6,000+
Team members
25+
Network countries
200+
Private aviation terminals
A large private aviation enterprise was running its analytics environment on Azure Databricks and Azure Data Factory, but the platform lacked the governance, security, and architectural maturity needed to support enterprise-scale operations. Raw data from 40+ heterogeneous sources was being used without cleansing, KPIs were built on unreliable Bronze data, and development and production workloads shared the same infrastructure with no access controls. Inferenz modernized the platform end-to-end, delivering a governed, multi-environment Databricks Lakehouse with automated pipelines, Unity Catalog governance, and business KPIs surfaced directly in Dynamics 365 CRM.
The client's analytics environment lacked governance, security, and architectural maturity. Four core problems blocked reliable, scalable analytics.
Development and production workloads shared a single Unity Catalog workspace with no environment segregation, no role-based access controls, and no protection against developers inadvertently affecting live data.
Raw data was loaded directly into Bronze with no medallion architecture, no schema documentation, and no incremental history. Every refresh truncated and reloaded point-in-time snapshots with no traceability between tables.
Development and production shared the same cluster infrastructure, creating resource contention and uncontrolled releases. No CI/CD pipeline existed to promote notebooks and workflows safely across environments.
KPIs and reporting views were built directly on raw Bronze data from 40+ heterogeneous sources with mismatched data types, no cleansing, and no standardization, leading to unreliable outputs and an inability to scale analytics.
Inferenz modernized and scaled the analytics platform end-to-end, delivering a governed, multi-environment Databricks Lakehouse with automated data pipelines, enterprise-grade security, and business-ready KPI outputs connected to the CRM system.
Designed a four-environment architecture (Dev, Test, UAT, Prod) on separate Azure VNets using Microsoft's Cloud Adoption Framework. All environments are fully isolated with governed Bronze and Silver data layers, with role-based access controls enforced via Unity Catalog and Databricks Asset Bundles.
Built a scalable, config-table-driven framework to standardize pipeline orchestration. Over 20 automated Bronze-to-Silver workflows execute scheduled data cleansing across six rule-based cleansing groups, eliminating manual data handling and accelerating onboarding of new sources without code changes.
Established enterprise data governance using Unity Catalog covering data ownership, full lineage tracking across external tables in ADLS Gen2, and centrally managed role-based access. An external table strategy ensures data persists independently of catalog operations, providing a reliable backup in ADLS storage.
Orchestrated end-to-end ingestion via ADF from 9 curated sources into Bronze. Applied rule-based validations and automated Silver-layer cleansing for fully client-approved data quality. Delivered 16 KPIs including net revenue, market flight share, and promoter scores, computed on Silver data and surfaced in Dynamics 365 CRM.
Implemented a full CI/CD pipeline using GitHub Actions with self-hosted runners and Databricks Asset Bundles for deterministic promotion of notebooks, workflow jobs, clusters, and access configurations across environments. Git integration is scoped exclusively to Development to maintain production integrity.
All infrastructure provisioned via Terraform. Databricks workspaces are accessible only within a private VNet via VPN. All ingress and egress filtered through Azure Firewall and App Gateway. Private endpoints configured for workspaces and storage. User-assigned managed identities enforce least-privilege access with zero dependency on individual user credentials.
for security and compliance
Private-network-only workspace access with catalog-level controls and least-privilege managed identity enforcement across all environments.
automated pipelines
Metadata-driven Bronze to Silver pipelines eliminated manual intervention and reduced schema-error risk across all data sources.
for business-ready intelligence
Trusted KPI metrics computed on curated Silver data and delivered directly into Dynamics 365 CRM for operational decisions.
in data quality and governance
Schema validation, cleansing rules, and Unity Catalog lineage ensure end-to-end auditability and consistent data quality across all layers.
Whether you’re starting with data modernization or exploring AI copilots, we’re here to help.
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