Data Lakehouse
Data & TrackingAlso: Lakehouse · Lakehouse Architecture
Quick definition
A data lakehouse is a data storage architecture that combines the flexibility of a data lake with the structure and query performance of a data warehouse. It lets teams store raw, unstructured data and cleaned, structured tables in one system rather than maintaining two separate platforms.
How it varies across Australia
Adoption of lakehouse architecture across Australian businesses sits well below enterprise norms in the US and Europe. Most mid-market Australian companies still run a simple data warehouse or a handful of disconnected tools, and only start evaluating a lakehouse once marketing, product and finance data volumes genuinely outgrow a single warehouse.
See data and tracking maturity across Australian industries →What it actually means
Think of a data warehouse as a filing cabinet. Everything inside is sorted, labelled and easy to pull out fast, but you have to decide the folder structure before anything goes in. A data lake is the opposite: a big shed where you can throw anything, raw and unlabelled, and sort it out later if you ever need to.
A lakehouse tries to be both. It stores raw event data, log files and unstructured content the way a lake does, but layers in the schema, governance and fast query performance that a warehouse gives you. The result is one system instead of running a lake for flexibility and a warehouse for reporting, then building pipelines to keep them in sync.
For marketing teams, this matters most when the data volume from tools like a customer data platform (CDP), the data layer on your site, and multiple ad platforms outgrows what a single warehouse can hold cheaply. A lakehouse lets raw clickstream data and cleaned attribution tables sit side by side, queried by the same engine.
The hard part isn't the technology. It's the same governance discipline every data project needs, whether it's a lake, a warehouse or a lakehouse.
A lakehouse doesn't fix a messy data strategy. It just gives the mess a bigger room to live in.
How it shows up
A lakehouse shows up in the tooling stack rather than in a single dashboard. You'll see it as the backend behind platforms like Databricks or Snowflake once a team has outgrown a plain warehouse. It also shows up in how quickly a data team can answer a new question. In a well-run lakehouse, a marketer asking for a new attribution cut doesn't require a new pipeline, just a new query against data that's already there in both raw and structured form.
The Australian context
Data residency matters more for Australian businesses running a lakehouse than for most other markets, particularly under the Privacy Act. Where the underlying storage physically sits, and which cloud region processes it, affects compliance obligations well before anyone touches the marketing layer. Australian companies handling customer data at lakehouse scale should confirm data residency before choosing a vendor, not after migration.
Where people get this wrong
Related terms
Common questions
Do I need a data lakehouse for marketing analytics?
Most Australian mid-market businesses don't yet. A lakehouse earns its complexity once you're combining large raw event volumes with structured reporting across multiple platforms. Below that scale, a well-organised data warehouse usually does the job at lower cost.
Is a data lakehouse the same as a customer data platform?
No. A lakehouse is storage and query infrastructure. A customer data platform (CDP) is built for identity resolution and marketing activation. They solve different problems and most mature data stacks use both together.
What's the main advantage of a lakehouse over separate lake and warehouse systems?
One system instead of two. Raw and structured data sit together, queried by the same engine, so teams stop building and maintaining pipelines just to move data between a lake and a warehouse.
What's the biggest risk when adopting a lakehouse?
Assuming the architecture solves data quality problems on its own. A lakehouse without governance just gives an existing mess more room to grow, and often at higher cost than the warehouse it replaced.
Debrief
Get the next one
No spam. No fluff. Just the next article, straight to your inbox.
Keep exploring
About New Rebellion
New Rebellion is a marketing intelligence consultancy. We build tools, score Australian businesses on how their marketing actually performs, and publish Debrief every day. This dictionary is part of how we work in the open.
How we think →