ScyllaDB

1. Ad requests arrive from various ad exchanges.

2. ScyllaDB stores real-time bidding data, including user profiles, ad inventory, and pricing.

3. The bidding engine queries ScyllaDB to determine the optimal bid for each request.

4. Ads are served based on the winning bids.

5. ScyllaDB logs all bidding activity for reporting and analytics.

1. Players achieve scores in the game.

2. The game sends score updates to ScyllaDB.

3. ScyllaDB updates the leaderboard in real-time.

4. Players can view their ranking and compare with others.

5. Leaderboard data is aggregated for daily, weekly, and monthly rankings.

1. IoT sensors generate data continuously.

2. ScyllaDB ingests sensor data through a data pipeline (e.g., Apache Kafka).

3. Data is stored in ScyllaDB with appropriate partitioning for efficient querying.

4. Real-time analytics are performed on the sensor data.

5. Alerts are triggered based on predefined thresholds.

1. User activities are tracked (e.g., page views, purchases).

2. ScyllaDB stores user profiles and item catalogs.

3. The recommendation engine queries ScyllaDB for relevant items.

4. Recommendations are generated based on user preferences and item popularity.

5. Recommendations are displayed to the user in real-time.

1. Transactions are processed in real-time.

2. ScyllaDB stores transaction data and user profiles.

3. The fraud detection engine queries ScyllaDB for suspicious patterns.

4. Fraudulent transactions are flagged.

5. Alerts are sent to fraud analysts for investigation.