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Kafka Stream Processing Pipeline

Real-time data processing pipeline using Apache Kafka for high-throughput, low-latency streaming applications.

High Complexity

Technologies & Stack

Apache KafkaKafka StreamsJavaDockerZookeeper

Pipeline Flow

1

Data Ingestion

Ingest data streams from various sources into Kafka topics

Kafka ConnectKafka Producers
2

Stream Processing

Process data streams in real-time using Kafka Streams

Kafka StreamsJava
3

Data Output

Send processed results to downstream systems

Kafka ConsumersDatabasesAPIs

Use Cases

Real-time analytics
Fraud detection
Live dashboards
IoT data processing
Real-time recommendations

Advantages

Real-time processing capabilities
High throughput and scalability
Fault tolerance and reliability
Rich ecosystem and community support

Challenges

Higher complexity and operational overhead
More expensive than batch processing
Requires specialized expertise
Debugging can be challenging

When to Use This Architecture

Real-time data requirements
High-throughput streaming applications
Event-driven architectures
Real-time analytics and monitoring

Alternative Solutions

Apache FlinkApache StormAWS KinesisGoogle Cloud Dataflow

Performance Metrics

Latency
Very Low (milliseconds to seconds)
Throughput
Very High (millions of events per second)
Scalability
Excellent
Reliability
High
Cost
Medium to High

Key Trade-offs

Latency

Very low latency for real-time processing

Complexity

Higher complexity compared to batch processing

Scalability

Excellent horizontal scalability

Architecture Category

Real-time Processing

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