Build high-throughput, low-latency streaming data pipelines using Apache Kafka, Pulsar, and modern stream processing frameworks. Process millions of events per second with real-time analytics and event-driven architectures.
Explore Streaming SolutionsDistributed streaming platform for building real-time data pipelines and streaming applications
Key Features:
Common Use Cases:
Throughput:
10M+ msgs/sec
Latency:
< 1ms
Next-generation distributed pub-sub messaging system with multi-tenancy and geo-replication
Key Features:
Common Use Cases:
Throughput:
3M+ msgs/sec
Latency:
< 5ms
Stream processing framework for stateful computations over unbounded and bounded data streams
Key Features:
Common Use Cases:
Throughput:
1M+ events/sec
Latency:
< 10ms
Scalable, high-throughput, fault-tolerant streaming processing of live data streams
Key Features:
Common Use Cases:
Throughput:
500K+ records/sec
Latency:
< 100ms
Hybrid approach combining batch and real-time stream processing for comprehensive data analysis
Architecture Layers:
Pros:
Cons:
Best For:
Large-scale analytics with historical data
Stream-only architecture that processes all data as streams, simplifying the data pipeline
Architecture Layers:
Pros:
Cons:
Best For:
Real-time applications with continuous data
Microservices architecture where services communicate through events via message brokers
Architecture Layers:
Pros:
Cons:
Best For:
Microservices and reactive systems
Command Query Responsibility Segregation with events as the primary source of truth
Architecture Layers:
Pros:
Cons:
Best For:
Financial and audit-critical systems
Processing infinite streams by grouping events into finite windows
Types:
Example:
Calculate hourly website traffic metrics
Handling late-arriving events in event-time processing
Types:
Example:
Process sensor data with network delays
Maintaining stateful information across stream processing operations
Types:
Example:
Track user session state across events
Managing flow control when consumers cannot keep up with producers
Types:
Example:
Handle traffic spikes in e-commerce systems
Process user behavior events to provide personalized product recommendations in milliseconds
Key Challenges:
Technologies Used:
Solution: Kafka + Flink + Redis pipeline with ML models
Business Impact
40% increase in conversion rate, sub-second recommendations
Real-time analysis of transaction patterns to detect and prevent fraudulent activities
Key Challenges:
Technologies Used:
Solution: Event sourcing with complex event processing
Business Impact
95% fraud detection accuracy, 50ms detection time
Process sensor data streams to predict equipment failures before they occur
Key Challenges:
Technologies Used:
Solution: Time-series pipeline with anomaly detection
Business Impact
30% reduction in downtime, $2M annual savings
Adapt streaming content and ads in real-time based on viewer behavior and preferences
Key Challenges:
Technologies Used:
Solution: Event-driven content delivery pipeline
Business Impact
25% increase in engagement, personalized content delivery
Optimization Techniques:
Performance Metrics:
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Optimization Techniques:
Performance Metrics:
Recommended Tools:
Optimization Techniques:
Performance Metrics:
Recommended Tools:
Optimization Techniques:
Performance Metrics:
Recommended Tools:
Let our streaming experts design and implement high-performance data pipelines that can handle millions of events per second with sub-millisecond latency.
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