logo

Streaming Data Pipeline Solutions

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 Solutions

Core Streaming Technologies

Apache Kafka

Distributed streaming platform for building real-time data pipelines and streaming applications

Key Features:

High throughput
Low latency
Fault tolerant
Scalable
Persistent storage

Common Use Cases:

  • Event sourcing
  • Log aggregation
  • Stream processing
  • Microservices communication

Throughput:

10M+ msgs/sec

Latency:

< 1ms

Apache Pulsar

Next-generation distributed pub-sub messaging system with multi-tenancy and geo-replication

Key Features:

Multi-tenancy
Geo-replication
Tiered storage
Functions
Schema registry

Common Use Cases:

  • IoT data ingestion
  • Financial messaging
  • Log streaming
  • Real-time analytics

Throughput:

3M+ msgs/sec

Latency:

< 5ms

Apache Flink

Stream processing framework for stateful computations over unbounded and bounded data streams

Key Features:

Exactly-once processing
Low latency
High throughput
Event time processing
SQL support

Common Use Cases:

  • Real-time analytics
  • Complex event processing
  • Fraud detection
  • Monitoring

Throughput:

1M+ events/sec

Latency:

< 10ms

Apache Spark Streaming

Scalable, high-throughput, fault-tolerant streaming processing of live data streams

Key Features:

Micro-batch processing
Lambda architecture
ML integration
Structured streaming
Checkpointing

Common Use Cases:

  • Batch + stream processing
  • ETL pipelines
  • Machine learning
  • Data warehousing

Throughput:

500K+ records/sec

Latency:

< 100ms

Streaming Pipeline Architectures

Lambda Architecture

Hybrid approach combining batch and real-time stream processing for comprehensive data analysis

Architecture Layers:

Batch Layer
Speed Layer
Serving Layer
Data Storage

Pros:

  • Fault tolerant
  • Comprehensive processing
  • Flexible queries

Cons:

  • Complex maintenance
  • Data consistency
  • Duplicate logic

Best For:

Large-scale analytics with historical data

Kappa Architecture

Stream-only architecture that processes all data as streams, simplifying the data pipeline

Architecture Layers:

Stream Processing
Serving Layer
Data Storage
Reprocessing

Pros:

  • Simple architecture
  • Single codebase
  • Real-time processing

Cons:

  • Reprocessing complexity
  • Stream storage requirements
  • Limited batch capabilities

Best For:

Real-time applications with continuous data

Event-Driven Architecture

Microservices architecture where services communicate through events via message brokers

Architecture Layers:

Event Producers
Message Broker
Event Consumers
Event Store

Pros:

  • Loose coupling
  • Scalability
  • Resilience

Cons:

  • Event ordering
  • Eventual consistency
  • Debugging complexity

Best For:

Microservices and reactive systems

CQRS with Event Sourcing

Command Query Responsibility Segregation with events as the primary source of truth

Architecture Layers:

Command Model
Event Store
Query Model
Projections

Pros:

  • Complete audit trail
  • Temporal queries
  • Scalable reads

Cons:

  • Added complexity
  • Eventual consistency
  • Storage requirements

Best For:

Financial and audit-critical systems

Stream Processing Patterns

Windowing

Processing infinite streams by grouping events into finite windows

Types:

  • Tumbling Windows
  • Sliding Windows
  • Session Windows
  • Global Windows

Example:

Calculate hourly website traffic metrics

Watermarks

Handling late-arriving events in event-time processing

Types:

  • Punctuated Watermarks
  • Periodic Watermarks
  • Custom Watermarks

Example:

Process sensor data with network delays

State Management

Maintaining stateful information across stream processing operations

Types:

  • Keyed State
  • Operator State
  • Broadcast State
  • Checkpointed State

Example:

Track user session state across events

Backpressure Handling

Managing flow control when consumers cannot keep up with producers

Types:

  • Buffering
  • Dropping
  • Blocking
  • Load Shedding

Example:

Handle traffic spikes in e-commerce systems

Real-Time Use Cases & Success Stories

Real-time Recommendation Engine

E-commerce

Process user behavior events to provide personalized product recommendations in milliseconds

Key Challenges:

High throughput
Low latency
Personalization accuracy

Technologies Used:

Apache Kafka
Apache Flink
Redis
TensorFlow

Solution: Kafka + Flink + Redis pipeline with ML models

Business Impact

40% increase in conversion rate, sub-second recommendations

Fraud Detection System

Financial Services

Real-time analysis of transaction patterns to detect and prevent fraudulent activities

Key Challenges:

Complex pattern matching
False positive reduction
Regulatory compliance

Technologies Used:

Apache Kafka
Apache Flink
Elasticsearch
PostgreSQL

Solution: Event sourcing with complex event processing

Business Impact

95% fraud detection accuracy, 50ms detection time

Predictive Maintenance

IoT & Manufacturing

Process sensor data streams to predict equipment failures before they occur

Key Challenges:

Sensor data volume
Time series analysis
Alert prioritization

Technologies Used:

Apache Pulsar
InfluxDB
Grafana
Python ML

Solution: Time-series pipeline with anomaly detection

Business Impact

30% reduction in downtime, $2M annual savings

Live Content Personalization

Media & Entertainment

Adapt streaming content and ads in real-time based on viewer behavior and preferences

Key Challenges:

Content delivery
Real-time segmentation
A/B testing

Technologies Used:

Apache Kafka
Apache Spark
CDN
MongoDB

Solution: Event-driven content delivery pipeline

Business Impact

25% increase in engagement, personalized content delivery

Performance Optimization Strategies

Kafka Optimization

Optimization Techniques:

  • Producer batching and compression
  • Consumer group scaling
  • Partition strategy optimization
  • Replication factor tuning
  • Log compaction configuration

Performance Metrics:

  • Throughput: 10M+ msgs/sec
  • Latency: < 1ms p99
  • Disk usage: 70% reduction

Recommended Tools:

Kafka Manager
JMX monitoring
Kafka tools

Stream Processing

Optimization Techniques:

  • Parallelism optimization
  • State backend tuning
  • Checkpointing strategy
  • Memory management
  • Operator chaining

Performance Metrics:

  • Processing rate: 1M+ events/sec
  • State size: 100GB+
  • Recovery time: < 30s

Recommended Tools:

Flink Web UI
Metrics reporters
Profiling tools

Storage Optimization

Optimization Techniques:

  • Tiered storage configuration
  • Compression algorithms
  • Retention policies
  • Indexing strategies
  • Partitioning schemes

Performance Metrics:

  • Storage cost: 60% reduction
  • Query performance: 10x faster
  • Retention: 1 year+

Recommended Tools:

Storage analytics
Query optimizers
Compression tools

Network & Infrastructure

Optimization Techniques:

  • Network bandwidth optimization
  • Load balancing configuration
  • Resource allocation tuning
  • Geographic distribution
  • Edge processing

Performance Metrics:

  • Network usage: 40% reduction
  • Cross-region latency: < 50ms
  • Availability: 99.99%

Recommended Tools:

Network monitoring
Load balancers
CDN services

Ready to Build Real-Time Data Pipelines?

Let our streaming experts design and implement high-performance data pipelines that can handle millions of events per second with sub-millisecond latency.

location marker

Canada

6410 Longspur RD, Mississauga

ON, L5N6E3, Canada

location marker

UAE

P.O. Box 215851

Dubai U.A.E

location marker

Holland

Carry van Bruggenhof 105

2548MT, 's-Gravenhage

phone icon

Sales: +1 514 577 8599

phone icon

Admin: +1 514 794 7041

mail icon

info@opensource.consulting

LET's

MEET

We'd like to get to know you. Together we'll look how we can help you in the best way possible.

Loading...
company logo

Unlocking the power of open source technologies for modern enterprises. Expert consulting, technical implementation, and managed services.

mail icon

info@opensource.consulting

facebook icontwitter iconlinkedin icon

Global Offices

🇳🇱 Netherlands • 🇨🇦 Canada • 🇦🇪 Dubai

Company

Careers

Partners

Press & Media

Services

24/7 Support

Enterprise Solutions

© 2025 OpenSource Consulting. All rights reserved.