You have probably heard the name in a vendor presentation, a technology roadmap discussion, or a conversation between your data engineering team. Databricks keeps coming up, and it is not entirely clear what it actually does or whether it belongs in your organisation’s plans.
This guide answers that question plainly, without assuming you have a computer science degree or an existing opinion on Apache Spark.
Databricks is a cloud-based data analytics and artificial intelligence platform. It gives data engineering, data science, and analytics teams a unified environment to store, process, analyse, and build machine learning models on large volumes of data.
If your organisation has a lot of data coming from many different sources, and you need to process and make sense of it at speed, Databricks is the infrastructure that makes that possible.
It was founded in 2013 by the original creators of Apache Spark, an open-source framework for distributed data processing, and is now one of the most valuable privately held technology companies in the world, used by thousands of enterprises including Shell, Comcast, Walgreens, and Regeneron.
Most enterprises reach a point where their data outgrows their tools.
Traditional data warehouses are fast for structured, well-organised data, but they struggle with unstructured data, real-time processing, and the scale required for modern machine learning workloads. Traditional data lakes are flexible and cheap for storage, but without structure and governance, they become what engineers call “data swamps”: vast repositories of data that nobody can reliably use.
Databricks solves this by introducing what it calls the “lakehouse” architecture, a model that combines the low-cost, flexible storage of a data lake with the performance, reliability, and governance of a data warehouse. The result is a single platform where data engineers, data scientists, and analysts can all work on the same data, using the same governance controls, without maintaining two separate systems.
Databricks covers several distinct capabilities, each serving a different user within a data organisation.
Data engineers use Databricks to build and manage data pipelines, the processes that move data from source systems, clean and transform it, and load it into a state where it can be analysed. Databricks uses Apache Spark under the hood, which means it can process data in parallel across hundreds of machines simultaneously, handling volumes and speeds that a single server could never manage.
Delta Live Tables, Databricks’ pipeline development framework, allows engineers to build reliable, self-managing data pipelines with built-in data quality checks. When something in the upstream data breaks, the pipeline surfaces the problem immediately rather than silently corrupting downstream reports.
Data scientists use Databricks to develop, train, and deploy machine learning models. The platform supports all major open-source ML frameworks including TensorFlow, PyTorch, and scikit-learn, and it includes MLflow, the most widely adopted open-source platform for managing the machine learning lifecycle.
MLflow tracks experiments, stores model versions, and manages the deployment of models into production. For enterprises building bespoke predictive models as part of their analytics strategy, this capability is foundational.
Databricks SQL gives analysts and business users the ability to query Databricks data using standard SQL, the language that most data professionals already know. Databricks AI/BI, launched in 2024, extends this with natural language querying and automated dashboard generation, allowing business users to explore data without writing any code.
This does not make Databricks a replacement for a dedicated visualisation tool. Most enterprises connect Databricks as the data layer and use a tool like Tableau or Power BI for front-end reporting. To understand how those tools compare, read our Tableau vs Power BI enterprise comparison.
Databricks handles streaming data natively through Delta Live Tables and its integration with Apache Kafka. Enterprises in retail, financial services, logistics, and manufacturing use this capability to process events as they happen, rather than waiting for a nightly batch process to complete.
One of the most significant enterprise features in Databricks is Unity Catalog, the platform’s unified data governance layer.
Unity Catalog provides a central place to define who can access what data, across all Databricks workloads and all three major cloud providers. Tables, models, notebooks, dashboards: all governed in one place, with full audit logging for compliance reporting.
For enterprises with regulatory obligations around data privacy and access, this is a material capability. The ability to enforce data governance policies consistently, across every team and every workload, without managing separate permission systems for each tool, reduces both compliance risk and administrative overhead.
Databricks runs on all three major cloud providers: Amazon Web Services, Microsoft Azure, and Google Cloud Platform. This multi-cloud flexibility is a significant advantage for enterprises that operate across cloud environments or want to avoid dependency on a single cloud vendor.
Each cloud deployment of Databricks is managed independently, but Unity Catalog can span multiple cloud environments, providing consistent governance across a distributed infrastructure.
The most common question enterprise decision makers ask when evaluating Databricks is how it compares to an existing data warehouse investment, typically Snowflake, Azure Synapse, or Google BigQuery.
| Dimension | Databricks | Traditional Data Warehouse |
|---|---|---|
| Data Types Supported | Structured and unstructured | Primarily structured |
| Machine Learning | Native, with MLflow | Limited or external |
| Real-Time Processing | Yes, via Spark Streaming | Limited |
| Open-Source Foundation | Yes (Delta Lake, Spark) | Mostly proprietary |
| Governance | Unity Catalog (unified) | Varies by platform |
| SQL Familiarity | Yes (Databricks SQL) | Yes |
| Best For | Engineering-heavy, ML-intensive workloads | Analyst-facing, SQL-first BI |
Many enterprises run Databricks and a data warehouse together. Databricks handles the raw data processing and ML layer; the data warehouse serves as the clean, query-optimised layer for BI reporting. If your reporting tool is Microsoft Fabric’s Power BI, our article on Microsoft Fabric vs Power BI explains how that architecture fits together.
Databricks is primarily an engineering-first platform. It delivers the most value to organisations that have or plan to build the following internal capabilities.
Data engineering teams who need to build reliable, scalable data pipelines at volume. If your organisation is still moving data manually or through brittle, script-based processes, Databricks is a transformative upgrade.
Data science teams who build machine learning models as a core business function. Fraud detection, demand forecasting, personalisation engines, predictive maintenance: these are the use cases where Databricks’ ML infrastructure earns its cost.
Organisations processing large data volumes. Databricks runs on distributed compute, which means it scales efficiently with data volume. Organisations processing gigabytes per day may not need it. Organisations processing terabytes or petabytes almost certainly do.
Databricks is not the right starting point for every enterprise.
If your organisation does not have data engineering expertise in-house, you will struggle to extract value from the platform independently. Databricks requires technical resource to set up, manage, and develop on. It is not a self-service analytics tool in the way that Tableau or Alteryx are.
If your primary need is reporting and dashboarding for business users, a platform like Power BI, Tableau, or Qlik will deliver more value at lower cost and complexity. Databricks shines as the data layer that feeds those tools, not as a replacement for them.
For a broader view of how Databricks sits alongside other leading enterprise analytics platforms, including tools better suited to self-service and visualisation use cases, read our guide to the Top 5 Data Analytics Tools for Enterprises in 2026.
Databricks is one of the most powerful data and AI platforms available to enterprises today. It handles the hardest problems in enterprise data: processing at scale, machine learning in production, and governance across complex multi-cloud environments.
It is not a simple tool, and it is not right for every organisation. But for enterprises that need to build serious data infrastructure and develop bespoke AI and machine learning capability, Databricks is the strongest foundation available.
No. Databricks is a data analytics and AI platform. It uses Delta Lake as its storage format and runs on cloud object storage, but it is not a database in the traditional sense.
Not necessarily. Many enterprises use both. Databricks excels at data engineering and machine learning; Snowflake excels at SQL analytics and structured data warehousing. They often complement each other rather than compete directly.
Databricks is primarily designed for enterprises with significant data volumes and technical teams. Small businesses with simpler analytics needs are better served by tools like Power BI, Tableau, or Google Looker Studio.
Databricks supports Python, SQL, R, and Scala. Python and SQL are by far the most commonly used within enterprise data teams.
