What is Databricks

Anyone talking about data platforms today will come across the name Databricks. In mid-sized companies it often reads like a tool for big players with huge IT departments: expensive, complex, only for big data. I hear that often, even though the reality has long shifted. Here is a short, honest take on what it is and whether it fits you.

The idea behind it: a house by the lake

Databricks is a platform where a company collects and processes its data, runs all of its analytics in one place, and shares results from there in a controlled way.

Its heart is the lakehouse, joining two worlds that used to be separate: the data lake, a big cheap storage for every kind of data, and the data warehouse, a fast orderly place for reports, but expensive and rigid. The lakehouse takes the best of both.

What you can use it for

Here are three ways the platform can be used.

Break up data silos and bring data together. Excel, an ERP, a CRM, sensors on a machine. Databricks gathers all this data in one place, keeps it clean, and makes it available for reports or ML models.

Reports and analytics. Dashboards that access lakehouse data directly, with consistent numbers and no intermediate copies. The built-in dashboard editor and AI assistants let you build dashboards quickly.

Data quality and governance. Who sees what, how long data stays, which version is current: the platform handles all of this centrally through Unity Catalog.

A force for innovation

Databricks is not just a tool, but one of the defining innovation drivers in the data space. Many of the standards the industry follows come from here. Take Declarative Automation Bundles (formerly Databricks Asset Bundles), a unified way to describe data pipelines as code. Or governance, where Databricks leads with concepts like AI governance and Unity Catalog.

Who the platform truly fits

Above I mentioned the misconception that Databricks is only for large corporations with big IT departments and generous budgets. I stand by that: any company facing one of the problems above should consider the Databricks platform.

Here are two arguments for why Databricks also works for small budgets and earlier skill levels. In the early days, the focus was on data engineers and data scientists. But the platform has opened up for non-technical users too, as more and more visual tools and AI assistants appear. It also follows a pay-as-you-go approach: the less you use, the less you pay. Larger data volumes cost more, but can be managed through cost optimization. I am happy to advise on both points.

I first used Databricks in the chemical industry and later operated it for a logistics company. What convinces me is the speed of development, both from Databricks and from developers working with the platform. After short onboarding, everyone can find their way around it.

If you are unsure whether Databricks fits your situation, feel free to reach out.