@Rohan_Samariya  this is fantastic work! 🚀🙌

I’m genuinely impressed with how you’ve taken the Databricks stack end-to-end: S3 ingestion → PySpark transformations → Delta optimisation → interactive SQL dashboards. This is exactly the type of hands-on, full-lifecycle learning that accelerates your capability as an engineer.

What I really love here is that you’ve not just followed a tutorial — you’ve stitched together a proper Lakehouse pattern with clean bronze → silver progression, Delta reliability, and data products you can iterate on. This is strong work. 👏

Now… for the next step 👇

Let’s start thinking about packaging all of this into two things:

 An AI/BI Genie space

Where:

• dashboards become smart with contextual insights,

• queries become conversational through an LLM layer,

• and users can ask: “Why did sales spike in July?” and get an intelligent breakdown.

This will push you into agentic workflows, RAG over Delta tables, and MLflow integration — all the good stuff.

 A Databricks App to expose your data & insights

Databricks Apps will allow you to:

• package the ETL + dashboard + ML components into a single deployable application,

• expose data securely to internal teams without moving it anywhere else,

• build UI components directly on top of your Lakehouse (instead of relying on external BI).

This is the direction the industry is moving fast: Lakehouse-native applications.

If you combine your existing pipeline with:

• Databricks Apps

• AI/BI Genie

• Real-time insights

• MLflow models (as you mentioned!)

You will have an accelerator with all databricks features😉 I recommend to also do a 5 min video and post it on social media such as Linkedin with your journey but also on Youtube.

Keep going!