Databricks Built for Production

We design and operate Databricks lakehouses that carry real production load. That means medallion architecture with clearly separated bronze, silver and gold layers, Delta Lake tables that stay fast as they grow and a governance model your security team can actually sign off on.

Our team holds the Databricks Certified Data Engineer Associate and Databricks Certified Associate Developer for Apache Spark credentials. We have spent eight years building Spark workloads, and most of our engagements begin by cutting the cost and runtime of pipelines somebody else already built.

What We Do With Databricks

Lakehouse Architecture

Medallion lakehouse design on Delta Lake with clear bronze, silver and gold separation.

Medallion architecture design
Delta Lake table design and partitioning
Schema evolution and time travel
Multi-workspace and environment strategy

Spark Optimization

Cutting runtime and cost on Apache Spark jobs through tuning and better physical design.

Query and job profiling
Shuffle and skew remediation
Photon and cluster right-sizing
Autoscaling and spot strategy

Unity Catalog Governance

Centralised governance, lineage and access control across every workspace.

Unity Catalog rollout and migration
Row and column level security
Data lineage and audit
Catalog, schema and grant design

Structured Streaming

Near real-time ingestion and processing with Structured Streaming and Auto Loader.

Auto Loader ingestion
Streaming aggregations and watermarks
Change data capture
Exactly-once delivery patterns

ML and GenAI Enablement

Preparing the lakehouse for machine learning and generative AI workloads.

Feature engineering pipelines
MLflow tracking and model registry
Vector search readiness
Notebook to production promotion

Cost Optimization

Making Databricks spend predictable and defensible.

Cluster policy design
Job cluster versus all-purpose review
DBU consumption analysis
Storage and compaction tuning

What You Get

A documented lakehouse architecture with the reasoning behind each decision
Delta Lake tables designed for the query patterns you actually run
Spark jobs that are measurably faster and cheaper than when we started
Unity Catalog governance covering access, lineage and audit
CI/CD for notebooks, jobs and infrastructure
Handover documentation and enablement for your own engineers

AI & GenAI on Databricks

AI on a lakehouse works best when the model reads from data that is already governed, so nothing needs copying out to answer a question. We build the retrieval side of that: vector indexes kept in step with Delta tables, embeddings refreshed by the same jobs that load the silver layer and Unity Catalog permissions enforced at retrieval time rather than trusted to the prompt.

Model serving endpoints for retrieval-backed applications
Vector Search indexes synced from Delta tables
Feature engineering and feature serving for ML workloads
MLflow tracking, model registry and evaluation harnesses
RAG pipelines grounded in Unity Catalog governed data
PII redaction before content reaches an index

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Databricks Consulting & Lakehouse Architecture