Databricks: what it is and when it is worth it for your company
Databricks keeps coming up in AI projects. Without the jargon: what the Lakehouse is, where Databricks shines, when it is too big for a company - and when Fabric or Snowflake serve better.
Richard Böhmer, MSc
There’s a lot of marketing around Databricks - one moment it’s the “solution to everything”, the next a “toy for corporations”. The truth sits somewhere in between. Let’s look at it soberly: what it is, where it shines and when it makes sense for your company - and when it doesn’t.
What is Databricks
Databricks is a cloud data platform built around the concept of a Lakehouse. It was founded by the creators of Apache Spark, and its core idea is to merge two worlds that were separate for years:
- A data lake - cheap storage for huge amounts of data in any format (including raw, semi-structured).
- A data warehouse - fast, structured queries for reporting and analytics.
The “Lakehouse” combines both: one place for all your data, on top of which reporting, data science and AI all run - without endless copying between systems.
Key components:
- Delta Lake - a reliable table format over the data lake (transactions, versioning, quality).
- Apache Spark - powerful processing of large data volumes.
- Unity Catalog - governance, access rights and a data catalogue in one place.
- MLflow and AI tools - training, deploying and managing models.
What Databricks excels at
- Large data volumes - terabytes and more, where a classic SQL warehouse hits its limits.
- Data engineering - robust ETL/ELT pipelines over heterogeneous sources.
- Data science and AI/ML - this is Databricks’ home turf. If you plan predictive models or your own AI over data, it’s among the best.
- Streaming and real-time - processing data that flows continuously.
- Openness - it’s built on open standards (Delta, Spark), so there’s less vendor lock-in risk.
When Databricks is overkill
Let’s be honest - not every company needs Databricks. It’s a powerful tool that also comes with cost and complexity.
- A small company with one or two systems and reporting in Power BI - a simpler data warehouse is enough; Databricks would be using a sledgehammer to crack a nut.
- Mostly classic BI reporting without AI and without huge volumes - cheaper and simpler solutions do the same job.
- A team without data engineers - the platform has a learning curve; without people who can use it, it stays underused.
We took an equally sober look at Microsoft Fabric in Microsoft Fabric: is it worth switching to?.
Databricks vs Microsoft Fabric vs Snowflake
A simplified comparison - reality always depends on the specific case:
| Databricks | Microsoft Fabric | Snowflake | |
|---|---|---|---|
| Strength | AI/ML, big data, engineering | Microsoft ecosystem, Power BI | Simple cloud warehouse, SQL |
| Best for | Data-mature firms, AI ambitions | Companies on Microsoft | Companies wanting quick and simple |
| Learning curve | Higher | Medium | Lower |
| Data format | Open (Delta) | OneLake (Delta) | Mostly proprietary |
It’s not about “which is best”, but which fits your data, team and goals. That’s exactly what we solve in data strategy - vendor-neutral, with no licences to sell.
How much does it cost
Databricks is paid mainly for the compute you consume - not a flat software fee. That’s both an advantage (you pay for real usage) and a risk (without control, costs can grow). That’s why it pays to design and optimise the platform well. For the overall cost framework of a data project, see How much does a data warehouse or BI project cost.
How we approach it
We’re not sellers of a single platform. First we understand your data, team and goals - and only then do we recommend whether Databricks is the right choice, or whether Fabric, Snowflake or a simple SQL warehouse would serve you better. The goal is value for a reasonable price, not the biggest technology.
Considering Databricks, or not sure which data platform is right for you? Check out our data strategy or get in touch - on a no-obligation consultation we’ll give you straight advice.