Data platform modernization

Move the enterprise off legacy data debt — without pausing the business

Connexis designs and builds lakehouse platforms on Azure and Databricks for enterprises and digital natives. Migration engineering, governed data products and revenue analytics that finance will sign off on.

6 TB+
Largest estate migrated
16%
Typical run-cost reduction
Reference data flow

Sources

SAP · EPOS · Stripe · events

Ingest

Auto Loader · ADF · CDC

Lakehouse

bronze → silver → gold

Governance

Unity Catalog · lineage · quality

Consumption

Power BI · semantic layer

ML & activation

MLflow · reverse ETL

Hyperscalers & platforms we engineer on

Microsoft AzureDatabricksDelta LakeUnity CatalogAzure Data FactoryEvent Hubs / KafkadbtMLflowPower BITerraformSnowflakeAWS

Services

Built by engineers, scoped for engineering leaders

No 80-slide transformation decks. Small senior teams, working software in weeks, and an architecture your platform group can defend in review.

Lakehouse architecture

Target-state design on Azure and Databricks: medallion layers, Unity Catalog governance, cost guardrails and a reference implementation your teams can extend.

Legacy migration engineering

Teradata, Informatica, SSIS, Hadoop and Synapse estates moved to Delta Lake with automated inventory, parity testing and wave-based cutover.

Revenue & commercial analytics

Gross-to-net revenue models, trade spend, subscription recognition and cohort economics — one definition consumed by finance, growth and product.

Data & ML engineering

Streaming and batch pipelines, dbt modelling, feature stores and MLflow-tracked models running in production, not in notebooks.

Governance & platform ops

Access models, lineage, PII handling, SOX-ready audit trails and FinOps reporting embedded from day one rather than bolted on.

Platform enablement

Golden paths, CI/CD with Terraform and Asset Bundles, and hands-on enablement so your engineers own the platform after we leave.

Reference architecture

A lakehouse blueprint we deploy and adapt

Sources

  • ERP / SAP
  • EPOS & distributor feeds
  • Shopify, Stripe
  • Events & clickstream

Ingest

  • Auto Loader
  • ADF & Event Hubs
  • CDC replication
  • Schema evolution

Lakehouse

  • Bronze / silver / gold
  • Delta Lake + dbt
  • Unity Catalog
  • Quality expectations

Consumption

  • Revenue cockpit
  • Semantic layer
  • ML & forecasting
  • Reverse ETL

How we work

Five stages, each with a decision gate

Every engagement is time-boxed and reversible. You can stop after the diagnostic and keep the plan.

Typical delivery cadence

Discovery

W1–W2

Architecture & landing zone

W2–W5

Wave 1 migration

W5–W12

Parallel run & sign-off

W10–W14

Every lane ends in a decision gate — stop, extend or scale.

  1. 01

    Diagnose

    Two weeks. Estate inventory, cost baseline, data-quality hotspots and the reporting flows that actually run the business.

  2. 02

    Architect

    Target lakehouse blueprint, governance model, migration waves and a business case with explicit run-cost projections.

  3. 03

    Build in waves

    Thin vertical slices per domain: ingest, model, validate, expose. Parity harness proves the new number matches the old one.

  4. 04

    Cut over

    Dual-run, sign-off gates, decommission plan. No reporting blackout, no big-bang weekend.

  5. 05

    Hand over

    Runbooks, CI/CD, on-call model and enablement sessions. Your team runs it; we stay available for the hard weeks.

Start here

A two-week diagnostic of your data estate

You get a current-state map, a target lakehouse architecture, a migration wave plan and a run-cost model. Fixed price, no lock-in, yours to keep.