Your data and AI platform shouldn't hold you back. We architect, build, and optimise Databricks lakehouses — with Unity Catalog, MLflow, and Delta Lake — so your teams ship models, not tickets.
The Reality
Your teams aren't failing because they lack talent. They're failing because the platform underneath them was never designed for AI-scale workloads. These problems compound — and they're costing you millions in wasted compute and lost opportunity.
Your data lives in 5 different systems — Snowflake, S3, on-prem databases, legacy warehouses — and nobody trusts a single number.
Data scientists build models in notebooks. Engineering can't deploy them. Most ML projects never make it past the prototype stage.
No unified catalog. No lineage. No access controls that work across data and AI assets. Compliance audits are a scramble every quarter.
Clusters running 24/7, no autoscaling, no job-level cost attribution. Your cloud bill grows meaningfully year-over-year and nobody knows why.
ETL jobs fail overnight. Nobody notices until a dashboard shows stale data on Monday morning. There's no alerting, no SLA monitoring.
Batch pipelines run once a day. Your fraud detection, pricing engine, and recommendation system are always 24 hours behind.
Tableau and Power BI query raw tables instead of a governed semantic layer. Every analyst writes their own SQL — and gets different answers.
Models are deployed with no versioning, no lineage, and no audit trail. You can't explain to a regulator what your model does or why.
Our Expertise
Design and deploy a unified lakehouse that replaces fragmented data warehouses and data lakes with a single, governed platform built on Delta Lake.
Build production-grade ML pipelines with MLflow — from experiment tracking and model registry to automated retraining and monitoring.
Build reliable, scalable data pipelines with Spark, Delta Live Tables, and Databricks Workflows — from ingestion to transformation.
Deploy and manage large language models on Databricks — fine-tuning, RAG pipelines, vector search, and production serving with governance.
Databricks + AI
Databricks isn't just a data warehouse — it's an AI platform. From feature engineering and model training to LLM fine-tuning and real-time inference, we build the infrastructure that turns your data into production AI.
Explore AI CapabilitiesGo beyond SQL and dashboards. Bring your own models into Databricks — or use built-in AI functions for classification, summarisation, and entity extraction.
Track every experiment, compare model runs, and promote the best performers to production — all within a governed, reproducible workflow.
Govern data, models, and AI assets from a single control plane. Fine-grained access, lineage tracking, and compliance — built in, not bolted on.
Deploy models to production endpoints with autoscaling, A/B testing, and real-time monitoring — so your AI delivers value, not just predictions.
Databricks Ecosystem
Databricks doesn't operate in isolation. We integrate it with your entire data ecosystem — ingestion, transformation, governance, BI, and AI serving — so every layer works together.
+ native connectors to data sources via Databricks Partner Connect
Our Process
We audit your current data infrastructure — sources, pipelines, governance gaps, and ML readiness — and deliver a prioritised lakehouse roadmap.
We design your lakehouse architecture: medallion layers, Delta Lake schemas, Unity Catalog policies, and compute strategy — tailored to your workloads.
Our engineers build the pipelines, migrate data from legacy systems, implement Delta Live Tables, and deploy MLflow for model tracking.
We go live — connecting Databricks to your BI layer (Tableau, Power BI), downstream applications, and alerting infrastructure.
Post-launch, we tune cluster configurations, optimise query performance, reduce cloud costs, and expand the platform as your AI ambitions grow.
Case Study
A global pharmaceutical company was running 12 disconnected data sources across 3 cloud providers. ML model deployment took 6 months. We built a unified lakehouse on Databricks, implemented Unity Catalog for GDPR compliance, and deployed MLflow — cutting model deployment time by 75%.
4×
Faster ML deployment
90%
Less data duplication
12→1
Unified platform
GDPR
Compliant from day one
Faster ML model deployment
Unified · Governed · AI-Ready
How We Work
Pick the model that fits where you are. All engagements include a dedicated Databricks lead and a clear outcome definition.
Ideal for: Teams already on Databricks who need an expert audit
A 2-week deep dive into your Databricks environment — cluster config, pipeline health, Unity Catalog setup, and cost efficiency — with a prioritised improvement plan.
Ideal for: Organisations migrating to or building on Databricks
A full lakehouse build — from architecture and migration through to MLflow, governance, and BI integration — delivered in 8–12 weeks with a dedicated engineering team.
Ideal for: Teams that want expert-managed Databricks operations
We manage your Databricks platform end-to-end — monitoring, optimisation, ML pipeline operations, and a dedicated engineering partner on call.
Common Questions
Databricks is the data + AI platform built on the lakehouse architecture — combining the structure of a data warehouse with the scale and flexibility of a data lake. It spans data engineering (Delta Lake, pipelines, streaming), machine learning (MLflow, Mosaic ML, Unity Catalog), generative AI (model serving, RAG, agents), and analytics (Databricks SQL, AI/BI Genie). Decision Foundry is a Databricks Premier Partner.
Lakehouse architecture and modernisation (migration from Hadoop, Cloudera, on-prem warehouses); MLOps and AI platform engineering on Mosaic ML and MLflow; data engineering and pipeline operations; LLMOps and generative AI engineering; Data Intelligence and BYOM (Bring Your Own Model) integration; experiment management; Unity Catalog governance setup; and ongoing platform optimisation.
Snowflake leads on SQL-heavy analytics, ease of use, and Salesforce integration via zero-copy federation. Databricks leads on data engineering, ML, streaming, and AI / agent workloads. The two are converging (Snowflake added compute-on-data, Databricks added SQL warehousing) but they still have distinct sweet spots. Many enterprises run both — Snowflake for the warehouse and BI layer, Databricks for engineering, ML, and AI.
A focused stand-up (workspace setup, 5–10 pipelines, basic Unity Catalog governance, Databricks SQL) runs 12–16 weeks. A full enterprise build (multi-workspace, complex governance, MLOps platform, real-time streaming, observability) runs 6–10 months. Cost depends on data volume, compute requirements, and which Databricks editions (Premium, Enterprise). Discovery call first.
Yes, and we're vendor-neutral on this — we're a Premier Partner of Databricks AND a Select Partner of Snowflake, so we don't have a one-sided incentive. We typically run a 4–6 week assessment workshop where we evaluate your workload mix (SQL analytics vs ML vs streaming vs governance bar) and make a written recommendation. About 30% of those workshops conclude with "run both, here's the split."
Databricks Premier Partner with deep Mosaic ML, Unity Catalog, and Delta Live Tables expertise. Plus full enterprise data context — Snowflake Select Partner, Salesforce SELECT, AWS / Azure / GCP certifications. Most consultancies specialise in Databricks OR Snowflake OR Salesforce. We genuinely operate across all three. 200+ data projects delivered.