Lakehouse Implementation Services

Overview

A Data Lakehouse combines the low-cost, flexible storage of a data lake with the governance and structure of a data warehouse, using a layered Bronze–Silver–Gold model to progressively refine raw data into decision-grade, AI-ready information. Avocado's Data Lakehouse and infrastructure service deploys a production-ready foundation on Azure, AWS or GCP in five days — a single governed platform for analytics, data science and AI, instead of fragmented, ungoverned sources.

It's built for IT and data leaders who need a scalable foundation now, not a multi-month platform project.

Who is this for?

You'll recognise the situation

You don't have a single pane of glass — answering one business question means pulling data from three or more systems that don't talk to each other.

Your existing data lake has become a “data swamp” nobody fully trusts.

Your AI initiatives can't safely go further without a governed data foundation underneath them.

You want a fixed-scope, fixed-timeline start rather than an open-ended platform build.

What's included?

A production-ready foundation for real workloads

Expert-led customisation

Workshops to align the architecture with your current and evolving objectives, run by our data platform specialists.

Built-in governance

Custom data management and quality policies implemented from day one — secure and reliable, not bolted on later.

End-to-end validation

The whole environment tested with representative data before handover, validating performance under real ingestion loads.

Platform-agnostic

Delivered on Azure, AWS or GCP — whichever you already run or are standardising toward.

Ready for any source

Built to ingest on-premises, relational, API, unstructured, semi-structured, SharePoint and streaming/IoT data.

Focus on value

We handle the foundational build so your team moves straight to high-value use cases, not infrastructure trial-and-error.

What does the 5-day build look like?

Five days to a working Lakehouse, day six to your data

Governance & architecture

Day 1 — Lakehouse governance and architecture set.

Storage configuration

Day 2 — storage configured using our accelerators and templates.

Ingestion

Day 3 — ingestion built using our accelerators and templates.

Analytics, data science & AI

Day 4 — test the dataflow and enable advanced analytics, data science and AI.

End-to-end dataflow

Day 5 — test dataflow end to end, modelling and visualisation.

Your data, your value

Day 6 — connect your own sources and start generating insights and value.

How do we deliver it?

From workshop to handover in a week

Align

Workshops to map the architecture to your current and future objectives.

Build

Deploy the platform on your chosen cloud, with governance and quality policies built in from day one.

Validate

End-to-end testing with representative data to confirm performance before handover.

Handover

Production-ready environment within 5 days, ready to connect to your sources — a consultant can typically begin within 24 hours.

What tools and technology do we use?

The stack behind it

We build on Azure as standard: Fabric and Data Factory for ingestion, Databricks and Delta Lake for transformation and machine learning across a Bronze, Silver and Gold architecture, real-time streaming through Event Hubs and Stream Analytics, and Power BI for reporting. We deliver the equivalent on AWS and Google Cloud, and integrate with Snowflake, Tableau or Qlik where they’re already part of your environment.

What you walk away with

A scalable, well-governed platform that underpins every future data and AI initiative — not a one-off project.

Structured, semi-structured and unstructured data handled in a single environment, ready for analytics and data science.

A foundation your team can build on immediately, rather than months of platform trial-and-error.

Self-service, device-agnostic access to real-time decisions, with reduced time-to-value and management cost.

How is this engagement structured?

Delivered as a fixed-scope, fixed-timeline engagement — a consultant can typically begin within 24 hours. Deliberately an accessible first step: the real value compounds from what you build on top. Contact us for a tailored quote.

Common questions

What is a Data Lakehouse?

An architecture that combines a data lake's low-cost, flexible storage with a data warehouse's governance and structure, using a Bronze–Silver–Gold model to refine raw data into trusted, decision-grade information.

How long does it take to deploy?

It's a fixed-scope, 5-day engagement, with a consultant typically able to start within 24 hours.

Which cloud platforms do you support?

Azure, AWS or GCP — whichever you already run or are standardising toward. The approach is technology-agnostic.

Do I need an existing data platform first?

No — it's designed for organisations starting from scratch as well as those modernising an existing platform.

What happens after the 5 days?

You have a governed, tested, production-ready environment your team can build on immediately — see AI-Ready Data for the next layer most organisations add.

Data Lakehouse and Infrastructure