Databricks vs Microsoft Fabric

Key takeaways

• ✓Databricks is the stronger choice for teams running complex data engineering, large-scale machine learning, or multi-cloud workloads. Microsoft Fabric suits organisations already deep in the Microsoft ecosystem who need integrated analytics without building a dedicated data platform team.

• ✓The two platforms overlap more than vendors admit. Both offer a lakehouse architecture, Delta-format storage, and notebook-based development. The real differences are in depth, flexibility, and where each product is heading.

• ✓Fabric's tight integration with Power BI, Azure, and Microsoft 365 is a genuine advantage for business intelligence-heavy teams. It is also a constraint: you are largely committed to Microsoft's roadmap and pricing model.

• ✓Databricks gives data and ML (machine learning) teams more control, more portability, and a more mature governance layer through Unity Catalog. That depth comes with steeper onboarding.

• ✓For most Australian enterprise teams, the decision comes down to one question: is your primary need faster BI on existing Microsoft infrastructure, or are you building a production-grade data and AI platform that needs to evolve independently?

What are these two platforms, really?

Databricks is an open-source-rooted data and AI platform built around the idea that a single, unified system should handle data engineering, data science, machine learning, and analytics without forcing teams to stitch together separate tools. It was created by the team that originally built Apache Spark at UC Berkeley and has since grown into one of the dominant platforms for organisations working with large-scale data. The architecture centres on what Databricks calls the lakehouse: a design that combines the raw storage flexibility of a data lake with the structured querying capabilities of a data warehouse, all sitting on open file formats that your organisation owns. Azure Databricks is the version that runs natively on Microsoft Azure, though Databricks also runs on AWS and Google Cloud.

Microsoft Fabric is a newer entrant. Microsoft announced it in 2023 as an end-to-end analytics platform built directly into the Microsoft 365 and Azure ecosystem. Where Databricks arrived as an independent company that built cloud partnerships over time, Fabric was designed from the start as a Microsoft product, deeply integrated with Power BI, Azure Data Factory, and OneLake, Microsoft's unified data storage layer. Think of it as Microsoft's attempt to consolidate what had previously been a fragmented set of Azure analytics services, including Azure Synapse Analytics, Power BI Premium, and Azure Data Factory, into a single licensed product.

The two platforms overlap considerably on paper. Both offer data engineering pipelines, SQL analytics, machine learning capabilities, and governance tooling. The meaningful differences show up in where each platform comes from, how it is priced, what it does exceptionally well, and how deeply it assumes you are already committed to a particular cloud ecosystem.

How do Databricks and Fabric differ in architecture?

The core difference comes down to where each platform starts. Databricks was built around a single, open storage layer: Delta Lake, an open-source table format that stores data as Parquet files with a transaction log on top. You own that storage. It sits in your Azure, AWS, or GCP account, and any tool that can read Parquet can access it without going through Databricks.

Microsoft Fabric takes a different approach. Its storage layer is OneLake, a single managed data lake provisioned within your Microsoft 365 tenant. All Fabric workloads (Power BI, Data Factory, Synapse Analytics, Real-Time Intelligence) read from and write to OneLake automatically. The table format Fabric uses internally is also Delta, but the operational model is meaningfully different: OneLake is a Microsoft-managed service, not raw storage you control directly.

Open ecosystem vs integrated suite

This distinction matters more than it sounds. With Databricks, you can connect third-party tools, run your own orchestration, and move data without vendor involvement. The openness is genuine. With Fabric, the integration story is tight and intentional. Every Fabric workload shares the same OneLake, which means a dataset built in Data Factory is immediately visible in Power BI with no pipeline in between. That is genuinely useful if your team lives in Microsoft tools. It also means you are more dependent on Microsoft's roadmap for what those connections can do.

Neither model is inherently better. An organisation with a strong Microsoft 365 footprint will find Fabric's integration saves real engineering time. An organisation running multi-cloud workloads, or one that wants to keep its options open across tools and vendors, will find Databricks' open architecture easier to extend.

Compute and runtime

Databricks runs on its own compute clusters, which your team provisions and sizes. The runtime is Databricks Runtime, a hardened and optimised distribution of Apache Spark with proprietary additions (Photon, the vectorised query engine, being the most significant). You pay for the compute you use and manage cluster lifecycles yourself, though serverless options have reduced that burden considerably in recent releases.

Fabric abstracts most of that. Its compute is Microsoft-managed and scales automatically behind each workload type. Data engineers writing Spark in Fabric Notebooks use a Spark runtime Microsoft maintains; analysts running SQL use a serverless SQL endpoint backed by OneLake. The trade-off is less configuration control in exchange for less operational overhead.

The architecture question is really a control question

Databricks gives you more control over compute, storage, and tooling choices. Fabric gives you less to manage. Which matters more depends on the complexity of your workloads and the size of your platform engineering team.

One practical implication: if your data engineering team is small and your primary consumers are Power BI analysts, Fabric's architecture removes several integration steps that Databricks would require you to build. If your team is running large-scale ML pipelines, multi-source data ingestion, or workloads that need fine-grained compute control, Databricks' architecture gives you more to work with.

Which workloads suit each platform best?

The honest answer is that these platforms overlap significantly in the middle, but each has a clear home ground. Knowing where those edges sit will save your team a lot of retrofitting.

A few things worth calling out beyond the table.

Databricks earns its place when the workload is technically demanding: complex transformations across hundreds of millions of rows, multi-stage ML pipelines, or streaming data arriving at high volume from IoT devices or financial systems. Its Delta Lake format and Unity Catalog give engineering teams fine-grained control over data quality and lineage. That control comes with a corresponding expectation: your team needs engineers who know how to use it.

Fabric's sweet spot is the organisation that already runs heavily on Microsoft 365 and Power BI, where the priority is getting business analysts to self-serve rather than building sophisticated pipelines. The OneLake storage layer and the tight Power BI integration reduce the handoff friction between data teams and the people consuming their output. That is a real productivity gain for many Australian enterprises, particularly those without a deep bench of data engineers.

Where the overlap creates confusion

Both platforms can run SQL, store data in open formats, and connect to Power BI. The confusion usually comes from buying decisions driven by licensing bundles rather than workload fit. An organisation that purchases Fabric through an E5 Microsoft agreement may discover it handles 80% of their needs well and 20% poorly, particularly if that 20% involves serious ML work or high-volume streaming.

Where each platform struggles is equally important. Databricks can feel overbuilt for a team that mainly needs governed SQL access and dashboards. The cluster management, workspace configuration, and cost controls require ongoing attention that a lean team may not have capacity for. Fabric, on the other hand, is a young platform. Features that are mature in Databricks, such as fine-grained table permissions, advanced streaming semantics, and MLflow experiment tracking, are still catching up in Fabric as of 2025.

Neither platform is the wrong choice in absolute terms. The question is which one fits the actual work your team does today, with a realistic eye on what you will need in two years.

How do the two platforms handle AI and machine learning?

Databricks was built by the team that created Apache Spark and MLflow, and that heritage shows. The platform treats machine learning as a first-class workload rather than a bolt-on. MLflow, now an open-source industry standard for tracking experiments, packaging models, and managing deployments, is native to Databricks. Your data scientists get experiment tracking, reproducible runs, and a model registry without configuring a separate service. Model Serving lets you deploy a registered model to a REST endpoint in minutes, and Unity Catalog extends governance across both data assets and ML models in a single control plane.

The AI story goes further than classical machine learning. Databricks' Mosaic AI layer supports fine-tuning open-source large language models (LLMs) on your own data, building retrieval-augmented generation (RAG) pipelines that pull from your lakehouse, and constructing AI agents that operate across multiple steps. If you want to train or fine-tune a model rather than simply call one via an API, Databricks gives you the infrastructure to do it. The AgentBricks framework extends this further, providing tooling for building and governing AI agents with the kind of auditability regulated industries require.

The key distinction in AI capability

Databricks is designed for teams who build and own their models. Microsoft Fabric is designed for teams who consume AI services built elsewhere. Both are legitimate choices; they serve genuinely different ambitions.

Microsoft Fabric takes a different approach. Its AI capabilities are largely integration points rather than native infrastructure. Fabric connects to Azure Machine Learning for model training and lifecycle management, and exposes Copilot features across its workloads, so an analyst in the Warehouse or Lakehouse experience can ask natural-language questions of their data without writing SQL. For many teams, that is exactly what they need.

The gap opens when you need something more custom. Azure ML is a capable platform, but it is a separate service you integrate with Fabric, not something embedded in it. Moving data between Fabric and Azure ML adds latency, permission surfaces, and operational complexity. Teams doing serious model development tend to find they are managing two environments rather than one.

For Australian enterprises evaluating generative AI use cases specifically, the practical question is whether you are deploying models or consuming them. A team building a document classification model on proprietary data, fine-tuning an LLM on internal knowledge, or governing AI outputs across a regulated pipeline will find Databricks' native tooling more coherent. A team that wants Copilot-style natural language access layered over their existing Microsoft data estate, without a dedicated ML engineering function, will find Fabric's approach lower-friction. Neither answer is wrong. It depends entirely on how much of the AI stack you intend to own.

What does the Microsoft ecosystem mean for Australian enterprises?

For most large Australian organisations, this decision does not happen in a vacuum. If your enterprise already runs Azure infrastructure, holds Microsoft 365 licences, and uses Power BI for reporting, Fabric arrives with a meaningful head start. It sits inside the same admin console, inherits your existing Entra ID (formerly Azure Active Directory) identity and access controls, and connects to Power BI workspaces your teams already know. That is a real advantage, not marketing copy.

The procurement angle matters too. Many Australian enterprises buy through Microsoft volume licensing or an Enterprise Agreement. Adding Fabric capacity can be straightforward within that framework, and procurement teams often prefer it because it keeps vendor relationships simple.

Databricks, by contrast, is available on Azure Marketplace and can be billed through your Azure commitment, but it is a separate vendor with its own contracts, support tiers, and release cadence. For some organisations that is a non-issue. For others, particularly in regulated industries or government, a single-vendor footprint genuinely simplifies security reviews and audit trails.

Ecosystem fit is not the same as technical fit

Choosing Fabric because it is already in your Microsoft agreement is a legitimate reason, but only if the workloads align. An engineering team building production ML pipelines or processing hundreds of terabytes daily will run into Fabric's current limits regardless of how tidy the procurement looks.

There is also a skills dimension worth naming. Australian enterprises that have invested in Power BI literacy, Azure Data Factory pipelines, or SQL-based analytics will find their existing teams can contribute in Fabric sooner. Databricks has a steeper on-ramp: it rewards engineers fluent in Python, Spark, and Delta Lake. That gap is closeable with the right training, but it takes deliberate investment.

One more consideration for Australian data teams: sovereignty. Both platforms offer Australian data centre regions (Azure Australia East and Southeast), so residency requirements can be met on either side. That is not a differentiator, but it is worth confirming at the workload level before you commit, especially if your data classification policies specify where processing (not just storage) occurs.

The honest summary is this: if your organisation is deeply embedded in the Microsoft stack and your primary use cases sit in BI, reporting, and moderate-scale data integration, Fabric's ecosystem alignment is a genuine advantage. If your team is building serious data engineering infrastructure or production AI systems, that alignment does not outweigh the capability gap, and Databricks is worth the additional vendor overhead.

Frequently asked questions

Which platform is more expensive for a mid-sized Australian enterprise?

Neither platform has a simple sticker price, because both charge based on compute consumption rather than a flat licence fee. Databricks pricing centres on Databricks Units (DBUs), a measure of processing capacity, billed per second. Microsoft Fabric uses a Fabric Capacity unit model, which can be purchased on a pay-as-you-go basis or reserved at a discount. In practice, Fabric often looks cheaper at the outset for organisations already paying for Microsoft 365 E5 licences, because some capacity is bundled. Databricks can carry higher costs if your workloads are compute-intensive, but many teams find that its performance on large-scale transformations offsets the difference. Get actual consumption estimates from both vendors before comparing numbers.

Can Databricks and Microsoft Fabric run side by side?

Yes, and many Australian enterprises already run them together. A common pattern is using Databricks for heavy data engineering, model training, and feature engineering, while Fabric handles reporting, Power BI integration, and business-facing analytics. The two platforms can share data through Delta Lake, the open table format that both support. This coexistence approach avoids a hard migration decision and lets each platform do what it does best. The article on Databricks and Power BI covers a related version of this pattern in more detail.

How difficult is it to migrate from one platform to the other?

A full migration is a significant undertaking either way. If you are moving workloads from Databricks to Fabric, the main friction points are rewriting PySpark pipelines into Fabric-native equivalents and rebuilding Unity Catalog governance structures inside Fabric's OneLake. Moving in the opposite direction, from Fabric to Databricks, typically involves converting Fabric dataflows and pipelines to Databricks notebooks and configuring Unity Catalog from scratch. For most teams, a phased coexistence strategy is less disruptive than a cut-over migration.

What skills does each platform require from a data team?

Databricks teams need strong Python and PySpark skills, comfort working in notebook-based environments, and ideally familiarity with MLflow for model tracking. SQL alone is not enough for the engineering layer. Microsoft Fabric is more approachable for teams whose background is in the Microsoft BI stack: strong SQL, Power Query, and Power BI skills transfer directly, and the graphical pipeline tools lower the entry point for analysts who are not software engineers. That said, serious Fabric engineering still rewards Python and Spark knowledge. If your team is Databricks-focused, Databricks certifications give a clear skills roadmap to work toward.

Is Databricks still relevant if we are a heavily Microsoft-aligned organisation?

It is, particularly if your data volumes are large, your ML requirements are serious, or you need fine-grained governance across complex data products. Many organisations running Azure and Microsoft 365 also run Azure Databricks precisely because Fabric's ML capabilities are still maturing. Azure Databricks is a native Azure service, so it fits inside your existing cloud agreements, security perimeter, and identity management. Being Microsoft-aligned does not make Fabric the automatic choice; it just means both options are available to you without introducing a new cloud vendor relationship.

How do your teams stay current on whichever platform you choose?

Choosing between Databricks and Microsoft Fabric is only the first decision. Getting your data engineers, analysts, and architects genuinely proficient on whichever platform you pick is the part that determines whether the investment pays off.

Both platforms move fast. Databricks, in particular, ships significant capability updates regularly, and certifications like the Databricks Data Engineer Associate have already been revised for 2026 to reflect those changes. Teams that trained two years ago may be working from an outdated mental model of what the platform can do.

Training gaps cost more than licensing gaps

A team that cannot fully operate its data platform leaves expensive capability sitting unused. The cost of underutilisation typically exceeds the cost of the training that would have prevented it.

For Australian enterprises running Databricks, structured training matters more than most people expect. The platform's depth, spanning the Lakehouse architecture, Unity Catalog for governance, MLflow for model tracking, and now AI agent frameworks, means ad hoc learning rarely gets a team to production confidence. That is especially true in regulated industries where understanding governance features like Unity Catalog is not optional.

If you are evaluating training options, it is worth reading how to choose between Databricks Academy and a custom training approach. The short version: Academy works well for individuals pursuing certification; custom programs work better when you need a whole team aligned to your specific architecture and data assets.

Better People delivers Databricks training in Australia as an authorised Databricks training partner. Programs can be structured around certification preparation, role-specific skill building for engineers or analysts, or practical workshops tied directly to your platform environment.

Not sure which training approach fits your team?

We can map a training program to your platform choice, your team's current skill level, and the outcomes you are trying to reach. No generic slide decks.

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