Unified Data Platform, Analytics & MLOps on Azure Databricks
From raw data ingestion to executive dashboards, controlled A/B experimentation, and production MLflow pipelines—we architect, build, test, and support your entire data lifecycle.
High-Level Consulting & Solution Architecture
We eliminate fragmented data silos by designing a unified Azure Databricks Lakehouse architecture that aligns data engineering, business intelligence, and machine learning under one governance layer.
Medallion Lakehouse Foundation
Structured Bronze, Silver, and Gold Delta tables on ADLS Gen2 secured with Unity Catalog RBAC, PII masking, and automated end-to-end data lineage.
Unified Compute & BI Layer
Scalable PySpark distributed processing paired with serverless Photon-accelerated Databricks SQL warehouses for sub-second executive analytics.
Production MLOps & CI/CD/CT
Closed-loop MLflow experiment tracking, statistically validated A/B rollouts, and automated CI/CD/CT model deployments with drift-triggered retraining.
Core Services & Low-Level Engineering Design (LLD)
Production-grade engineering blueprints tailored to transform raw enterprise data into governed KPIs, validated experiments, and self-service intelligence.
Setting Up an Enterprise Data Platform
Identifying Key Metrics & Strategic Data Analysis
Our Approach: Translate executive business goals into governed, single-source-of-truth KPIs and uncover actionable growth levers from historical data.
Low-Level Design: Semantic Gold star-schemas in Databricks SQL tracking customer lifetime value (LTV), churn propensity, funnel conversion, and unit economics, backed by PySpark cohort and root-cause correlation models.
Running A/B Tests & Analyzing Experiment Results
Our Approach: Validate product changes, pricing strategies, and ML model upgrades through statistically rigorous controlled experiments.
Low-Level Design: Deterministic user/traffic splitting in PySpark, full parameter and metric logging in MLflow, and automated statistical significance pipelines (Welch’s t-test, Bayesian uplift, and Sample Ratio Mismatch checks).
Creating Dashboards & Visualizing Data
Dev/Test/Prod Support & End-to-End Testing Solutions
Keep your data pipelines, dashboards, and ML models running reliably across every environment with proactive SLA support and four-layer automated validation.
Dev, Test & Production Fixes Support
- Dev & Sandbox Support: Resolve PySpark wheel/dependency conflicts, configure Unity Catalog permissions, tune Photon cluster policies, and standardize MLflow tracking.
- QA & Staging Support: Fix DLT schema evolution failures, eliminate PySpark data skew and shuffle bottlenecks, and debug MLflow Model Registry staging webhooks.
- Production Fixes (L3 / SRE): Rapid root-cause resolution for executor Out-Of-Memory (OOM) crashes, broken upstream API schemas, instant MLflow model rollbacks on drift, and automated Continuous Training (CT) triggers.
End-to-End (E2E) Testing & Quality Assurance
- Data & Schema Testing: Automated PySpark and DLT expectations verifying null thresholds, primary key uniqueness, and feature distribution drift.
- SQL Metric Reconciliation: Automated cross-checks reconciling Databricks SQL dashboard KPIs against source ERP/CRM databases.
- A/B & MLflow Quality Gates: Pre-deployment checks enforcing statistical power, Sample Ratio Mismatch (SRM) integrity, model accuracy thresholds, and slice fairness.
- Load & Chaos Testing: High-concurrency stress testing across Databricks SQL warehouses and Model Serving endpoints to guarantee SLA latency under peak traffic.
Flexible Consulting, Build & Managed Support Plans
Start with a focused Lakehouse architecture blueprint, commission a full custom build, or retain dedicated L2/L3 SRE engineers.
Architecture & KPI Blueprint
Ideal for teams needing a clear Azure Databricks roadmap, KPI definitions, and FinOps cost optimization plan.
- Current Data & Cloud Readiness Audit
- High-Level Lakehouse & Unity Catalog HLD
- Core Business KPI & Gold Schema Mapping
- Cluster Sizing & Photon Cost-Reduction Plan
- 90-Day Phased Implementation Roadmap
Lakehouse, BI & MLOps Delivery
Complete production setup of your Azure Databricks platform, PySpark pipelines, SQL dashboards, and MLflow A/B engine.
- Terraform IaC & Medallion Pipeline Setup
- PySpark Auto Loader & DLT Quality Rules
- Executive Databricks SQL AI/BI Dashboards
- MLflow Tracking & A/B Experimentation Engine
- Full 4-Layer E2E Automated Testing Suite
Dev/Test/Prod Support & SRE
Dedicated L2/L3 engineering coverage to resolve pipeline failures, optimize queries, and monitor model drift 24/7.
- Dev & QA Environment Debugging Support
- L3 Production RCA for OOM & Skew Issues
- Automated Drift Monitoring & MLflow Rollbacks
- Ongoing SQL Dashboard & KPI Enhancements
- Dedicated Incident Response SLA Tiers
Schedule Your Azure Databricks Consultation
Share your current data stack, bottlenecks, or project timeline. Our principal data and MLOps architects will prepare a tailored scope and custom quote within 1 business day.
- Direct consultation with Principal Databricks & PySpark Architects
- Complimentary 30-minute Lakehouse & FinOps posture review
- NDA-protected architecture & low-level design scoping
