System requirements

Functional Requirements

1. User Management
   • Allow users to sign up, log in, and manage profiles.
   • Enable users to save favorite locations and preferences.
2. Business Listings
   • Display information for local establishments (e.g., name, category, address, contact, opening hours).
   • Allow users to search, filter, and sort businesses by location, category, and rating.
   • Support business owners to claim and update their listings.
3. Review System
   • Enable users to write, edit, and delete reviews for businesses.
   • Allow users to rate businesses using a standardized system (e.g., stars).
   • Display aggregated ratings and reviews for each business.
4. Search and Recommendation
   • Provide search functionality with filters (e.g., price range, proximity, popularity).
   • Offer personalized recommendations based on user history and preferences.
5. Photo Upload
   • Allow users to upload photos for businesses as part of their reviews.
   • Display user-uploaded photos on business profiles.
6. Location-Based Services
   • Enable users to search for nearby businesses using geolocation.
   • Show businesses on an interactive map.
7. Analytics and Insights
   • Display analytics to business owners (e.g., views, clicks, and reviews).
   • Provide administrators with system-wide statistics.
8. Notifications
   • Notify users of responses to their reviews or updates on businesses they follow.
   • Inform business owners of new reviews or changes in their listing.
9. Moderation
   • Implement mechanisms for flagging inappropriate reviews or photos.
   • Provide admins tools to review and act on reported content.
10. Offline Access
    • Allow users to save businesses or reviews for offline access.

Non-Functional Requirements

1. Performance
   • Support low-latency responses for search and listing queries (<200ms).
   • Handle thousands of concurrent users during peak traffic.
2. Scalability
   • Ensure horizontal scalability to handle increasing users and reviews.
   • Use sharded databases and distributed search engines.
3. Availability
   • Provide 99.9% uptime with failover and redundancy mechanisms.
4. Security
   • Protect user data with encryption (at rest and in transit).
   • Prevent spam and fraud with rate limiting and CAPTCHA.
5. Usability
   • Offer an intuitive user interface across web and mobile platforms.
   • Support multiple languages and regional preferences.
6. Data Consistency
   • Ensure strong consistency for user-submitted reviews and ratings.
   • Support eventual consistency for search indices and recommendations.
7. Maintainability
   • Use modular architecture for easier updates and feature additions.
   • Provide detailed logging and monitoring for debugging.
8. Privacy
   • Comply with privacy regulations like GDPR.
   • Allow users to manage and delete their reviews or personal data.
9. Extensibility
   • Enable integration with third-party APIs for reservations or promotions.
   • Support APIs for developers to build extensions or analytics.
10. Search Optimization
    • Use a search engine (e.g., Elasticsearch) to handle text and geospatial queries.
    • Provide fast autocomplete and relevance-based ranking.

Capacity estimation

1. User Base

• Monthly Active Users (MAU): ~10 million.
• Daily Active Users (DAU): ~1 million.
• Concurrent Users: Assume 1% of DAU active at the same time → ~10,000 concurrent users.

2. Business Listings

• Total Listings: ~1 million businesses.
• Average Reviews/Business: 100 reviews/business.
• Photos/Business: 50 photos/business.
• Total Data Size:
  • Reviews: 1 million * 100 reviews * 1 KB/review ≈ 100 GB.
  • Photos: 1 million * 50 photos * 500 KB/photo ≈ 25 TB.

3. Reviews

• Daily Reviews: ~1 million users, 5 reviews/day → ~5 million reviews/day.
• Storage Requirements:
  • 5 million reviews/day * 1 KB/review = ~5 GB/day → ~1.8 TB/year.

4. Photos

• Daily Photo Uploads: ~2 million photos/day.
• Storage Requirements:
  • 2 million photos/day * 500 KB/photo = ~1 TB/day → ~365 TB/year.

5. Search Queries

• Search Volume: ~10 searches/user/day.
• Total Queries/Day: 1 million users * 10 searches = 10 million queries/day.
• Peak Queries/Second: ~10,000 QPS during peak hours.

6. Notifications

• Notifications:
  • ~5 million daily reviews generate ~5 million notifications.
  • Assume 10% users enable push notifications = ~500,000 notifications/day.

7. Database Size

• User Data:
  • 10 million users * 10 KB/user = 100 GB.
• Business Data:
  • 1 million businesses * 50 KB/business = 50 GB.
• Search Index:
  • 1 million businesses + 100 million reviews ≈ ~10 GB.

8. Bandwidth

• Review and Photo Uploads:
  • ~2 TB/day for photos + ~5 GB/day for reviews → ~2.005 TB/day.
• Search Requests:
  • 10 million queries/day * ~5 KB/query = ~50 GB/day.
• Notifications:
  • 500,000 notifications * 1 KB = ~500 MB/day.

9. Compute Requirements

• API Gateway:
  • Handle ~10,000 concurrent requests.
• Search Service:
  • Serve ~10,000 QPS with low latency (<200 ms).
• Photo Service:
  • Upload/download ~2 TB/day.

Summary

• Storage:
  • Total: ~500 TB/year (including photos, reviews, and metadata).
• Traffic:
  • ~2 TB/day for photos + 100 GB/day for other activities.
• Compute:
  • ~10,000 concurrent users, 10,000 QPS for search, scalable to higher peaks.

API design

1. User Management APIs

1. POST /user/signup
   • Registers a new user with email, password, and optional profile details.
2. POST /user/login
   • Authenticates a user and returns an access token.
3. GET /user/profile
   • Fetches the user’s profile details.
4. PUT /user/profile
   • Updates the user’s profile information.
5. DELETE /user/account
   • Deletes the user’s account.

2. Business Listing APIs

1. GET /business/search
   • Search businesses with filters (e.g., location, category, rating).
2. GET /business/{id}
   • Fetch details for a specific business.
3. POST /business/claim
   • Allows business owners to claim their business listing.
4. POST /business/create
   • Adds a new business listing (admin or verified owner only).
5. PUT /business/{id}
   • Updates business details (admin or verified owner only).

3. Review APIs

1. POST /review
   • Submit a new review for a business.
2. GET /business/{id}/reviews
   • Fetch reviews for a specific business.
3. PUT /review/{id}
   • Edit an existing review.
4. DELETE /review/{id}
   • Remove a review.

4. Photo APIs

1. POST /photo
   • Upload a photo for a specific business.
2. GET /business/{id}/photos
   • Retrieve all photos for a business.
3. DELETE /photo/{id}
   • Remove a photo (admin or uploader only).

5. Search and Recommendation APIs

1. GET /search/autocomplete
   • Fetch autocomplete suggestions for search queries.
2. GET /search/popular
   • Fetch popular searches or trending locations.
3. GET /recommendations
   • Fetch personalized recommendations for the user.

6. Notification APIs

1. GET /notifications
   • Retrieve user notifications (e.g., new reviews, responses).
2. POST /notifications/mark-read
   • Mark a notification as read.
3. POST /notifications/flag
   • Report inappropriate notifications or responses.

7. Moderation APIs

1. POST /moderation/flag
   • Flag inappropriate reviews or photos.
2. GET /moderation/queue
   • Fetch flagged content for admin review.
3. PUT /moderation/{id}
   • Approve or reject flagged content.

8. Analytics APIs

1. GET /business/{id}/analytics
   • Provide analytics for a specific business (views, clicks, reviews).
2. GET /admin/analytics
   • Platform-wide statistics for administrators.

9. Miscellaneous APIs

1. GET /location/nearby
   • Fetch nearby businesses using geolocation.
2. GET /categories
   • Retrieve a list of supported business categories.
3. GET /offline/download
   • Download offline data for a region.

Database design

1. User Database

• Attributes Stored and Keys:
  • users table:
    • Attributes: user_id (PK), name, email (unique), password_hash, preferences, created_at.
    • Primary Key (PK): user_id.
    • No Foreign Keys (FK).
• Purpose:
  • Manage user profiles, authentication details, and preferences.
• Tech Used:
  • PostgreSQL
• Reason:
  • Relational model ensures strong consistency and supports complex queries for user data.

2. Business Database

• Attributes Stored and Keys:
  • businesses table:
    • Attributes: business_id (PK), name, category, location, contact_info, claimed_by (FK).
    • Primary Key (PK): business_id.
    • Foreign Key (FK): claimed_by references users.user_id.
• Purpose:
  • Store business listings and metadata for search and filtering.
• Tech Used:
  • MySQL
• Reason:
  • Optimized for structured data with frequent queries, sorting, and filtering.

3. Review and Rating Database

• Attributes Stored and Keys:
  • reviews table:
    • Attributes: review_id (PK), business_id (FK), user_id (FK), rating, review_text, created_at, updated_at.
    • Primary Key (PK): review_id.
    • Foreign Keys (FK):
      • business_id references businesses.business_id.
      • user_id references users.user_id.
• Purpose:
  • Manage user-submitted reviews, ratings, and associated metadata.
• Tech Used:
  • MongoDB
• Reason:
  • Flexible schema for dynamic and diverse review content.

4. Photo Storage

• Attributes Stored and Keys:
  • photos table:
    • Attributes: photo_id (PK), business_id (FK), uploader_id (FK), photo_url, created_at.
    • Primary Key (PK): photo_id.
    • Foreign Keys (FK):
      • business_id references businesses.business_id.
      • uploader_id references users.user_id.
• Purpose:
  • Store metadata of user-uploaded photos for businesses.
• Tech Used:
  • AWS S3 (photo storage) and PostgreSQL (metadata).
• Reason:
  • Object storage for scalable photo handling, relational DB for efficient metadata queries.

5. Search Index Database

• Attributes Stored and Keys:
  • search_index:
    • Attributes: business_id, name, category, location, keywords.
    • No Primary or Foreign Keys as it is an index.
• Purpose:
  • Support full-text and geospatial search for businesses and reviews.
• Tech Used:
  • Elasticsearch
• Reason:
  • Optimized for fast, relevance-based search and proximity queries.

6. Analytics Database

• Attributes Stored and Keys:
  • business_analytics table:
    • Attributes: business_id (PK), views, clicks, reviews_count, timestamp.
    • Primary Key (PK): business_id.
  • platform_analytics table:
    • Attributes: metric_id (PK), active_users, reviews_posted, photos_uploaded, timestamp.
    • Primary Key (PK): metric_id.
• Purpose:
  • Track business performance and system-wide metrics for analysis.
• Tech Used:
  • Amazon Redshift or Google BigQuery
• Reason:
  • Optimized for OLAP queries and large-scale analytics.

High-level design

1. User Interface (UI)

• Overview:
  • Web and mobile applications that users interact with to explore businesses, write reviews, and view recommendations.
• Features:
  • User-friendly search, filters, and business browsing.
  • Features for adding reviews, uploading photos, and viewing analytics (for business owners).

2. API Gateway

• Overview:
  • Acts as a single entry point for all client requests, routing them to the appropriate services.
• Features:
  • Authentication, rate limiting, request validation, and routing.
  • Centralized monitoring and logging for all requests.

3. User Management Service

• Overview:
  • Handles user registration, login, profile management, and authentication.
• Features:
  • Password hashing, token management, and session handling.

4. Business Management Service

• Overview:
  • Manages business listings, categories, and ownership claims.
• Features:
  • CRUD operations for businesses, owner verification, and updates.

5. Review Management Service

• Overview:
  • Handles user reviews and ratings for businesses.
• Features:
  • CRUD operations for reviews, flagging inappropriate content, and aggregating ratings.

6. Photo Management Service

• Overview:
  • Manages uploading, storing, and retrieving photos associated with businesses.
• Features:
  • Processes metadata and integrates with a scalable storage service (e.g., AWS S3).

7. Search Service

• Overview:
  • Enables users to search for businesses, reviews, and categories.
• Features:
  • Full-text search, geospatial queries, and autocomplete functionality.

8. Recommendation Service

• Overview:
  • Provides personalized business and review recommendations for users.
• Features:
  • Uses machine learning models to analyze user behavior and preferences.

9. Notification Service

• Overview:
  • Sends updates and alerts to users and business owners about reviews, responses, and flagged content.
• Features:
  • Push notifications, emails, and real-time alerts.

10. Moderation Service

• Overview:
  • Handles flagged reviews and photos for inappropriate content.
• Features:
  • Enables admins to review and act on flagged content.

11. Analytics and Reporting Service

• Overview:
  • Aggregates user interaction data and generates insights for business owners and admins.
• Features:
  • Tracks views, clicks, and reviews; generates platform-wide statistics.

12. Search Index Database

• Overview:
  • Stores indexed data for fast text and geospatial searches.
• Features:
  • Optimized for proximity-based and relevance-based queries.

13. Offline Access Service

• Overview:
  • Allows users to download and access business information offline.
• Features:
  • Provides cached business details, reviews, and photos.

14. Data Pipeline

• Overview:
  • Processes and aggregates data for analytics and machine learning.
• Features:
  • Handles real-time data ingestion and batch processing.

15. Load Balancer

• Overview:
  • Distributes incoming traffic across servers to ensure high availability and performance.
• Features:
  • Fault tolerance and traffic management during peak loads.

16. Monitoring and Logging Service

• Overview:
  • Tracks system performance and logs user activities for debugging and insights.
• Features:
  • Alerts for system health issues and detailed activity logs.

Request flows

1. User Registration

1. User Interface:
   • User inputs name, email, and password on the registration page.
2. API Gateway:
   • Routes the request to the User Management Service.
3. User Management Service:
   • Validates input and checks for duplicate email.
   • Hashes the password and creates a new user entry in the User Database.
4. Database Interaction:
   • Inserts user details into the User Database.
5. Response:
   • Returns a success message to the user.

2. Business Search

1. User Interface:
   • User enters search terms and filters on the search bar.
2. API Gateway:
   • Routes the request to the Search Service.
3. Search Service:
   • Queries the Search Index Database for matching businesses.
   • Applies filters like category, location, and rating.
4. Database Interaction:
   • Fetches relevant business data from the Business Database.
5. Response:
   • Sends the list of matching businesses back to the user.

3. Submit a Review

1. User Interface:
   • User submits a review for a specific business.
2. API Gateway:
   • Routes the request to the Review Management Service.
3. Review Management Service:
   • Validates the review content and user authentication.
   • Saves the review in the Review Database.
4. Database Interaction:
   • Updates the Search Index with the new review.
   • Updates aggregated ratings for the business in the Business Database.
5. Notification Service:
   • Sends a notification to the business owner about the new review.
6. Response:
   • Confirms the review submission to the user.

4. Upload a Photo

1. User Interface:
   • User selects and uploads a photo for a business.
2. API Gateway:
   • Routes the request to the Photo Management Service.
3. Photo Management Service:
   • Processes and stores the photo in Object Storage (e.g., AWS S3).
   • Saves metadata (e.g., uploader_id, business_id, URL) in the Photo Database.
4. Database Interaction:
   • Links the photo to the corresponding business in the database.
5. Response:
   • Confirms the upload and displays the photo to the user.

5. Business Claim by Owner

1. User Interface:
   • Business owner submits a claim request for their business.
2. API Gateway:
   • Routes the request to the Business Management Service.
3. Business Management Service:
   • Validates the owner’s identity.
   • Updates the ownership status in the Business Database.
4. Notification Service:
   • Notifies the admin team for verification, if required.
5. Response:
   • Confirms the claim request submission to the owner.

6. Notification Retrieval

1. User Interface:
   • User checks notifications via the app or website.
2. API Gateway:
   • Routes the request to the Notification Service.
3. Notification Service:
   • Fetches unread notifications from the Notification Database.
4. Response:
   • Displays notifications to the user.

7. Flagging Inappropriate Content

1. User Interface:
   • User flags a review or photo as inappropriate.
2. API Gateway:
   • Routes the request to the Moderation Service.
3. Moderation Service:
   • Saves the flagged content in the Moderation Queue Database.
4. Notification:
   • Notifies the admin team for review.
5. Response:
   • Confirms the flag submission to the user.

8. Analytics Retrieval

1. User Interface:
   • Business owner or admin views analytics.
2. API Gateway:
   • Routes the request to the Analytics Service.
3. Analytics Service:
   • Fetches data from the Analytics Database.
4. Response:
   • Displays detailed insights to the user.

9. Geolocation-Based Search

1. User Interface:
   • User opts to search nearby businesses.
2. API Gateway:
   • Routes the request to the Search Service.
3. Search Service:
   • Queries the Search Index for businesses near the user’s geolocation.
4. Database Interaction:
   • Retrieves business details from the Business Database.
5. Response:
   • Displays a map with nearby businesses.

Detailed component design

1. Search Service

1. End-to-End Working

• Accepts user queries via the API Gateway.
• Parses query parameters (keywords, filters like location or category).
• Queries the Search Index Database (Elasticsearch) for relevant matches using full-text and geospatial search.
• Applies filters (e.g., category, proximity) and ranks results based on relevance.
• Returns the results to the user.

2. Data Structures and Algorithms

• Inverted Index: Maps keywords to document IDs for efficient text search.
• R-Trees/Geohashing: For spatial indexing to handle proximity-based queries.
• TF-IDF or BM25: Scoring algorithms to rank search results by relevance.
• Trie Structure: For efficient autocomplete suggestions.

3. Handling Peak Traffic (Scaling)

• Sharded Index: Divides search data across nodes by region or category to distribute load.
• Caching: Frequently searched queries are cached in Redis for faster responses.
• Horizontal Scaling: Additional Elasticsearch nodes are deployed to handle increased query volume.

4. Edge Cases and Handling

• Case 1: Typo in Search Query:
  • Handling: Implements fuzzy search with Levenshtein distance to suggest corrections.
• Case 2: No Results Found:
  • Handling: Suggests popular businesses or categories as fallback.
• Case 3: High Query Volume:
  • Handling: Rate limits users during extreme surges and prioritizes logged-in users.

2. Review Management Service

1. End-to-End Working

• Accepts review submissions via the API Gateway.
• Validates review content and user authentication.
• Stores reviews in the Review Database (MongoDB).
• Updates the Search Index with the review for improved business visibility.
• Sends notifications to business owners and relevant users.

2. Data Structures and Algorithms

• Document Store: MongoDB’s flexible schema for storing diverse review content.
• TTL Indexes: Automatically deletes flagged or temporary reviews after a set period.
• Aggregation Pipelines: Efficiently calculates business ratings and review counts.

3. Handling Peak Traffic (Scaling)

• Partitioned Collections: Shards reviews by business ID to distribute writes.
• Write Queues: Temporarily queues reviews for delayed processing during heavy loads.
• Horizontal Scaling: Adds MongoDB nodes to scale read and write capacity.

4. Edge Cases and Handling

• Case 1: Duplicate Reviews:
  • Handling: Enforces uniqueness by combining user ID and business ID.
• Case 2: Profanity or Inappropriate Content:
  • Handling: Filters reviews with text-analysis models and flags inappropriate ones.
• Case 3: Review Flooding:
  • Handling: Rate limits review submissions to prevent spam.

3. Photo Management Service

1. End-to-End Working

• Processes photo uploads from users.
• Stores photos in Object Storage (AWS S3/Google Cloud Storage).
• Saves metadata (e.g., uploader ID, business ID) in the Photo Database.
• Links photos to the corresponding business profile.

2. Data Structures and Algorithms

• Object Metadata: Stored in relational format for efficient indexing and retrieval.
• Content Delivery Networks (CDN): Ensures fast photo delivery to end-users.
• Chunked Uploads: Splits large files into smaller parts for efficient uploads.

3. Handling Peak Traffic (Scaling)

• CDN Integration: Caches frequently accessed photos at the edge for low-latency delivery.
• Object Storage Partitioning: Distributes photos across multiple storage buckets.
• Upload Queueing: Handles burst traffic by queueing uploads during surges.

4. Edge Cases and Handling

• Case 1: Large File Uploads:
  • Handling: Implements chunked uploads and retries for network failures.
• Case 2: Duplicate Photo Uploads:
  • Handling: Uses hash-based deduplication to avoid redundant storage.
• Case 3: Photo Abuse (e.g., offensive content):
  • Handling: Integrates AI-based moderation tools to flag inappropriate images.

4. Notification Service

1. End-to-End Working

• Receives events like new reviews or flagged content.
• Queues notifications in the Notification Queue.
• Delivers notifications to users via push notifications or email.
• Updates notification statuses in the Notification Database.

2. Data Structures and Algorithms

• Priority Queues: Ensures high-priority notifications (e.g., flagged content) are delivered first.
• Redis Pub/Sub: Real-time delivery of notifications.
• Event-Driven Architecture: Processes notifications asynchronously for scalability.

3. Handling Peak Traffic (Scaling)

• Message Queues: Uses RabbitMQ/Kafka to buffer notifications during spikes.
• Parallel Processing: Processes notification batches with worker nodes.
• Fallback Mechanisms: Switches to delayed email notifications during service outages.

4. Edge Cases and Handling

• Case 1: Undelivered Notifications:
  • Handling: Implements retries with exponential backoff.
• Case 2: Notification Flooding:
  • Handling: Groups multiple notifications into a single digest for the user.
• Case 3: Failed Delivery Channels:
  • Handling: Switches between push, SMS, or email channels based on availability.

5. Business Management Service

1. End-to-End Working

• Handles CRUD operations for business listings.
• Allows business owners to claim listings after verification.
• Updates business information in the Business Database and Search Index.

2. Data Structures and Algorithms

• Relational Tables: Efficiently store structured data like business details.
• Verification Workflow: Processes claims with step-by-step verification checks.

3. Handling Peak Traffic (Scaling)

• Read Replicas: Serves high read traffic from replicas to offload the primary database.
• Async Index Updates: Processes search index updates in batches to reduce load.
• Horizontal Scaling: Scales business management microservices to handle concurrent updates.

4. Edge Cases and Handling

• Case 1: Duplicate Business Listings:
  • Handling: Merges listings with matching metadata.
• Case 2: Fake Business Claims:
  • Handling: Requires ownership proofs (e.g., utility bills or government-issued IDs).
• Case 3: Large Updates (e.g., bulk edits):
  • Handling: Processes updates in smaller batches to avoid database overload.

Trade offs/Tech choices

MongoDB for Reviews:

• Trade-Off: Flexibility for diverse review structures but lacks strong consistency.
• Reason: Reviews are dynamic and vary in content; MongoDB's schema flexibility handles this efficiently.

Elasticsearch for Search:

• Trade-Off: High resource usage but provides fast and accurate full-text and geospatial search.
• Reason: Optimized for real-time search and filtering across large datasets.

AWS S3 for Photo Storage:

• Trade-Off: Higher latency compared to local storage but highly scalable and cost-effective for large volumes of photos.
• Reason: Ensures durability and easy integration with CDNs for global delivery.

PostgreSQL for Users:

• Trade-Off: More complex scaling compared to NoSQL, but provides strong consistency.
• Reason: Relational structure fits well with user authentication and profile management.

Event-Driven Notifications:

• Trade-Off: Adds complexity with message queues but decouples services for scalability and fault tolerance.
• Reason: Handles high notification volumes asynchronously for better performance.

Failure scenarios/bottlenecks

Search Index Overload:

• Issue: High query volume slows down Elasticsearch.
• Mitigation: Implement caching for frequent queries and shard the index.

Photo Storage Latency:

• Issue: Slow uploads or retrievals during traffic surges.
• Mitigation: Use CDNs and chunked uploads for scalability.

Database Overload:

• Issue: High read/write traffic on relational databases.
• Mitigation: Use read replicas, query optimization, and caching.

Notification Delays:

• Issue: Message queues back up during peaks.
• Mitigation: Scale workers and prioritize notifications.

Review Spam:

• Issue: Excessive or inappropriate reviews overwhelm the system.
• Mitigation: Use rate limiting and automated moderation tools.

Service Downtime:

• Issue: API Gateway or key services become unavailable.
• Mitigation: Use load balancers, failover servers, and health checks.

Flagged Content Abuse:

• Issue: Users maliciously flag valid reviews or photos.
• Mitigation: Implement user reputation scoring and manual moderation.

Scaling Bottlenecks:

• Issue: Inefficient horizontal scaling under sudden spikes.
• Mitigation: Pre-scale resources during predicted high traffic.

Future improvements

Enhanced Search Optimization:

• Improvement: Use ML-based ranking for personalized search results.
• Mitigation: Reduces search index bottlenecks with smarter query handling.

AI-Powered Moderation:

• Improvement: Deploy AI models for review and photo moderation.
• Mitigation: Minimizes flagged content abuse and reduces manual workload.

Real-Time Analytics:

• Improvement: Introduce real-time pipelines for instant business insights.
• Mitigation: Handles database overload by offloading analytics.

Auto-Scaling for Peak Traffic:

• Improvement: Implement predictive auto-scaling for all services.
• Mitigation: Prepares the system for sudden traffic spikes.

Enhanced Caching Strategy:

• Improvement: Expand caching for popular searches, reviews, and photos.
• Mitigation: Alleviates search index and database load during peaks.

Improved Notification Handling:

• Improvement: Group similar notifications and introduce digest alerts.
• Mitigation: Prevents queue backlogs and reduces delivery delays.

Offline Capabilities:

• Improvement: Allow users to download more robust offline data.
• Mitigation: Reduces reliance on real-time data during outages.