File-based Postgres Analytics with DuckDB and AWS S3

Tutorial: File-based Postgres Analytics with DuckDB and AWS S3

1. Setting Up the Environment

1. Access the GitHub repository provided in the video to follow along with the tutorial.
2. Set up the necessary environment variables for your Postgres database and AWS S3 storage.
3. Ensure you have the required packages installed in your Jupyter notebook environment.

2. Connecting DuckDB to AWS S3 and Postgres Database

1. Utilize DuckDB to query files directly on any S3 compatible object storage and connect to any Postgres database.
2. Set up the connection by providing the necessary details such as database name, username, password, S3 endpoint URL, and AWS credentials.
3. Use DuckDB's secrets manager to securely pass AWS credentials for querying and exporting files.

3. Analyzing Data with DuckDB

1. Install the Postgres extension to call Postgres database tables easily.
2. Query data directly from your Postgres database using DuckDB within your Jupyter notebook environment.
3. Visualize the queried data using Pandas data frames and Jupyter's table formatting capabilities.

4. Exporting Data to AWS S3

1. Copy queried data as Parquet files or CSV files to your AWS S3 storage bucket.
2. Partition larger files to manage file sizes effectively, especially considering any file upload limit sizes on your storage plan.
3. Explore the stored data in your AWS S3 bucket to ensure successful file exports.

5. Analyzing and Visualizing Data

1. Query the stored data in your AWS S3 bucket using DuckDB's read_par function.
2. Utilize file globbing to select specific files for querying and analysis.
3. Join multiple datasets together for a fully denormalized table view for comprehensive data analysis.

6. Performing Data Analytics

1. Use DuckDB to perform lightweight data analytics on your Postgres database data.
2. Generate insights such as monthly sales figures, order statuses, and trends using DuckDB's querying capabilities.
3. Visualize the analyzed data using data visualization libraries in Python for enhanced insights.

7. Conclusion

1. Experiment with different data analytics tools and techniques on your Superbase storage using DuckDB and AWS S3.
2. Explore further possibilities for data analysis and visualization to derive meaningful insights from your Postgres database.

By following these steps, you can effectively utilize DuckDB and AWS S3 for file-based Postgres analytics as demonstrated in the video tutorial.