Platform Buildout · Proof of Concept

Databricks Lakehouse PoC

End-to-end pipelines across Bronze, Silver, and Gold, a Databricks SQL Warehouse, and an AI/BI dashboard, built on your actual data. Not a template. Your data and your metrics, working on Databricks.
Talk to an expert
See what’s included
The Outcomes

At the end of the PoC, here's what exists in your Databricks workspace.

The PoC is more than a report. It's a working implementation, with pipelines, metric views, an AI/BI dashboard, a SQL Warehouse, and a Genie agent, all built on your data and your use case. Everything delivered carries straight into the Platform Buildout if you decide to proceed.
01
Running Multi-Hop Pipeline
End-to-end pipelines running on your data through the Bronze, Silver, and Gold layers.
02
Metric Views and Metadata Enrichment
Your key business metrics defined once as Unity Catalog metric views, with table and column descriptions added so both your team and AI tools understand the data.
03
AI/BI Dashboard and SQL Warehouse
A dashboard built on your metric views, plus a SQL Warehouse that exposes your Gold data to your own BI tools and downstream integrations.
04
Genie Agent Setup
A Genie agent configured on your data, so business users can ask questions in plain language and get answers backed by SQL.
05
Enablement Session
A walkthrough of what was built and how, so your team understands the approach, can learn from it, and can put it to use straight away.
06
Findings Report
A summary of the PoC scope, what was built, what was learned, and the recommended next steps for a production Lakehouse.
The Lakehouse Architecture

The PoC builds a working slice of the full Lakehouse

The Databricks Lakehouse architecture unifies data engineering, analytics, and machine learning on a single platform. The PoC implements the core data path that turns raw source data into business-ready insights, using the same patterns the full Platform Buildout extends across your entire data estate.
Talk to an expert
BI
Reports
Data Science
Machine learning
Metadata and Governance Layer
Data Lake
Structured, Semi-structuredand Unstructured Data
The Process

Four steps.
A working Lakehouse on your data.

The PoC follows a structured sequence. We work with your team to select the use case and define the right scope, and implementation runs against your actual data. The final session is a walkthrough with your team, not just handover document.
step 00
Pre-requisites & Access
You choose the use case, with our help to make sure it reflects your real business questions, and we confirm workspace access and data source credentials so work can begin straight away.
step 01
Assessment & Planning
Project kickoff and requirements gathering, including a data source inventory and the business logic behind your use case.
step 02
Lakehouse Implementation
Data source integration, multi-hop pipelines through the Bronze, Silver, and Gold layers, metric views and metadata enrichment, a SQL Warehouse, an AI/BI dashboard, and a Genie agent. Built against your actual data throughout.
step 03
Validation & Handover
Everything is reviewed and signed off with your team, followed by a walkthrough of what was built and the findings report.
Ready to talk?
Talk to an expert
Good to Know

Common questions before getting in touch.

Training buyers have specific questions before they'll pick up the phone. Here are the most common ones — answered directly.

Can you deliver on-site or does it have to be virtual?

Both. Every programme can be delivered in-person at your location or virtually — globally. The format is agreed during the design phase and has no impact on content depth or lab access. Several tracks are delivered both ways on different engagements simultaneously.

Do participants receive a Databricks certification after the training?

Participants receive a Datapao certificate on completion of any programme. This is a Datapao-issued credential, not an official Databricks certification. However, all programmes aligned to the Databricks Academy official curriculum are designed to prepare participants for official Databricks certification exams, which participants can then sit independently. Many do.

Can you tailor the content to our specific tools, stack, or industry?

Yes — every engagement is private, so all content can be calibrated to your environment. For standard ILT tracks, this means adjusting examples, labs, and exercises to reflect your Databricks workspace and data architecture. For Custom Courseware engagements, it means building the entire programme from your specific requirements from the ground up.

Is training available for non-technical teams, not just engineers?

Yes. The Data & AI for Business Leaders track is designed specifically for executives and senior decision-makers with no data or engineering background. Complex Enablement Programs also routinely span both technical and non-technical audiences within a single programme, combining different content formats and depth levels across personas.

The PoC is Phase 1. The Lakehouse Buildout is everything that follows.

The PoC is designed as the first phase of the Lakehouse Buildout, not a standalone engagement disconnected from what comes next. The architecture and pipeline patterns from the PoC carry directly into the full implementation.
Platform Buildout
Lakehouse Buildout
A full production Lakehouse implementation: cloud infrastructure, data pipelines, Unity Catalog governance, and CI/CD, with knowledge transfer throughout. Everything your team needs to operate, extend, and troubleshoot the platform independently.
Learn More

The fastest way to know if Databricks is right for you is to run it on your data.

Tell us your use case and your data environment. We'll confirm the right scope and show you what two weeks produces.
Services
Experience
Resources
Careers
Company
Legal information
GDPR