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What is Databricks?
Databricks is a cloud-based data platform founded by the creators of Apache Spark. It combines data engineering, data warehousing, machine learning, and generative AI into a single environment—the Data Intelligence Platform concept.
Databricks is a pioneer of the Lakehouse approach: a combination of the flexibility of a data lake and the structure of a data warehouse. Its core component is Delta Lake —an open format with ACID transactions in object storage.
The platform offers specialized tools for different roles: notebooks for data scientists, SQL warehouses for BI users, jobs and pipelines for data engineers, and MLflow for ML operations. Everything is centrally managed in Unity Catalog.
Databricks is particularly relevant for midsize businesses when ambitious data science and AI projects are on the horizon. For pure BI use cases, alternatives such as Fabric or Snowflake are often more streamlined.
Best Practices for Databricks Projects
These six principles have proven effective:
- Use Unity Catalog from the start: Implementing governance later is a hassle—start right away.
- Medallion architecture: Bronze/Silver/Gold for quality tiers—proven.
- Check cluster configuration: Incorrect cluster size = the number one cost driver.
- Use Delta Live Tables: Declarative pipelines require less maintenance.
- MLflow from day one: Experiment tracking and model registry—no need for reverse engineering later.
- Cost Management: Budget alerts and cluster policies—Databricks can quickly become expensive.
Common Mistakes in Databricks Projects
We frequently encounter these pitfalls in customer projects:
- Cost Spiral: Large clusters run 24/7 — cost management is configured too late.
- No Unity Catalog: Governance is chaotic—permissions are scattered across workspaces.
- Overengineering: Databricks is implemented for simple BI use cases—Fabric or Snowflake would be a more streamlined solution.
- No CI/CD: Notebooks are deployed manually — reproducibility suffers.
- Small-chunked files: Unoptimized delta tables — run OPTIMIZE regularly.
Databricks vs. Microsoft Fabric vs. Snowflake
Three leading cloud data platforms—each with its own strengths:
- Databricks: Best lakehouse experience, strong for data science and ML. Multi-cloud.
- Microsoft Fabric: Ideal for Microsoft customers with a focus on BI. All-in-one approach.
- Snowflake: Very user-friendly, strong SQL data warehouse. Less robust in ML.
Databricks for Your Data & AI
In a free initial consultation, we’ll assess whether Databricks is the right platform for your needs—including a cost estimate and comparison with alternatives.
As a data and AI partner for small and medium-sized businesses, we’ll build your Databricks solution in a pragmatic way—with a focus on business value and cost control.
What We Offer
- AI Consulting — Machine learning on Databricks.
- BI Consulting — Databricks as a BI platform with dashboards and Genie.
- Software & Pipelines — Data engineering with Delta Live Tables.
- Lakehouse in the Glossary — the concept that has shaped Databricks.
