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

Rohan_Samariya — Mon, 17 Nov 2025 06:23:16 GMT

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

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

bianca_unifeye — Mon, 17 Nov 2025 15:40:32 GMT

@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!

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