From Code to Quality: Automating ETL and Data Validation with Amazon Q Developer
Traditional ETL development often involves writing extensive code and manual quality checks. This blog post demonstrates how Amazon Q Developer is changing this paradigm by introducing AI-assisted automation in JupyterLab, on how we approach data pipeline development and validation.
Govindhi
Amazon Employee
Published Apr 1, 2025
Last Modified Apr 2, 2025
Amazon Q is now seamlessly integrated with JupyterLab. To get started, follow the installation instructions in the official AWS documentation.
Let's walk through a practical example using a public dataset of US state population statistics:
Data Preparation: Place the dataset (USStates_Population.csv) in your JupyterLab working directory.
Launch and Access Amazon Q: Launch a terminal from the JupyterLab Launcher and enter q chat to launch Q.
Generating ETL Scripts: Use natural language to request script creation. Here's the prompt used with Q:
Create a notebook called us_population.ipynb that uses Apache Spark to:- Read the USStates_Population.csv file- Calculate population differences between 2010 and 2020- Identify states with highest and lowest population growthScreenshot showing the conversation with Q chat:
Here’s the screen shot showing the ipynb file Amazon Q created:
The generated python notebook executed successfully providing us the statistics from the dataset.
Data quality is crucial for reliable analytics and decision-making. Amazon Q can enhance your ETL processes with built-in data quality validation:
Here's the prompt used in conversation with Q:
> Create a notebook called us_population_data_validation.ipynb that uses Apache Spark to:- Read the USStates_Population.csv file- Calculate population differences between 2010 and 2020- Identify states with highest and lowest population growth- Add data quality checksAutomated Validation: Amazon Q generates comprehensive checks for:
- Schema validation
- Null value detection
- Data type consistency
- Value range verification
Below screenshots shows the data quality check added by Amazon Q in the notebook:
Amazon Q's natural language processing capabilities enable interactive data validation. In our testing, we deliberately introduced:
- Null values
- Schema mismatches
- Data inconsistencies
Amazon Q successfully identified these issues, demonstrating its effectiveness in maintaining data integrity.
Amazon Q successfully identified issues like formatting with the original dataset as well.
Amazon Q Developer in JupyterLab significantly enhances data engineering workflows by:
- Accelerating ETL script development
- Automating data quality checks
- Enabling natural language interactions for data validation
- Increasing overall productivity in data processing tasks
This powerful combination allows data engineers to focus on higher-value tasks while ensuring data quality and reliability.
Any opinions in this post are those of the individual author and may not reflect the opinions of AWS.