Dear Databricks Community,
One thing I have learned from my data engineering experience is that big solutions do not always start big.
Many times, they start with one simple question.
Can we make this easier?
Can we make this faster?
Can we help someone make a better decision with data and AI?
As data engineers, we usually think about pipelines, tables, jobs, transformations, performance, and dashboards. These are all important. Without strong data engineering, there is no trusted foundation for analytics or AI.
But in the current Data + AI world, I feel our role is expanding.
We are not only building pipelines anymore. We are also helping build intelligent solutions.
I started seeing this more clearly while working on practical POCs and experiments using Databricks. Whether it was a movie discovery idea, a grocery data intelligence use case, or an HR assistant concept like AirHR One, the starting point was not a perfect enterprise project.
The starting point was always a small problem.
A movie discovery POC can begin with a simple idea: can we help users find movies based on meaning, mood, and context instead of only keywords?
A grocery intelligence use case can begin with a question: can data help stores understand demand better, reduce waste, and plan smarter?
An HR assistant idea can begin with a need: can employees and managers get trusted answers faster, take simple actions, and reduce manual back-and-forth?
These ideas may look small in the beginning. But when we add trusted data, good engineering, AI thinking, governance, and a platform like Databricks, small POCs can slowly become meaningful Data + AI solutions.
That is the real power of experimentation.
A POC does not need to be perfect on day one. It does not need every feature. It does not need to solve everything. The purpose of a POC is to test an idea, learn from it, and understand whether it can create real value.
From my experience, a good POC usually has three things.
It solves a real problem.
It uses real or realistic data.
It helps someone understand, decide, or act faster.
This is where Databricks gives us a strong advantage. We can bring data engineering, Delta Lake, SQL, PySpark, notebooks, dashboards, governance, machine learning, and GenAI ideas together in one ecosystem. We can start small, test quickly, and improve step by step.
Sometimes we wait for a big project, a big team, a big budget, or perfect requirements. But many useful ideas can begin with one dataset, one notebook, one business question, and one simple AI use case.
The industry is also moving in this direction.
Businesses do not just want more reports. They want faster insights, natural language analytics, intelligent alerts, trusted AI answers, and smarter automation. They want to reduce the time between data and decision.
To support this, data engineers need to think beyond only โDid the pipeline run?โ
We also need to ask:
Did the data help someone?
Did the output create trust?
Did the insight lead to action?
Can AI make this workflow simpler?
Can this small POC become a reusable solution?
Not every POC will become a product. Not every idea will become a big solution. But every practical experiment improves the way we think as builders.
For me, this is one of the most exciting parts of the Databricks ecosystem. We can move from idea to experiment quickly. We can build, test, improve, and share what we learn with the community.
And when more people share practical POCs, everyone benefits.
A beginner gets inspiration.
A data engineer gets a new design idea.
An architect sees a possible pattern.
A business user understands what is possible.
That is how knowledge grows.
My encouragement to every data professional is simple: do not wait for the perfect Data + AI project.
Pick one pain point from your work.
Pick one dataset you understand.
Pick one user problem.
Pick one small AI idea.
Build a simple version in Databricks and improve it.
Maybe it starts as a notebook. Then it becomes a dashboard. Then it becomes an app. Then it becomes an assistant. Slowly, it can become a real business solution.
Small POCs can become big Data + AI solutions when we combine curiosity, practical data engineering, trusted data, and continuous learning.
What is one small POC you would like to build in Databricks that could become a useful Data + AI solution?
