The workspace exists, but delivery patterns differ by team
Shared patterns for environments, ingestion, transformation, testing and deployment give teams a coherent starting point without hiding domain needs.
Databricks
A Databricks platform built for trusted use.
We help enterprises design, deliver and evolve Databricks foundations that connect data engineering, governance, analytics and AI around real business use.
The right Databricks architecture is not the one with the most features enabled. It is the one your teams can operate, govern and extend while serving the decisions and products that matter.
Lakehouse pathConnected
Cloud and enterprise sources01
Databricks engineering patterns02
Unity Catalog governance03
Analytics, data products and AI04
One governed platform path connects engineering, analytics and AI workloads.
What this solves
Make Databricks an operating capability, not just a technology programme.
We use a concrete delivery path to make platform choices testable. This keeps architecture, governance and operating ownership connected to a useful outcome.
Governance is documented but not embedded in the data path
Unity Catalog structures, permissions, lineage and ownership are designed alongside the data products that will exercise them.
Platform activity is growing without a clear business path
We connect technical increments to a decision, analytical product or AI workflow so the platform backlog has a reason and an owner.
When to use it
These are common signals that the next move should be a focused foundation or delivery engagement.
- A Databricks adoption needs a practical reference architecture and first governed delivery path.
- Legacy data workloads are being migrated or consolidated into a lakehouse platform.
- Teams need consistent engineering and deployment patterns across workspaces and domains.
- Unity Catalog governance must be translated into usable access and ownership models.
- Analytics and AI initiatives need shared, trusted data products.
- The platform needs an operating model the internal team can maintain and extend.
Capabilities
What the engagement can include
We work across architecture and implementation, selecting the Databricks capabilities that fit the current delivery need and the team's operating constraints.
Lakehouse architecture
Workspace, storage, compute, networking and domain decisions shaped into a target architecture with explicit trade-offs.
Data engineering patterns
Reusable ingestion, transformation, orchestration and data-quality patterns for batch and streaming workloads.
Unity Catalog governance
Catalog structures, access models, lineage and ownership aligned to the way teams discover and consume data.
Data products and semantics
Curated products and shared business definitions that connect platform data to analytics, applications and decisions.
AI-ready platform services
Governed context, evaluation paths and data services that support bounded assistants and agentic workflows.
Operations and evolution
Deployment, observability, runbooks and ownership practices that make platform change understandable and repeatable.
How Avantic works
Senior specialists from decision to handover
Senior Databricks specialists work through a real domain or use case, turning decisions into reusable foundations and transferring the implementation to your team.
- 01
Connect the platform to a priority
Agree the business use, current constraints and target service levels. This defines which platform decisions are urgent and which can wait.
- 02
Make architecture decisions explicit
Assess the current estate and document the choices around environments, data boundaries, governance, deployment and operations.
- 03
Deliver one governed path
Build through a bounded domain or product so engineering patterns, Unity Catalog controls and operational responsibilities are proven together.
- 04
Enable the team to scale it
Handover code, decisions, documentation and working practices, then use evidence from the first path to prioritise the next increment.
Continue exploring
Related expertise and thinking
A focused first conversation
Start with the challenge, the decision and the data behind it.
We will help you clarify the most useful next step, whether that is an assessment, a foundation increment or a bounded AI workflow.
Discuss your data challenge