How to Build an ETL Pipeline in Databricks

 Introduction 

Every company works with data. But raw data straight from websites, apps, or devices is usually messy and hard to use. It might have missing values, be in different formats, or come from multiple places. So before we can use that data for reports, dashboards, or machine learning, we need to clean it up. 

That’s where ETL pipelines come in. ETL stands for: 

• Extract – Get the data from different sources 
• Transform – Clean and change the data 
• Load – Save the data to a system where it can be used 

Databricks makes building ETL pipelines easier because it brings everything together—storage, code, tools, and teamwork on one platform. 

In this blog, you’ll learn how to build a basic ETL pipeline using Databricks, explained in simple terms. 

 

Agenda 

• What is an ETL pipeline? 
• Step 1: Extract – Get the data 
• Step 2: Transform – Clean and prepare the data 
• Step 3: Load – Save the clean data 
• Automating the pipeline 
• Conclusion 

1. What is an ETL Pipeline? 

Think of an ETL pipeline like a water filter system: 

• First, you collect dirty water (Extract) 
• Then, you clean it (Transform) 
• Finally, you store it in a bottle for drinking (Load) 

In the same way, an ETL pipeline takes raw data from sources (like files, databases, or websites), cleans and formats it, and then stores it in a place like a data warehouse. 

With Databricks, you can write and run your ETL steps in a notebook, using simple code and tools like Spark. 

 

2. Step 1: Extract – Get the Data 

Let’s say we want to process sales data stored in a CSV file. 

In Databricks, you can read a file like this: 

df = spark.read.format("csv").option("header", "true").load("/databricks-datasets/sample-data/sales.csv") 
df.show(5) 
  

This command loads the CSV file into a DataFrame, which is a table-like structure you can work with. You can also extract data from databases (like MySQL or PostgreSQL), cloud storage (like AWS S3), or real-time tools like Kafka. 

 

3. Step 2: Transform – Clean and Prepare the Data 

Now that you’ve loaded the data, the next step is to clean it. 

Here are some common things you might do: 

• Remove rows with missing values 
• Change data types (e.g., from string to number) 
• Add new columns (like total price = quantity × unit price) 
• Filter rows (like only keeping sales from the year 2024) 

Example: 

from pyspark.sql.functions import col 
 
# Remove rows with missing data 
clean_df = df.dropna() 
 
# Add a new column for total sales 
final_df = clean_df.withColumn("total_sales", col("quantity") * col("unit_price")) 
 
final_df.show(5) 
  

This step is where you make the data usable. Clean data means better reports, better dashboards, and better decisions. 

 

4. Step 3: Load – Save the Clean Data 

Once your data is cleaned and ready, you need to save it somewhere. Databricks can write data to many places, such as: 

• Delta Lake (Databricks’ optimized format) 
• Parquet or CSV files 
• SQL Databases 
• Cloud storage (AWS S3, Azure, GCP) 

Example of saving as a Delta table: 

final_df.write.format("delta").mode("overwrite").save("/mnt/cleaned-sales-data") 
  

You can also register this as a table for others to use: 

spark.sql("CREATE TABLE cleaned_sales USING DELTA LOCATION '/mnt/cleaned-sales-data'") 
  

Now your clean data is saved and ready for use in reports, dashboards, or machine learning. 

 

5. Automating the Pipeline 

You don’t want to run this process manually every time. Databricks lets you schedule jobs that run your ETL pipeline daily, hourly, or based on a trigger (like a file arriving). 

You can do this by: 

• Saving your ETL code in a notebook 
• Going to “Jobs” in Databricks 
• Creating a new job and choosing your notebook 
• Setting the schedule (e.g., run every night at 2 AM) 

This turns your ETL process into an automatic system that runs by itself and keeps your data updated. 

 

Conclusion 

ETL pipelines are the backbone of every data system. They turn messy, raw data into clean, usable information. 

With Databricks, building ETL pipelines becomes simple—even if you’re not a professional developer. You can: 

• Load data from different sources 
• Clean and transform it using Python and Spark 
• Save it to a secure place like Delta Lake 
• Automate the process so it runs by itself 

If you’re just getting started with data projects or want to handle big data more easily, learning how to build ETL pipelines in Databricks is a great first step. It saves time, improves data quality, and helps your team make better decisions faster. 

What’s Next? Build Real-World ETL Pipelines That Scale 

Want to go from beginner to pro in building data pipelines? At AccentFuture, we teach you how to build real-world ETL workflows using Databricks, Spark, and Delta Lake — just like top data teams do. 

Learn how to automate, monitor, and scale ETL pipelines that power dashboards, reports, and ML models. Our hands-on courses walk you through everything from ingesting raw files to deploying production-grade pipelines on cloud platforms like AWS, Azure, and GCP. 

• 👩‍💻 Work with real datasets 
  
• 📊 Clean, transform, and load like a pro 
  
• 🚀 Optimize and schedule your ETL flows with ease 

Join us and start building pipelines that fuel smarter decisions. 

• 📓 Enroll now: https://www.accentfuture.com/enquiry-form/ 
  
• 📧 Email: contact@accentfuture.com 
  
• 📞 Call: +91–9640001789 
  
• 🌐 Visit: www.accentfuture.com 

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• https://www.accentfuture.com/2025-dlt-update-intelligent-fully-governed-data-pipelines/
• https://www.accentfuture.com/dimensional-data-warehouse-databricks-sql/
• https://www.accentfuture.com/databricks-architecture-overview/
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