My road to becoming a Databricks Champion

The road to mastering a certain subject is often a long, hard and unsettling road. It takes numerous hours and days to become good at something, let alone master something. This is a blog about my road to becoming a Databricks Champion. The Databricks Champion program is an invitation-only program, where it’s required that the company you work for has a registered and successful partnership with Databricks. The description of a Databricks Champion according to Databricks themselves is the following:

Databricks Champions are evangelist and leaders of success for their Unified Analytics and Machine Learning practices. They are our extended Solutions Architects family, at their best.

I will talk about my motives, the help I got from Databricks and my overall experiences during the path

My introduction to Databricks

During my university years, I got already acquainted with Spark, and in lesser forms Databricks. This was way back in 2020, when Databricks was still finding its place in the big landscape of data platforms and cloud solutions. For a group project, we ended up using a local variant of Spark, still young and unknowing about the power of Databricks.

After graduating, I started to work at Aivix, who are a Partner of Databricks. This meant that a lot more resources became available, partly in computing power, partly in learning paths. During my first project, I briefly got introduced to Databricks. My interest in the platform got sparked when I saw the capabilities. This prompted me to start a learning path, which were available because of our partnership with Databricks. The learning path I started with was the entry level for a lot of people: Data Engineering Associate

Step 1: Becoming an Associate

You also get a taste of one of the other main features of Databricks: Unity Catalog. A quick overview of what you can do plus some demos and Unity Catalog is no longer just a buzz-word, but a meaningful addition to your knowledge set.

For me, the Data Engineering Associate was a very gentle way into the Databricks platform. The demos that are included with the lessons give you a nice intro on what you can do with Databricks. The exam to get the certification was not too hard if you have studied the material well. It is feasible for every developer that is willing to start the journey with Databricks. But of course, this is only the start of the road towards becoming a Champion. Courses and learning paths are nice, but you only get to know the real deal and the real problems and solutions when implementing the Databricks platform at actual companies…

Step 2: Implementing the Databricks Platform at a customer

Most of the customers I worked at, already had a Databricks platform in place. Getting to know how it was set up and checking the different pipelines allowed me to transform the knowledge I got from the Associate learning path into real-life cases. It showed me that theory and practice do not always align. During your work at a customer, there are a lot more things to take into account compared to a training environment. You need to know the business specifics, you need to know whether or not a certain project can be done in Databricks and you need to be able to combine different systems with each other. A notable use-case I did and that really showcased my growing knowledge of Databricks was the following: we received tons of gigabytes of files per day, and in the legacy architecture, they were structured as parquet files in separate, hardcoded partitions on year, month, day and hour.

After analyzing the access patterns and data volume, I proposed migrating the data to a Delta table and applying Liquid Clustering—a newer, more flexible clustering technique in Databricks that adapts to query patterns over time.

The result? We reduced query times by about 50%, while also simplifying the data ingestion process. More importantly, it opened up a broader conversation with the team about how modern features like Delta Lake and Liquid Clustering can future-proof pipelines and lower infrastructure costs.

This project not only highlighted Databricks’ technical potential but also taught me the importance of aligning data architecture with evolving business needs. It’s one thing to follow best practices; it’s another to adapt them meaningfully in a real-world, high-pressure environment and that is something you don’t always experience in a Learning Path.

Step 3: Becoming a Professional

Step 4: Becoming a Solutions Architect and joining the community

As a final preparation for my interview, I had the opportunity to follow a 3 day course by Databricks on Solutions Architects Essentials. This comprehensive bootcamp showed the ins and outs of how to tackle different types of projects. From migrations to greenfield implementation, almost everything was covered.

After all these different steps, it was time for my final test: a panel review with Guillermo G. Schiava D’Albano of Databricks!

Step 5: Becoming a Champion, the panel review

Becoming a Champion gets you a lot of benefits, some of them, amongst others are:

• Monthly sessions with other Champions to discuss the latest new features or special projects you did
• Access to the quarterly roadmap of Databricks, to stay on top of the new developments
• Access to learning materials, special sessions
• Invitation to Databricks internal tech summit

It was very nice to see myself as well as Databricks evolve over time. Of course, the journey is not yet to an end, but it will continue over the coming the years. Databricks will evolve, and then it’s up to the Champions to keep up