Building ETL Pipelines in no time using ChatGPT

Given the volume, velocity, and diversity of data expanding at an exponential rate in the modern era, it is crucial to utilize this data for data analytics and machine learning projects to generate business insights. Let's discuss in this post how to utilize ChatGPT to develop ETL pipelines considering the growing popularity of ChatGPT in recent years.

What is an ETL (Extract, Transform, and Load) Pipeline

 Data must first be fetched from one or more sources, processed, or transformed in accordance with the requirements, and then loaded into a storage system that can be used directly by end users without the need for data validation or poor data quality.

 ETL Pipeline also known as a ‘Data Pipeline’ sometimes known as three phases.

Image 1: ETL Process Flow (Extract, Transformation, and Load)

During the ETL process, first, we fetch the data, and we perform data quality and validation checks on the extracted data. Once data is extracted then data pre and post-processing should be done to transform the data into a usable format. Once data processing is done, the last step is to store the data from where the end user can access this data. 

Let’s ask ChatGPT to build an ETL pipeline for data engineering.

Problem Statement

Using Databricks and Pyspark build an ETL pipeline using 3 layered approaches: Raw, Bronze, and Gold Layers.  Data should be ingested incrementally automatically, and data should be stored in Azure SQL Database. Business Analysts can use this data to derive business insights.

1. Input to ChatGPT with the programming language, and file information (location, format, delimiter, headers, output)

Image 2 : Input to chatGPT to create ETL Pipeline

 Here, the input has been given to the ChatGPT to build an ETL pipeline with all the required input.

2. Prerequisites suggested by ChatGPT before using the code are as follows:

 Image 3 : Prerequisite to setup ETL Flow

Here, ChatGPT first lists down the prerequisites to set up the blob storage, azure data lake storage gen2, and Databricks workspace.

3. Importing the necessary libraries and configuring the ADLS Gen2 storage credentials as shown in the preceding figure:

Image 4 : ADLS Gen2 configuration for Pyspark Notebook

This code configures ADLS gen2 using the pyspark notebook to connect and use the data using the storage account key. Here, you should replace the storage account name and key with your storage account name and key details. 

4. In this step, pyspark notebook creates the schema for the pyspark data frame based on the file information and we must replace adls container name with the actual container name of ADLS Gen2.

Image 5 : Create Structured Schema for pyspark dataframe

5. This piece of code renames the columns and once the data frame is ready, it will write the content of the dataframe to the delta format.

Image 6 : Pyspark Notebook - Data Transformation Logic 

6.  Finally, ChatGPT provides guidance on where to run this code and how to set up an automated pipeline using Databricks.

Image 7 : Final ChatGPT Output

7. At a first glance, it looks like it worked like a charm. Generated code can be directly used in the Databricks workspace to build an ETL pipeline. But the limitation of this solution is that it is hard coding the file path so it is not a generic code.

Image 8 : Pyspark Code Optimization - ChatGPT input

8.     ChatGPT creates a generic code that can be used to trigger the ETL pipeline whenever there is a new source file in the ADLS Gen2 container.

 Image 9 : ADLS Gen2 configuration for Pyspark Notebook

9.     Next step is to configure the ADLS Gen2 to connect to Databricks using the storage account key.

Image 10 : Schema Definition for CSV File

10.  Next step is to create a structured schema to use it while creating the pyspark dataframe in the next step:

 

Image 11: Setting up a loop to check new files

11.  As a final step, we will work towards optimizing the PySpark code:

Image 12: Optimised Pyspark Code for incremental data load

To process any file using the ETL pipeline code, the suggestion is to loop the code to continuously poll the storage container location to check if there is any new file and execute the code to process the new file if any.

So, we can use this code and set up Databricks notebooks in a couple of minutes to set up an automated data pipeline for the incremental data load.

Conclusion

It is getting much easier and more efficient to build ETL data pipelines using the ChatGPT. ChatGPT can also assist to create a generic and optimized code as per our requirements promptly without spending development efforts.

 Author Bio

Sagar Lad is a Cloud Data Solution Architect with a leading organization and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP.

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