Data Engineering & Platforms

Platform Capability

Databricks

The lakehouse platform for unified data engineering, analytics, and AI.

Discuss a Databricks project

What it is

Databricks unifies data engineering, analytics, and machine learning on a single platform built on open standards — Delta Lake, MLflow, and Unity Catalog. It is the platform of choice when you need large-scale transformation pipelines and production ML in the same environment.

When we recommend it

  • You need large-scale transformation pipelines alongside ML — and do not want to manage two separate platforms.
  • Your data science teams are building models that need to be productionised alongside the pipelines that feed them.
  • You are moving off Hadoop or a legacy Spark environment and want a modern, managed alternative.
  • You need open-format data (Delta / Parquet) that can be queried by multiple tools without vendor lock-in.
  • You want a single governance layer (Unity Catalog) over all your data assets.

What we deliver

Our Databricks capabilities

Lakehouse Architecture

Delta Lake design, medallion architecture (bronze / silver / gold), and data modelling for analytics-ready tables at enterprise scale.

Spark Pipeline Engineering

Batch and streaming pipelines in PySpark and Spark SQL — optimised for cost and performance with auto-scaling clusters and job scheduling.

Unity Catalog & Governance

Centralised metadata, lineage, access control, and audit logging across all your Databricks workspaces using Unity Catalog.

MLflow & Model Lifecycle

End-to-end ML lifecycle management: experiment tracking, model registry, versioning, and deployment via MLflow on Databricks.

Databricks AI / LLM Engineering

Fine-tuning, RAG pipelines, and LLM serving using Databricks Model Serving and Foundation Model APIs — with data staying inside your environment.

Migration from Hadoop / Legacy Spark

Workload migration from on-premise Hadoop, legacy EMR clusters, or early-generation Spark deployments to a managed, governed Databricks environment.

Implementation playbook

From notebooks and clusters to a governed lakehouse operating model

Databricks succeeds when the lakehouse is engineered as a production platform, not a collection of notebooks. We design the data layout, governance model, orchestration, CI/CD, and ML lifecycle so teams can ship reliably.

Discuss this architecture
01

Lakehouse operating model

  • 01

    Workspace, catalog, schema, storage, cluster, job, and permission design across environments.

  • 02

    Bronze, silver, and gold Delta tables with clear contracts, partitioning, compaction, and retention rules.

  • 03

    Batch and streaming pipelines using Spark SQL, PySpark, Delta Live Tables, Workflows, and Auto Loader.

  • 04

    Unity Catalog governance for lineage, fine-grained access, table ownership, audit, and cross-workspace control.

  • 05

    MLflow, feature engineering, model registry, serving, monitoring, and handoff patterns for production ML.

02

Delivery motions

  • Lakehouse architecture and medallion implementation.

  • Hadoop, EMR, legacy Spark, or on-premise pipeline migration to Databricks.

  • Unity Catalog rollout and data governance remediation.

  • Performance tuning for Spark jobs, cluster policies, workload isolation, and cost control.

03

Production handover

  • Workspace and Unity Catalog operating model.

  • Production-grade notebooks, jobs, DLT pipelines, and orchestration workflows.

  • Data contracts, tests, quality gates, and lineage conventions.

  • Cluster policies, cost controls, and performance tuning recommendations.

  • MLflow model lifecycle and deployment runbooks where ML is in scope.

Working with Databricks?

Tell us where you are and what you are trying to build. We will be straight with you about whether Databricks is the right tool for it.

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