Kafka Use Cases

Kafka, developed at LinkedIn, is widely used for various data processing and handling tasks. Below are its key use cases:

1. Activity Tracking

• Primary Use: Originally used for tracking user activities on websites.
• Data Types: Captures both passive interactions (e.g., page views) and active user actions (e.g., profile updates).
• Back-End Processing: Messages are published to topics and then consumed by backend applications for diverse purposes like report generation, machine learning, and updating search results.

2. Messaging

• Notifications: Ideal for sending notifications like emails.
• Simplified Message Production: Allows applications to focus on message content without worrying about formatting or delivery.
• Centralized Processing: A single application can handle all messages, ensuring consistent formatting and application of user preferences, which avoids duplicative efforts.

3. Metrics and Logging

• Versatile Data Collection: Suitable for collecting application/system metrics and logs.
• Efficient Data Utilization: Data can be used for monitoring, alerting, and long-term analysis (like in Hadoop).
• Flexibility in System Changes: Simplifies transitions to new systems without needing to alter frontend applications.

4. Commit Log

• Database Change Broadcasting: Kafka’s commit log structure is great for publishing database changes.
• Real-Time Updates and Replication: Useful for live tracking of updates, replicating changes to remote systems, or consolidating data from multiple sources.
• Data Reliability: Features like durable retention and log-compacted topics enhance data reliability.

5. Stream Processing

• Real-Time Data Processing: Contrasts with batch processing in Hadoop by offering real-time processing.
• Flexible Application Use: Enables small applications to perform tasks like metrics counting, message partitioning, and data transformation.