Building an End-to-End ETL Pipeline with Data from S3 in Databricks

[Rohan_Samariya]

Hey everyone 👋

I’m excited to share the progress of my Databricks learning journey! Recently, I worked on building an end-to-end ETL pipeline in Databricks, starting from data extraction from AWS S3 to creating a dynamic dashboard for insights.

Here’s how I approached it 👇

Step 1: Extract – Bringing Data from S3

I connected Databricks to an AWS S3 bucket to read raw data (CSV files).
Using PySpark, I mounted the S3 location and read the data directly into a DataFrame:

df = spark.read.option("header", True).csv("s3a://roh-databricks-v1/ecommerce-data/S3 Order Line Items.csv")

This step helped me handle large files efficiently without manual uploads.

---

Step 2: Transform – Cleaning and Structuring Data

Next, I applied several transformations:

• Removed duplicate records

• Handled missing values

• Formatted date and numeric columns

• Derived new calculated fields for better reporting

The transformation logic was implemented using PySpark DataFrame APIs, which made the process scalable and easy to modify.

---

Step 3: Load – Creating a Delta Table

After cleaning the data, I stored it as a Delta table to take advantage of ACID transactions, versioning, and easy querying:

df.write.format("delta").mode("overwrite").saveAsTable("etl_demo_1.silver.orders_cleaned")

This made it simple to query and use the data later for analysis or dashboards.

---

Step 4: Visualization – Building a Dynamic Dashboard

Once the Delta table was ready, I used Databricks SQL to create a dashboard that visualizes:

• Total sales by category

• Monthly revenue trends

• Top-performing products

The dynamic dashboard updates automatically whenever the data in Delta changes, giving real-time insights directly from the Lakehouse.

Key Learnings

• Connecting Databricks with AWS S3 simplifies data ingestion.

• Delta Lake ensures reliability, version control, and smooth updates.

• Databricks SQL dashboards are great for interactive analysis without needing external BI tools.

This project gave me a strong understanding of how Databricks can handle the entire data pipeline — from raw data ingestion to insight generation.

I’m planning to enhance this next by integrating MLflow to perform predictive analysis on sales data.

Thanks to @bianca_unifeye for inspiring this project idea, really appreciate the push to apply my learning through hands-on work!

#Databricks #ETL #DeltaLake #DataEngineering #AWS #DataAnalytics #MLflow #Dashboard

[bianca_unifeye · Accepted solution]

@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!