From 0a99d6b79f91c3b9d69d6b0c330b8dc4ac31b5bb Mon Sep 17 00:00:00 2001
From: Sankalp Modi <sankalpmodi94@gmail.com>
Date: Thu, 17 Jul 2025 17:25:35 +0530
Subject: [PATCH] Add restaurant_data_mart_PRD.md for managing restaurant data
 with LDP

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 resturant_data_mart_PRD.md | 84 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 resturant_data_mart_PRD.md

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+# Restaurant Data Mart - Product Requirements Document (PRD)
+
+## Objective
+To build a data mart for managing restaurant data, enabling seamless data collection, storage in Iceberg, and reporting to Slack through the LDP (Lakehouse Data Platform).
+
+---
+
+## Key Features
+
+### 1. Data Collection
+- **Source**: Collect data from restaurant management systems, APIs, and manual uploads.
+- **Data Types**:
+  - Restaurant details (name, location, cuisine, etc.)
+  - Menu items and pricing
+  - Sales and revenue data
+  - Customer reviews and ratings
+- **Validation**: Ensure data integrity with schema validation and deduplication.
+
+### 2. Data Storage
+- **Storage Format**: Apache Iceberg for efficient querying and versioning.
+- **Partitioning**: Partition data by restaurant ID and date for optimized performance.
+- **Metadata Management**: Maintain metadata for schema evolution and auditing.
+
+### 3. Reporting and Notifications
+- **Slack Integration**:
+  - Send daily/weekly reports on key metrics (e.g., revenue, top-performing restaurants).
+  - Alert on anomalies (e.g., sudden drop in sales).
+- **Dashboards**: Provide visual insights into restaurant performance using BI tools.
+
+### 4. Automation via LDP
+- **ETL Pipelines**:
+  - Extract data from sources.
+  - Transform data into a unified schema.
+  - Load data into Iceberg tables.
+- **Scheduling**: Automate pipelines using LDP's orchestration capabilities.
+- **Monitoring**: Track pipeline health and data quality metrics.
+
+---
+
+## Technical Requirements
+
+### 1. Data Schema
+- **Restaurant Table**:
+  - `restaurant_id` (string, primary key)
+  - `name` (string)
+  - `location` (string)
+  - `cuisine` (string)
+  - `created_at` (timestamp)
+- **Sales Table**:
+  - `sale_id` (string, primary key)
+  - `restaurant_id` (string, foreign key)
+  - `amount` (decimal)
+  - `sale_date` (date)
+  - `created_at` (timestamp)
+
+### 2. Iceberg Configuration
+- Enable time travel and schema evolution.
+- Optimize for large-scale queries.
+
+### 3. Slack Integration
+- Use Slack Webhooks for notifications.
+- Configure channels for different types of reports.
+
+---
+
+## Success Metrics
+- Reduced manual effort in managing restaurant data.
+- Improved data accuracy and reporting timeliness.
+- Increased adoption of insights by stakeholders.
+
+---
+
+## Timeline
+- **Week 1-2**: Requirements gathering and schema design.
+- **Week 3-4**: Build ETL pipelines and Iceberg integration.
+- **Week 5**: Implement Slack reporting and dashboards.
+- **Week 6**: Testing and deployment.
+
+---
+
+## Stakeholders
+- **Product Owner**: [Your Name]
+- **Engineering Team**: Data Engineers, Backend Developers
+- **Business Team**: Restaurant Managers, Analysts
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