Microsoft SQL and MongoDB integration

Consolidate data from 5 sources for automated reporting with SQL, MongoDB & Google tools

How it works This workflow consolidates data from five different systems — Google Sheets, PostgreSQL, MongoDB, Microsoft SQL Server, and Google Analytics — into a single master Google Sheet. It runs on a scheduled trigger three times a week. Each dataset is tagged with a unique source identifier before merging, ensuring data traceability. Finally, the merged dataset is cleaned, standardized, and written into the output Google Sheet for reporting and analysis. Step-by-step 1. Trigger the workflow Schedule Trigger – Runs the workflow at set weekly intervals. 2. Collect data from sources Google Sheets Source – Retrieves records from a specific sheet. PostgreSQL Source – Extracts customer data from the database. MongoDB Source – Pulls documents from the defined collection. Microsoft SQL Server – Executes a SQL query and returns results. Google Analytics – Captures user activity and engagement metrics. 3. Tag each dataset Add Sheets Source ID – Marks data from Google Sheets. Add PostgreSQL Source ID – Marks data from PostgreSQL. Add MongoDB Source ID – Marks data from MongoDB. Add SQL Server Source ID – Marks data from SQL Server. Add Analytics Source ID – Marks data from Google Analytics. 4. Merge and process Merge – Combines all tagged datasets into a single structure. Process Merged Data – Cleans, aligns schemas, and standardizes key fields. 5. Store consolidated output Final Google Sheet – Appends or updates the master sheet with the processed data. Why use this? Centralizes multiple data sources into a single, consistent dataset. Ensures data traceability by tagging each source. Reduces manual effort in data cleaning and consolidation. Provides a reliable reporting hub for business analysis. Enables scheduled, automated updates for up-to-date visibility.