Databricks Consulting
Databricks Consulting Services
We help UK enterprises design, implement, and optimise Databricks Lakehouse architectures — unifying data engineering, analytics, and machine learning on a single governed platform built for production workloads.
Why organisations need Databricks consulting
Databricks has become the platform of choice for organisations that need to unify data engineering, data science, and AI on a single platform. The Lakehouse architecture eliminates the traditional split between data lakes and data warehouses, providing a single governed layer for all analytical workloads.
However, implementing Databricks effectively requires deep expertise in Spark optimisation, Delta Lake, Unity Catalog governance, and MLflow-based ML operations. Without this expertise, organisations end up with poorly performing clusters, ungoverned data, and ML workflows that cannot move from notebook to production.
We work with organisations at every stage of Databricks adoption — from initial architecture design and migration planning through to production optimisation and ML pipeline engineering. Our focus is always on building systems that work reliably in production, not just in development notebooks.
What we deliver
Lakehouse Architecture Design
We design and implement medallion architectures (bronze, silver, gold layers) using Delta Lake for ACID transactions, time travel, and schema enforcement. Our designs include proper partitioning strategies, file compaction, vacuum policies, and Z-ordering for optimal query performance.
Unity Catalog & Governance
We implement Unity Catalog for centralised governance across your entire Databricks environment — including fine-grained access control, data lineage tracking, data classification, audit logging, and cross-workspace data sharing. Governance that enables rather than restricts.
ML & AI Production Pipelines
We build production ML workflows using MLflow for experiment tracking, Feature Store for consistent feature engineering, and Model Serving for low-latency inference. We bridge the gap between data science experimentation and production deployment.
Migration to Databricks
We migrate from legacy Hadoop clusters, standalone Spark deployments, or other platforms to managed Databricks. This includes workload assessment, cluster sizing, code migration, performance optimisation, and governance setup.
Databricks expertise
Our engineers work with Databricks daily in enterprise production environments. We bring deep knowledge of Spark internals, Delta Lake optimisation, and the full Databricks platform ecosystem.
Example engagement
Lakehouse Implementation — Financial Services
Databricks + Delta Lake + Unity Catalog + MLflow + Terraform
A mid-sized financial services company had fragmented Spark clusters across multiple teams with no governance, inconsistent data, and no ability to move ML experiments into production. Data scientists and data engineers were working on completely separate platforms with no shared infrastructure.
Challenges
- — Fragmented Spark clusters with no centralised governance
- — Data science and engineering on completely separate platforms
- — No reproducibility for ML experiments
- — Ungoverned data access with no audit trail
- — Manual, error-prone deployment processes
Outcomes delivered
- ✓ Unified Lakehouse platform serving all teams
- ✓ Unity Catalog governance across 200+ tables
- ✓ MLflow-based experiment tracking and model registry
- ✓ Automated CI/CD for notebook and pipeline deployment
- ✓ 3 ML models moved from notebook to production serving
How we approach Databricks engagements
We begin every Databricks engagement with a workspace assessment — reviewing your current cluster configurations, job scheduling, data organisation, governance setup, and cost structure. This identifies quick wins and informs the target architecture design.
For new Databricks implementations, we design the Lakehouse architecture from scratch — defining medallion layers, Unity Catalog structure, workspace organisation, cluster policies, and access patterns. We implement everything using infrastructure as code (Terraform and Databricks Asset Bundles) for reproducibility and version control.
For ML-focused engagements, we establish the full MLOps lifecycle — from feature engineering through experiment tracking, model validation, and production serving. Our goal is to reduce the time from data science idea to production deployment from months to days.
Ready to build your Lakehouse?
Book a free technical discovery call to discuss your Databricks environment and goals.
Book a Discovery CallFrequently Asked Questions
What is a Lakehouse architecture?
A Lakehouse combines the best of data lakes (low-cost storage, schema flexibility, support for unstructured data) with the best of data warehouses (ACID transactions, schema enforcement, fast SQL queries). Databricks implements this through Delta Lake — providing reliable, governed, performant data storage on cloud object storage.
Should I use Databricks or Snowflake?
It depends on your workload mix. Snowflake excels at SQL analytics and structured data warehousing. Databricks excels at unified workloads — combining data engineering, data science, and ML on one platform. Many enterprises use both. We can help you assess which fits your specific requirements, or design a architecture that leverages both.
How does Unity Catalog compare to other governance tools?
Unity Catalog is Databricks-native governance that provides fine-grained access control, data lineage, auditing, and cross-workspace sharing. It is tightly integrated with the Databricks platform which makes it simpler to implement than external governance tools. For Databricks-centric organisations, it is typically the best choice.
Can you help move ML models from notebooks to production?
Yes — this is one of our core capabilities. We implement MLflow for experiment tracking and model registry, build automated validation pipelines, configure Model Serving endpoints, and establish monitoring for model performance in production. The goal is a repeatable, auditable path from experiment to deployment.
How do you optimise Databricks costs?
We implement cluster policies with autoscaling limits, spot instance strategies, job cluster isolation (preventing expensive interactive clusters from running ETL), photon enablement for SQL workloads, and regular compute utilisation reviews. We also optimise Delta tables for query performance which reduces compute time.
Do you work with Databricks on AWS, Azure, or GCP?
We have production experience with Databricks on both AWS and Azure. Our architectural approaches and governance frameworks work across cloud providers, though specific integration patterns differ. We select the cloud provider based on your existing infrastructure and requirements.