Why Kafka?

What is Apache Kafka?

• Distributed streaming platform designed for high-throughput message processing
• Excels at handling low-latency, fault-tolerant data streams
• Go-to solution for processing large volumes of real-time data
• Seamlessly manages real-time data feeds across distributed systems

Common Use Cases

Real-time Analytics

• Process data as it flows in for immediate insights
• Enable faster decision-making with live data
• Essential for monitoring user interactions
• Track financial transactions in real-time

Event Sourcing

• Record every state change as an event
• Reconstruct entire application state from event history
• Critical for financial services requiring auditability
• Provides complete traceability of system changes

Log Aggregation

• Centralize logs from various services
• Simplify monitoring and analysis
• Common setup for microservices architectures
• Each service generates logs collected by Kafka

Metrics Collection

• Collect performance metrics across distributed systems
• Process metrics in real-time
• Provide unified view of system health
• Monitor application performance centrally

Data Integration

• Serves as backbone for connecting different data sources
• Ensures data consistency across applications
• Integrates heterogeneous systems seamlessly
• Maintains data flow between various platforms

Real-World Examples

Uber

• Processes millions of trip events per second
• Powers dynamic pricing in real-time
• Matches riders with drivers efficiently
• Analyzes trip data for enhanced user experience

LinkedIn

• Birthplace of Apache Kafka
• Extensive message processing infrastructure
• Powers activity feeds for millions of users
• Enables real-time analytics across platform

Netflix

• Handles customer interactions at scale
• Tracks viewing patterns for recommendations
• Powers personalized content delivery
• Ensures seamless user experience

Why Choose Kafka?

Scalability

• Handles millions of events per second
• Scales horizontally across multiple nodes
• Supports growing data volumes effortlessly

Resilience

• Fault-tolerant by design
• Maintains data availability during failures
• Replicates data across cluster nodes

Real-time Processing

• Processes data with minimal latency
• Keeps data fresh and up-to-date
• Ideal for time-sensitive applications

Reliability

• High performance under heavy load
• Consistent delivery guarantees
• Perfect for modern data-driven applications