Day 3 — Data Storage and Basic Reporting

Day 3 — Data Storage and Basic Reporting

Day_003_Data_Storage_and_Reporting.md

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+## 🎯 Goal
+Persist the processed data for historical analysis and send a **daily summary report** to stakeholders.
+
+---
+
+## 🧩 Tasks
+
+### 1. Set Up a Data Destination
+- Create a **Google Sheet** or **Database Table**.
+- Define columns such as:
+Date | ProductID | TotalRevenue | UnitsSold | AvgSentimentScore
+
+yaml
+Copy code
+
+---
+
+### 2. Store the Processed Data
+- Add a **Google Sheets Node** after the Python node.
+- Authenticate your Google account.
+- Configure it to **Append** new rows.
+- Map each field from the Python output to the corresponding columns.
+
+---
+
+### 3. Craft the Summary Report
+- Add a **Set Node** to create a short summary message.
+- Example:
+Daily Report: Top product by revenue was {{ $json.product_name }} with ${{ $json.total_revenue }} in sales.
+
+yaml
+Copy code
+
+---
+
+### 4. Send the Report
+- Add a **Slack** or **Email Node**.
+- Send the summary to:
+- A Slack channel (e.g., #daily-sales-reports), or
+- A mailing list for stakeholders.
+
+---
+
+## ✅ Deliverable
+- The workflow appends daily data to a Google Sheet.
+- A formatted summary is sent automatically to Slack or email.