Stream processing architectures enable real-time analysis of continuous data flows. These systems ingest, process, and output data instantly, allowing organizations to gain immediate insights and respond swiftly to changing conditions.

Key components include data sources, message brokers, processing engines, and data sinks. This architecture differs from batch processing by handling unbounded data streams, providing low-latency results, and maintaining stateful computations for real-time applications.

Stream Processing Architectures

Components of stream processing architecture

• Data sources continuously generate data in real-time
  • IoT devices, sensors, clickstreams, log files, social media feeds (Twitter, Facebook)
• Message broker or queue ingests and buffers incoming data streams
  • Acts as a buffer to handle varying data rates and processing speeds
  • Apache Kafka, Amazon Kinesis, RabbitMQ
• Stream processing engine consumes data from the message broker or queue
  • Performs real-time computations, transformations, aggregations
  • Apache Flink, Apache Storm, Apache Spark Streaming
• Data sinks are destinations for processed data
  • Databases (PostgreSQL, MongoDB), file systems (HDFS), downstream applications
• Data flow enables real-time processing and analysis of continuous data streams
  1. Data sources → Message broker/queue
  2. Message broker/queue → Stream processing engine
  3. Stream processing engine → Data sinks

Stream vs batch processing paradigms

• Stream processing handles unbounded, potentially infinite data streams
  • Processes data in real-time as it arrives
  • Continuously computes results based on incoming data
  • Provides low latency and near-instant insights
  • Performs stateful processing, maintaining intermediate state for computations
• Batch processing operates on finite, bounded datasets
  • Processes data in discrete batches at scheduled intervals
  • Has higher latency compared to stream processing
  • Suitable for complex, long-running computations
  • Performs stateless processing, each batch is processed independently
• Use cases differ based on processing paradigm
  • Stream processing: Real-time monitoring (network traffic), fraud detection (credit card transactions), live dashboards (stock tickers)
  • Batch processing: Historical analysis, data warehousing (ETL), offline reporting

Use cases for stream processing

• Real-time analytics and monitoring analyzes data in real-time
  • Sensor data (temperature, humidity), user interactions (clicks, scrolls), system logs
  • Detects anomalies, patterns, trends as they occur
• Fraud detection and prevention identifies suspicious activities in real-time
  • Transactions (banking), activities (login attempts)
  • Enables prompt actions to prevent fraudulent activities
• Real-time recommendations and personalization based on user behavior and preferences
  • Product recommendations (Amazon), content suggestions (Netflix)
• IoT and sensor data processing enables real-time monitoring, control, automation
  • Smart homes (Nest thermostat), industrial equipment (turbines), vehicles (GPS)
• Live dashboards and visualizations provide up-to-date insights and metrics
  • Business KPIs (revenue, user growth), system metrics (CPU usage, network traffic)

Message brokers in stream processing

• Ingestion and buffering of incoming data streams from various sources
  • Receive and store data streams
  • Handle varying data rates and processing speeds
• Decoupling and scalability of data ingestion and processing components
  • Decouple data producers (IoT devices) from data consumers (stream processing engines)
  • Enable independent scaling
• Fault tolerance and reliability through data persistence and delivery
  • Persist data to disk, ensuring data durability
  • Provide fault-tolerant data storage
• Data partitioning and ordering based on keys or topics
  • Partition data (user ID, device ID)
  • Preserve the order of messages within each partition
• Pub-sub messaging model for parallel processing and load balancing
  • Allow multiple consumers to subscribe to and consume data from the same topics
  • Enable parallel processing across consumer instances (Kafka consumer groups)