Databricks done right: lakehouse, pipelines and AI
For data teams standardising on Databricks who want a governed lakehouse and reliable pipelines (and, increasingly, trustworthy AI on top).
Databricks is powerful, which is exactly why it can sprawl: ungoverned tables, notebooks nobody can rerun, and 'talk to your data' AI that answers confidently but wrongly because the metrics beneath it were never defined.
We build Databricks the way it should be (a governed lakehouse with tested Delta pipelines and Unity Catalog) and, where you want AI, the semantic layer that makes Genie's answers reliable.
Understand first, then build
- 1
Understand
We map your sources, your consumers, and the questions the platform must answer, including the ones you want AI to handle.
- 2
Architect
We design a medallion lakehouse (bronze/silver/gold) on Delta Lake with Unity Catalog governance, so data is reliable and access is controlled.
- 3
Build pipelines
We build tested, idempotent pipelines in PySpark and SQL that you can rerun with confidence: no fragile notebooks.
- 4
Enable AI
Where you want conversational analytics, we build the semantic layer (governed metrics, definitions and joins) that makes Databricks Genie answer reliably.
Concrete deliverables
- Lakehouse architecture on Delta Lake (medallion model)
- PySpark / SQL pipelines with testing and orchestration
- Unity Catalog governance, lineage and access control
- Semantic layer for Databricks Genie / conversational analytics
- Migration from legacy warehouses or notebooks
Tools we typically use
- Databricks
- PySpark
- Delta Lake
- Unity Catalog
- Databricks Genie
- SQL
Not sure which tool fits? That’s our job. We recommend based on your stack and team, not our preferences.
Talk it through with usRelated work
Large government department
We piloted Databricks Genie Space with a large government department and built the semantic layer needed for reliable conversational analytics.
Fewer ad-hoc analysis requests; analysts freed for higher-value work
Read the case study
Department of Transport and Main Roads (Queensland)
We developed an automated monitoring tool on Snowflake and Tableau to identify South East Queensland road corridors needing intervention and prioritise investment.
Defensible, automated corridor prioritisation
Read the case study
Komatsu Australia
We transitioned Komatsu Australia from a cumbersome manual process to automated financial reporting in Power BI, integrating data from SAP and Palantir Foundry.
50+ staff hours saved every month
Read the case studyThe questions buyers ask us
- What does a Databricks consultant do?
- We design and build the platform on Databricks: the lakehouse architecture, the pipelines that populate it, and the governance (Unity Catalog) that keeps it trustworthy. Increasingly we also build the semantic layer that lets AI features like Genie answer questions about your data reliably.
- Can Databricks Genie really let us 'talk to our data'?
- It can, but only if the data underneath is governed. On its own, an AI that queries raw tables will guess at definitions and produce confident, wrong answers. We piloted Genie Space with a large government department and built the semantic layer (definitions, joins and governed metrics) that made the answers reliable enough to reduce ad-hoc analysis requests.
- Should we use Databricks or Snowflake?
- Both are first-class. Databricks leans toward data science, machine learning and large-scale engineering on a lakehouse; Snowflake leans toward simplicity and SQL analytics. The honest answer depends on your team's skills and workloads. We work in both and will recommend based on your situation, not a preference.
- Do you offer Databricks consulting in Brisbane?
- Yes. Motta Consulting is a Brisbane-based Databricks consultant serving clients across Australia. We design governed medallion lakehouses on Delta Lake, build tested PySpark and SQL pipelines, set up Unity Catalog governance, and build the semantic layer that makes Databricks Genie answer reliably. Book a free consultation to talk through your platform.
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