ENTERPRISE SOLUTIONS · LAKEHOUSE ARCHITECTURE

Databricks Lakehouse & Enterprise Data Engineering

Turn sprawling, fragmented operational databases into a governed, high-throughput lakehouse architecture. We design and operate production-grade data foundations on Databricks, Azure, and GCP that deliver trusted metrics to BI teams and verified ground truth to AI applications.

Databricks Delta Lake PySpark Unity Catalog dbt Azure Synapse / ADF GCP BigQuery Apache Kafka
9B+ Daily Kafka Events Ingested
30 TB Daily Streaming Processing
100% Auditable Semantic Metrics
Sub-Second BI Query Response Times

WHAT WE DELIVER

Production-Grade Lakehouse Architecture Built for Reality

We solve the hard technical and organizational problems that cause 70% of enterprise lakehouse projects to stall between raw ingestion and business adoption.

1. Governed Semantic Layer & Metric Registry

Eliminate cross-departmental metric battles. When Finance, Sales, and Operations calculate the same KPI differently, trust in executive dashboards evaporates.

  • Single, version-controlled definition for enterprise KPIs (OTIF, Gross Margin, Net Churn)
  • Promotion gating from domain data marts to certified Enterprise Gold
  • Decoupled semantic interface serving Power BI, Tableau, and LLM text-to-SQL agents identically

2. Delta Lake Performance & Cost Optimization

Prevent astronomical Databricks cloud compute bills caused by unoptimized shuffles, full-table scans, and the "small files problem."

  • Z-Ordering and Liquid Clustering on high-cardinality join and filter keys
  • Partition pruning and auto-compaction routines tailored to your ingestion cadence
  • ACID incremental streaming merges with idempotent state management

3. Legacy Modernization & Entity Disambiguation

Ingest from fragile, undocumented legacy ERPs (SAP ECC, Oracle EBS), on-premise WMS, and bespoke databases without impacting operational systems.

  • Change Data Capture (CDC) and micro-batching without production lockouts
  • Conformed dimension modeling that unifies conflicting customer and SKU identifiers
  • Codification of undocumented business logic into testable transformation code

4. Automated Data Assertions & Pre-Serving Contracts

Stop bad data before it poisons executive reports or triggers catastrophic automated actions.

  • Automated schema validation, uniqueness assertions, and null thresholds
  • Quarantine pipelines for malformed payloads with automated ops alerts
  • Data freshness heartbeats with explicit SLA status flags

BLUEPRINT

The Enterprise Lakehouse Data Flow

[Source Systems: SAP, Oracle, WMS, Databases, Kafka Feeds, 3PL APIs] │ ▼ (Non-intrusive CDC, micro-batching & raw validation) [Bronze / Raw Layer: Immutable, Append-Only Historical Event Feeds] │ ▼ (Entity disambiguation, conformed schemas, contract checks) [Silver Layer: Cleaned, Auditable Conformed Dimensional Models] ├── Conformed Master Dimensions (Customer, Product, Facility, Date) ├── Lifecycle Event Facts (State transitions with microsecond timestamps) └── Automated Quarantine Table (Malformed records isolated with root-cause tags) │ ▼ (Governed metric definitions, aggregation paths, access controls) [Gold Layer & Enterprise Semantic Engine] ├── Unity Catalog / dbt Semantic Layer (Certified KPI registry) ├── Optimized Data Marts (Liquid clustering for sub-second BI response) └── AI & Feature Store (Normalized ground truth for predictive ML and RAG) │ ┌─────────────────────┴─────────────────────┐ ▼ ▼ [Executive BI & Self-Service] [AI Agents & Advanced ML] • Power BI & Tableau certified models • Text-to-SQL querying verified schemas • Zero metric discrepancies across teams • Predictive forecasting & anomaly detection

FIELD EVIDENCE

Proven in Production at Massive Scale

REAL-TIME AT SCALE

9B+ Kafka Messages / 30 TB Daily

How we designed a streaming platform handling 9 billion daily operational events, solving incremental ACID merges into live customer analytics with zero downtime.

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ENTERPRISE ARCHITECTURE

AI-Ready Semantic Foundation

How we unified disconnected legacy ERPs and operational silos into a governed semantic layer where proprietary data becomes a true competitive moat.

Read Case Study →

Planning a Databricks migration or lakehouse refactor?

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