—no bill shock.

What we deliver

Lakehouse & warehousing
  • Snowflake / Databricks / BigQuery architectures
  • Bronze–Silver–Gold models, Delta/ICEBERG
  • Performance tuning, caching & clustering
Pipelines (batch & streaming)
  • CDC, Kafka/Kinesis/PubSub, orchestration
  • dbt, Airflow, Delta Live Tables
  • Testing, SLAs, data contracts
Governance & quality
  • Catalog/lineage (OpenLineage), ownership & SLAs
  • Row/column security, masking, tokenization
  • DQ rules, anomaly detection, great_expectations
BI & self-service
  • Semantic layers (MetricsLayer/LookML/dbt metrics)
  • Dashboards & exploration (Power BI / Looker / Tableau)
  • Embedded analytics & exports
ML data platform
  • Feature stores, experiment tracking
  • Model registry & serving endpoints
  • Feature computation & online/offline sync
Ops & FinOps
  • Cost visibility & chargeback for data products
  • SLOs for freshness & availability
  • Incident response, runbooks, auto-remediation

Reference lakehouse flow

Ingest → bronze → quality checks → silver marts → semantic layer → BI & ML features—observable with freshness/volume SLOs.

  • CDC + streams for low-latency domains
  • dbt models with tests & docs
  • Semantic metrics for consistent KPIs
Lakehouse flow sketch
Fresh KPIs
Trustworthy metrics & lineage
Near-real-time
When speed matters
−30% cost
FinOps for compute & storage

FAQs

Which lakehouse do you recommend?

We’re platform-agnostic; guide by your constraints (skills, cost, compliance) and interop needs.

How do you enforce quality?

Tests in code (dbt/GE), SLOs for freshness/volume, and alerts into on-call with runbooks.

Can we start small?

Yes—start with one domain and a thin platform slice; expand to additional data products.

Where does AI fit?

Curated features and governed datasets feed ML; we add eval/observability for safe rollout.

Ready to unlock your data?

Describe your domains, sources and BI goals—let’s map a pragmatic roadmap.

Tell us about your goal

We’ll follow up with next steps and a tailored approach.