What is data streaming processing?

Streaming data sources

Streaming data can come from a variety of sources that generate data on a continuous or very frequent basis. Examples of such sources include IoT (Internet of Things) sensor data, server and application logs, user activity on websites and mobile apps (clickstream), financial transactions, social media data, market data or vehicle telemetry data.

Architecture of streaming systems

A typical stream processing system consists of several key components:

• Data Sources (Producers): Systems or devices that generate data streams.
• Message Broker/Queue (Message Broker): An intermediary system that receives data from producers and makes it available to consumers in a reliable and scalable manner. It acts as a buffer and provides separation between producers and consumers. Popular brokers include Apache Kafka, RabbitMQ, AWS Kinesis, Google Cloud Pub/Sub.
• Stream Processing Engine (Stream Processor): A component that reads data from a stream (from a broker), performs processing operations on it (e.g. filtering, aggregation, transformation, enrichment) and generates results. Popular engines include Apache Flink, Apache Spark Streaming, Kafka Streams, AWS Kinesis Data Analytics, Google Cloud Dataflow.
• Results storage (Sink): A place where the results of stream processing are stored, such as a database, data warehouse, analytics dashboard or other system.

Key concepts of stream processing

• Events (Events): A basic unit of data in a stream, representing a single event or record (e.g., user click, sensor reading).
• Time windows (Windows): A mechanism that allows aggregating and analyzing data at specific time intervals (e.g., the number of events in the last minute). Windows can be fixed (tumbling), sliding (sliding) or session-based.
• State (State): Many stream operations (e.g., aggregations) require intermediate state to be stored between processed events. Managing state in distributed systems is one of the challenges of stream processing.
• Event time vs processing time: Distinguish between the time when an event actually occurred (event time) and the time when it was processed by the system (processing time). Handling delayed or unordered events is an important aspect.

Applications of stream processing

Stream processing is used wherever fast data analysis and real-time response to events is needed:

• Monitoring and alerting: Detect anomalies, failures or security threats in real time based on logs and system metrics.
• Real-time analytics: Create dashboards and reports showing the current business situation (e.g. sales, website traffic).
• Fraud detection: Real-time analysis of financial transactions to identify suspicious patterns.
• Real-time personalization: Customize content or offers for users based on their current activity.
• IoT applications: processing sensor data to monitor and control devices in real time.
• Recommendation systems: Update recommendations to users on a regular basis.

Summary

Streaming data processing is a powerful paradigm for analyzing and responding to data in near real-time. It is central to many modern applications, from system monitoring to business analytics to IoT applications and personalization. Leveraging the right streaming tools and architectures allows companies to gain valuable insights and make faster, more accurate decisions based on the freshest data.