Consolidate data from 5 sources for automated reporting with SQL, MongoDB & Google tools
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: Function (legacy). 1 breaking item(s) found. Static check, last run 2026-10-19.
What to replace before upgrading
| Removed in 3.0 | Replace with |
|---|---|
| Function (legacy) ⚙️ Process Merged Data, Add Sheets Source ID, Add PostgreSQL Source ID, Add SQL Server Source ID, Add MongoDB Source ID, Add Analytics Source ID | Code node, mode 'Run Once for All Items' |
Nodes used (8)
- Google Sheets
n8n-nodes-base.googleSheets - Postgres
n8n-nodes-base.postgres - MongoDB
n8n-nodes-base.mongoDb - Function
n8n-nodes-base.function - Microsoft SQL
n8n-nodes-base.microsoftSql - Google Analytics
n8n-nodes-base.googleAnalytics - Merge
n8n-nodes-base.merge - Schedule Trigger
n8n-nodes-base.scheduleTrigger
What it does
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.