Google Sheets and Microsoft SQL integration

This workflow automates currency rate uploads into SAP Business One via Service Layer, using flexible input sources such as JSON (API), SQL Server, Google Sheets, or manual values. It leverages logic branching, AI validation, and logging for complete control and traceability. ++⚙️ HOW IT WORKS:++ 🔹 1. Receive Data via Webhook The workflow listens on the endpoint /formulario-datos via HTTP POST. The request body should include: origen: one of JSON, SQL, GoogleSheets, or Manual Depending on the value, the flow branches accordingly. 🔹 2. Authenticate with SAP Business One A POST request is sent to SAP B1’s Login endpoint. A session cookie (B1SESSION) is retrieved and used in all subsequent API calls. 🔹 3. Switch by Origin The flow branches into four processing paths based on origen: JSON: The payload is normalized using OpenAI to extract an array of rates. Each rate is sent to SAP individually after parsing. SQL: The SQL query provided in the payload is executed on a connected Microsoft SQL Server. The results are checked by AI to validate the date format. If valid, rates are sent to SAP. GoogleSheets: Rates are pulled from a connected spreadsheet. Each entry is sent to SAP in sequence. Manual: Uses currency, rate, and rateDate directly from the webhook payload. Sends the result directly to SAP. 🔹 4. AI-Powered Enhancements (Optional but enabled) Normalize JSON: Uses OpenAI (LangChain node) to convert any messy structure into a uniform array under the key rate. Date Formatting: Another OpenAI call ensures RateDate is in yyyyMMdd format (required by SAP), converting from ISO, timestamp, or other formats. 🔹 5. Send to SAP Business One (Service Layer) All paths send a POST request to: /SBOBobService_SetCurrencyRate With a payload such as: { "Currency": "USD", "Rate": "0.92", "RateDate": "20250612" } 🔹 6. Log Results All success/failure results are appended to a Google Sheets log (LOGS_N8N) The log includes method, URL, sent payload, status code, and message. ++🛠 SETUP STEPS:++ 1️⃣ Create Required Credentials: Go to Credentials > + New Credential and configure: SAP Business One (Service Layer) Type: HTTP Request Auth or Token Base URL: https://<your-host>:50000/b1s/v1/ Provide Username, Password, and CompanyDB via variables or fields Google Sheets OAuth2 connection to a Google account with access Microsoft SQL Server SQL login credentials and host OpenAI API key with access to models like GPT-4o 2️⃣ Environment Variables (Recommended) Set these variables in n8n → Settings → Variables: SAP_URL=https://<host>:50000/b1s/v1/ SAP_USER=your_username SAP_PASSWORD=your_password SAP_COMPANY_DB=your_companyDB 3️⃣ Prepare Google Sheets Sheet 1: RATE (for charging the data) Columns: Currency, Rate, RateDate Sheet 2: LOGS_N8N (to save the logs, success or failed) Columns: workflow, method, url, json, status_code, message 4️⃣ Activate and Test Deploy the webhook and grab the URL. ++✅ BONUS++ Built-in AI assistance for input validation and structure Logs all results for compliance and audit Flexible integration paths: perfect for hybrid or transitional systems

WORKFLOW OVERVIEW This workflow is an AI-powered business intelligence agent designed for founders and business owners. It automatically collects key business metrics, calculates performance KPIs, applies decision logic, uses AI reasoning, and sends clear, actionable notifications — without dashboards or manual reports. Key Features: ✅ Aggregates multiple data sources (MSSQL, Google Analytics, Google Sheets) ✅ Calculates critical KPIs: ROAS, CAC, Revenue & User Growth ✅ Applies rule-based decision logic for business risk and opportunity detection ✅ AI-powered reasoning: summarizes insights and recommends actions ✅ Multi-channel notifications: Email, WhatsApp, Slack, Telegram ✅ Fully automated daily execution via Cron trigger ✅ Enterprise-ready: error handling, structured data, KPI validation Setup & Requirements: API access to data sources (MSSQL, Google Analytics, Google Sheets) OpenAI or Google Gemini API for AI reasoning Messaging integration: Gmail, Twilio (WhatsApp), Slack, Telegram Workflow Flow: Cron Trigger – runs daily at a chosen time Data Collection – revenue, users, marketing spend, website analytics Merge Node – combines all data sources Function Node – consolidates into a single JSON object KPI Calculation – calculates ROAS, CAC, growth rates Business Logic Engine – identifies risks and opportunities AI Reasoning Agent – summarizes insights, suggests actions Notification Formatter – builds founder-friendly message Notification Delivery – sends via WhatsApp, Email, Slack, or Telegram Example Data Formation These data below Getting from all different channels. `{ "revenue": 4290, "registeredUsers": 20, "totalUsers": 3, "adSpend": 800 }` Applies rule-based logic to detect potential risks or opportunities `{ "ROAS": 5.36, "CAC": 40, "agentStatus": "normal", "agentPriority": "low", "insights": ["Marketing campaigns are performing very well"] }` Workflow Highlights Fully automated, runs daily without human intervention Integrates multiple business data sources Converts raw data into KPIs for actionable insight Applies both rule-based logic and AI reasoning Generates concise, human-friendly notifications Sending notification to different channels.

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 Trigger the workflow Schedule Trigger** – Runs the workflow at set weekly intervals. 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. 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. Merge and process Merge** – Combines all tagged datasets into a single structure. Process Merged Data** – Cleans, aligns schemas, and standardizes key fields. 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.