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

Rohan_Samariya
Databricks Partner

‎11-16-2025 10:23 PM

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

Rohan

bianca_unifeye
Databricks MVP

‎11-17-2025 07:40 AM

@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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