Key takeaways
✓Databricks is a unified platform for data engineering, analytics, machine learning, and AI, built so teams can do all of that work in one place rather than stitching together separate tools.
✓At its core sits the Databricks Lakehouse, an architecture that combines the low-cost storage of a data lake with the query performance and governance of a data warehouse.
✓Most enterprise teams use Databricks to build data pipelines, run SQL analytics, train and deploy machine learning models, and increasingly to build AI applications.
✓Databricks runs on the major cloud providers (AWS, Azure, Google Cloud) and integrates with tools many Australian teams already use, including Power BI and dbt.
✓It is built for technical roles (data engineers, scientists, analysts) but newer features like Databricks Genie are bringing self-service data access to non-technical business users.
What is Databricks, in plain English?
Databricks is a cloud platform that brings data storage, processing, analytics, and AI development into a single environment. Instead of stitching together four or five separate tools to move data from raw storage through to a business report or a machine learning model, teams do it all in one place.
The concept at the centre of Databricks is the lakehouse. To understand why that matters, consider what most organisations had before it: a data lake (cheap storage for large volumes of raw data) sitting alongside a data warehouse (a structured, query-optimised database for reporting). Keeping the two in sync was expensive, slow, and a constant source of data quality problems. The lakehouse collapses that architecture into one layer. You store your data once, and that same data supports everything from SQL queries run by an analyst to model training run by a data scientist.
Databricks runs on the major cloud providers: AWS, Microsoft Azure, and Google Cloud. Your data stays in your cloud environment; Databricks provides the compute and the tooling on top of it.
It is worth being precise about what Databricks is not. It is not a business intelligence tool in the way Power BI or Tableau is, though it connects to both. It is not a database you manage row by row. And it is not an AI chatbot or a copilot product, though it provides the infrastructure that organisations use to build and run those things.
The lakehouse architecture deserves more space than a single paragraph, and we cover it in detail separately. For now, the short version: one copy of your data, one platform, far less engineering overhead to keep everything consistent.
What is Databricks used for?
Databricks handles four broad categories of work, and most enterprise teams end up doing at least two of them on the same platform.
Data engineering. This is the most common starting point. Data engineers use Databricks to build pipelines that pull raw data from source systems (a CRM, an ERP, a payment gateway), clean and transform it, and load it somewhere analysts can actually query. Databricks calls its modern pipeline framework Lakeflow, and it supports both batch processing (run nightly, process yesterday's data) and streaming (process transactions the moment they arrive). For teams drowning in brittle, hard-to-maintain ETL scripts, this is usually where the value becomes obvious quickly.
SQL analytics. Analysts and BI teams query data using Databricks SQL, a dedicated environment that runs standard SQL against the lakehouse. It connects to tools like Power BI and Tableau, so a team does not have to abandon the dashboards they already use. If your analysts are comfortable writing SQL, they can work in Databricks without learning an entirely new skill set.
Machine learning. Data scientists use Databricks to build, train, and deploy machine learning models. The platform includes MLflow, an open-source tool for tracking experiments and managing model versions, which solves a persistent problem: knowing which model you used to generate a particular prediction six months ago. Teams working on churn prediction, demand forecasting, or fraud detection tend to find this capability valuable early.
AI applications. This is where investment is accelerating. Databricks now supports building and deploying AI applications directly on your own data, including retrieval-augmented generation (RAG) systems, where a language model answers questions by searching your internal documents rather than relying on its training data alone. Organisations in regulated industries find this particularly appealing because the data never leaves their own environment.
One platform, not four separate tools
Most teams do not adopt Databricks for a single use case. The commercial case for the platform is that the same data, security controls, and governance layer underpin all four workloads. Running them separately across different tools means duplicating data, managing multiple access policies, and paying for integration work that Databricks makes unnecessary.
The mix varies by team. A retailer might start with engineering pipelines and SQL reporting, then add forecasting models a year later. A financial services firm might begin with ML and later extend into AI applications once governance controls are in place. The lakehouse architecture is what makes this progression possible without rebuilding from scratch each time.
What is the Databricks Data Intelligence Platform?
Databricks rebranded its product in 2023 under the name Data Intelligence Platform. The label reflects a genuine shift in product direction: the platform is no longer just a place to process and store data, it is increasingly designed to understand your data and help your team work with it using AI.
The platform sits on top of cloud infrastructure (AWS, Azure or Google Cloud) and brings several capabilities under one roof.
The lakehouse. The architectural foundation is the lakehouse), a design that combines the flexibility of a data lake (storing any type of data cheaply) with the reliability and query performance you would normally only get from a data warehouse. You store data once, and different teams can use it for different purposes without copying it into separate systems.
Unity Catalog. Governance is handled through Unity Catalog, Databricks' centralised metadata and access-control layer. It gives you a single place to define who can see which data, track how data moves through your organisation, and produce audit trails for compliance. For teams operating under Australian Privacy Act obligations or financial sector regulations, this is often the feature that makes Databricks viable at enterprise scale.
AI and machine learning. The platform includes a managed environment for building, training and deploying machine learning models. More recently, Databricks has pushed further into generative AI, with tools for building AI agents and querying data in plain English rather than SQL.
The direction of travel matters
Databricks started as a data engineering tool. It is now positioning itself as AI infrastructure. If your organisation is planning to build AI applications on top of your data, understanding this roadmap is more important than understanding any single feature.
Lakeflow and pipelines. Data rarely arrives clean and ready to use. Databricks includes pipeline tooling (now branded Lakeflow) for moving raw data from source systems, transforming it, and making it available for analysts and models downstream.
What ties these pieces together is a shared metadata layer. Because Unity Catalog understands what every asset in the platform is and what it contains, features like search, lineage tracking and AI-assisted querying can work across all of them. That is the "intelligence" in the platform name: not a single AI feature, but a layer of shared understanding built into the architecture.
The practical implication for business leaders is that decisions made early in a Databricks deployment, particularly around how Unity Catalog is structured, shape what the platform can do for your organisation for years afterwards. Getting the foundational configuration right matters, and it requires people who understand both the technology and how your data is actually organised.
Who actually uses Databricks?
Databricks is used across the full data team, not by a single specialist role. That breadth is part of what makes it worthwhile to fund, and part of what makes the training question more complicated than it first appears.
Data engineers are usually the heaviest users. They build and maintain the pipelines that move raw data into the lakehouse, clean it, and make it available for analysis. On Databricks, that work happens in notebooks and increasingly through Lakeflow, Databricks' declarative pipeline tool, rather than hand-written scripts stitched together across separate systems.
Data scientists and ML engineers use Databricks to train and deploy machine learning models, run experiments, and increasingly to build AI applications. The platform gives them a managed environment with access to large datasets without having to negotiate with infrastructure teams for every compute job.
Data analysts sit closer to the business and tend to work in Databricks SQL, writing queries and building dashboards against data that engineers have already prepared. If your team currently uses tools like Power BI or Tableau, those tools continue to sit on top of Databricks rather than being replaced by it. The relationship between Databricks and Power BI is a common point of confusion, and it is worth clarifying early in any rollout.
Business users are an emerging category. Features like Databricks Genie let non-technical staff ask questions of their data in plain language, which shifts the platform from a back-office engineering tool toward something with a wider organisational footprint.
Executives and data leaders are rarely direct users, but they are the ones deciding whether the investment makes sense. For them, Databricks is a consolidation play: one governed platform instead of a sprawl of disconnected tools, with clearer lineage (meaning the ability to trace where data came from and how it was transformed) and cost visibility through a single pricing model.
The training gap is often widest in the middle
Data engineers usually get enablement first. Analysts and business users are frequently left to self-serve, which slows adoption and limits the return on the platform investment.
The mix of roles matters because each group needs a different type of capability. Engineers need depth in pipeline construction and platform administration. Analysts need confidence in SQL and the data model. Business users need enough context to trust the outputs they are seeing. A rollout that trains only one group tends to create bottlenecks at the others.
How does Databricks fit with tools your team already uses?
Databricks is designed to sit underneath the tools your analysts and engineers already rely on, not replace them.
Take Power BI. Your analysts do not need to abandon it. Databricks acts as the data foundation, storing and processing the data at scale, while Power BI connects to it for reporting and visualisation. The two work together rather than compete. If you want more detail on where the line sits, Databricks and Power BI covers that relationship specifically.
The same logic applies to your cloud environment. Databricks runs on AWS, Azure, and Google Cloud. Australian enterprises using Azure will find it integrates tightly with the broader Microsoft stack, including Azure Data Factory for ingestion and Azure Active Directory for identity management. There is no need to migrate away from your existing cloud provider to adopt it.
For SQL-fluent analysts, the transition is gentler than many expect. Databricks SQL lets analysts query data in the lakehouse using standard SQL, through a familiar notebook or BI tool interface. They do not need to learn an entirely new language to get value from the platform.
Complement, not replacement
Databricks is most effective when it strengthens the data layer that existing tools sit on top of. Teams that approach it as a rip-and-replace project take longer to see value than those who layer it in alongside current workflows.
Where things get more complex is governance: when data lives across multiple tools and clouds, controlling who can access what becomes genuinely difficult. That is where Unity Catalog, Databricks' built-in governance layer, does most of its work. It provides a single place to manage permissions, data lineage, and access policies across the whole platform, regardless of which tool is reading the data.
Frequently asked questions
What does Databricks cost?
Databricks pricing is consumption-based, built around a unit called a DBU (Databricks Unit), which measures compute usage per hour. You pay for the cloud infrastructure underneath (AWS, Azure, or Google Cloud) separately from the Databricks platform fee on top. Actual spend depends heavily on workload type, cluster configuration, and how well your team manages compute resources. A team running poorly configured jobs can spend significantly more than a team doing equivalent work efficiently. There is no simple per-seat figure to quote here, but Better People covers the cost levers in detail in Databricks pricing explained: DBUs, and the skills that keep spend down.
Does it matter which cloud we run Databricks on?
For most teams, the choice comes down to which cloud provider you are already standardised on. Databricks runs on AWS, Azure, and Google Cloud, and the core platform capabilities are consistent across all three. Azure Databricks is tightly integrated with Microsoft's ecosystem, which matters if your organisation uses Azure Data Factory, Azure DevOps, or Microsoft Fabric. AWS deployments benefit from tighter integration with services like S3 and Glue. The functionality gap between clouds is small for most use cases, so organisational context usually decides it.
How is Databricks different from Snowflake?
Databricks and Snowflake overlap significantly on data warehousing and SQL analytics, but they start from different origins. Snowflake was built as a cloud data warehouse first. Databricks was built as a data engineering and machine learning platform first, then added strong SQL and warehousing capabilities. If your team's primary need is SQL-based BI and reporting, Snowflake is a credible fit. If you are running machine learning, training AI models, or processing large volumes of unstructured data, Databricks is more naturally suited. Many large organisations run both. A fuller comparison is available in Databricks vs Snowflake: an enterprise comparison.
Do our teams need training to use Databricks effectively?
Yes, and underinvestment in training is one of the most common reasons Databricks implementations underdeliver. The platform is powerful, but it rewards teams who understand how to model data well, manage compute costs, and apply the right tools to the right problems. Engineers who come from pure SQL backgrounds often need to build fluency in Python, Delta Lake, and pipeline design. Data scientists need to understand how to productionise work rather than just prototype it. Business analysts moving onto Databricks SQL need to learn how the lakehouse model differs from a traditional warehouse. Without that foundation, teams tend to default to familiar patterns that do not take advantage of what they have invested in.
Is Databricks only for large enterprises?
Databricks scales down as well as up, and mid-market organisations do run it successfully, particularly when they have data volumes or machine learning ambitions that outgrow simpler tools. That said, the platform carries meaningful complexity. Smaller teams without dedicated data engineers can find the operational overhead significant. Databricks Free Edition offers a way to explore the platform before committing, and it is a practical option for upskilling individuals before a broader rollout.
Where to go next
If this article has answered the basic question, the natural next step is understanding what it means for your team specifically: what skills they need, where the gaps typically sit, and how organisations like yours have built capability on the platform.
The Databricks training for enterprise teams pillar hub is a good place to continue. It maps the full learning landscape, from foundational concepts through to certification and advanced engineering.
When you're ready to talk about your team's situation directly, the Databricks training Australia page covers how Better People works with enterprise teams, and what a structured program typically looks like.
Not sure where your team's Databricks skill gaps actually are?
We'll ask about your current stack, your team's roles, and what you're trying to build or improve. You'll leave with a clear picture of where to focus first.
