What is Databricks?

What Databricks actually is

Databricks began as a company built around Apache Spark, the open-source distributed processing engine, and it now offers a managed platform for data engineering, analytics and machine learning built on top of that engine. Rather than being one single tool, it is a workspace: notebooks for writing code, managed Spark clusters that run it, and a set of services layered on top for storing and governing data, orchestrating pipelines, and building and serving machine learning and AI models.

The lakehouse, explained

A "lakehouse" is the architectural idea Databricks is most associated with: instead of choosing between a data lake (cheap, flexible storage that accepts data in almost any raw format) and a data warehouse (structured, query-optimised storage with strong performance and reliability guarantees), a lakehouse tries to give you both properties on the same underlying storage. Data sits in open file formats in cheap object storage, as it would in a lake, but a table layer on top adds the reliability, structure and fast-query performance more associated with a warehouse. Our data lake vs data warehouse explainer covers the two traditional models this architecture sits between.

Apache Spark, at a beginner level

Spark is the distributed processing engine underneath Databricks — it splits a large computation (transforming data, running a query, training a model) across many machines working in parallel, so the job finishes in a reasonable time even when the dataset is far too large for one machine to handle alone. You do not need to operate a Spark cluster by hand to use Databricks; the platform’s job is largely to manage that infrastructure for you, so teams can write code against Spark without becoming experts in cluster management. Our what is big data explainer covers why distributed processing frameworks like Spark exist in the first place.

What Databricks is commonly used for

A few workloads recur across most Databricks deployments:

• Large-scale data processing — transforming and cleaning big datasets that would overwhelm a single-machine tool, using Spark under the hood.
• Analytics and business intelligence — querying curated data in the lakehouse for dashboards and reporting, similar in purpose to a data warehouse.
• Machine learning and AI — training, tracking and deploying models, using the same platform and data that feeds analytics, rather than moving data to a separate ML-specific system.

A multi-cloud, third-party platform

Databricks is not owned by AWS, Azure or Google — it is an independent company whose platform runs on top of all three. Azure Databricks is the one exception worth naming: it is sold and billed as a native Azure service and integrates tightly with Azure’s identity and networking, making it feel closer to a first-party Azure offering than the AWS or Google Cloud versions do. On any of the three clouds, the underlying storage, compute and networking are still that provider’s infrastructure — Databricks runs on top of it rather than replacing it, which is why it is often described alongside, rather than instead of, each cloud’s own native data services.

Where this appears in cert study

Databricks is not the focus of any single certification in the exam-prep catalogue here, but the concepts underneath it — distributed processing, the lakehouse and data-lake-versus-warehouse trade-offs — are core material in the Microsoft Fabric Data Engineer Associate exam and the AWS Data Engineer Associate exam, both of which expect you to recognise when a lakehouse-style architecture fits a described workload. Google Cloud’s Associate Cloud Engineer exam touches on the equivalent analytics landscape at a broader, more conceptual level.