Google Sheets RAG Automation for PostgreSQL Data Management

Target Audience

• Data Analysts: Need to automate the process of importing Google Sheets data into PostgreSQL.
• Database Administrators: Manage and maintain PostgreSQL databases and require a streamlined method for data insertion.
• Business Analysts: Analyze data from Google Sheets and need a quick way to integrate it into their database for reporting.
• Developers: Looking to automate database operations and integrate with Google Drive and Sheets for seamless data management.

Problem Solved

This workflow automates the process of transferring data from Google Sheets into a PostgreSQL database. It handles:

• Dynamic Table Creation: Automatically creates tables based on the structure of the Google Sheets data.
• Data Validation and Transformation: Ensures data integrity by validating formats such as dates and currencies before insertion.
• Conditional Logic: Checks if a table already exists and removes it if necessary, preventing duplication and ensuring fresh data import.
• Efficient Data Insertion: Constructs and executes SQL insertion queries efficiently, allowing for bulk data operations.

Workflow Steps

1. Trigger: The process begins with a manual trigger or when a specific Google Sheets file is updated.
2. Fetch Data: Retrieves the data from the specified Google Sheets document and sheet.
3. Check Table Existence: Verifies if a corresponding table already exists in the PostgreSQL database.
4. Remove Existing Table: If the table exists, it is dropped to allow for a fresh import.
5. Create Table: Constructs a new table based on the schema inferred from the Google Sheets data, including types for each column (e.g., TEXT, TIMESTAMP, DECIMAL).
6. Prepare Insertion Query: Generates an SQL insertion query using the fetched data, ensuring values are correctly formatted and validated.
7. Execute Insertion: Executes the prepared SQL insertion query to populate the PostgreSQL table with the Google Sheets data.
8. AI Integration: Uses an AI agent to assist with generating SQL queries based on natural language prompts, enhancing user interaction and query generation.