Can you take over a Databricks estate someone else built?

Yes. We start with a platform assessment covering architecture, governance, and spend, then deliver a prioritised remediation roadmap before touching production.

Do we need to abandon our existing warehouse?

Rarely. Most clients run a lakehouse for engineering, ML, and streaming alongside a warehouse for BI, federated through one catalog. We design for coexistence rather than a forced rip-out.

How quickly can an engagement start?

Discovery usually begins within one to two weeks of scope agreement, and a delivery team is typically mobilised within three to four weeks depending on the skills involved.

Can you work alongside our in-house team?

Yes — most of our engagements are blended. We work in your repositories, ceremonies, and tooling, and we plan for knowledge transfer from the start so capability stays with you.

How are engagements priced?

Fixed price for well-bounded projects, monthly capacity for dedicated pods, and outcome-linked SLAs for managed services. We are explicit about assumptions and what would change the number.

NeonAITech services — AI and data engineering, product engineering, cloud and DevOps, managed operations, security and quality

Lakehouse Engineering on Databricks

One governed platform for analytics, streaming, and machine learning.

Book Free Consultation All Services

30–45%

Reduction in platform compute cost

Minutes

Freshness on streaming domains

End-to-end

Lineage across every gold table

Overview

What Lakehouse Engineering on Databricks means at NeonAITech

We design, build, and tune Databricks lakehouse platforms end to end — medallion architecture, Delta Lake pipelines, Unity Catalog governance, and MLflow-managed models — so analytics and AI workloads share one trusted copy of the data instead of competing silos.

Tools & Platforms

Databricks
Delta Lake
Unity Catalog
PySpark
MLflow
dbt

We are not tied to a single vendor — the stack follows the problem, your existing estate, and your team’s skills.

What We Deliver

Lakehouse Architecture & Build

Bronze-silver-gold medallion design, Delta Lake tables, and workspace topology sized for your workloads and your budget.

Pipeline & Workflow Engineering

Batch and streaming pipelines with Delta Live Tables, expectations-based quality checks, and orchestrated job dependencies.

Governance & Cost Tuning

Unity Catalog lineage and access control, plus cluster policies, photon tuning, and job right-sizing that cut compute spend.

Capabilities

Inside the Engagement

Delta Lake and Delta Live Tables

Unity Catalog governance and lineage

Structured Streaming and CDC ingestion

MLflow model tracking and serving

Migration from Hadoop and legacy warehouses

How We Work

A delivery rhythm you can see into

Every Lakehouse Engineering on Databricks engagement runs the same four phases, with AI used wherever it removes effort rather than adds novelty.

01
Discover
We map the current state, agree the outcome, and size the work — so scope is a shared decision, not a surprise.
02
Design
Architecture, delivery plan, and success measures are set before build, with costed options where trade-offs exist.
03
Build
Short increments with working output you can review, steer, and stop — never a black box until go-live.
04
Operate
We measure against the agreed outcomes, hand over documentation, and stay on for support where you want it.

Engagement Models

Buy it the way that fits

Fixed-Scope Project

A defined outcome, timeline, and price. Best when requirements are clear and the deliverable is well bounded.

Dedicated Pod

A cross-functional team working to your backlog and priorities, scaling up or down with a month’s notice.

Managed Service

Ongoing ownership against agreed SLAs, with a share of capacity reserved for continuous improvement.