Stream Processing
Stream Processing is the continuous, real-time computation on unbounded data flows where events are processed individually or in small windows as they arrive.
Stream processing handles data that arrives continuously: user clicks, sensor measurements, financial transactions, operational logs. Unlike batch processing that waits for a schedule, streaming processes data with minimal latency as it arrives. Stream processors (Kafka Streams, Apache Flink, AWS Kinesis) maintain state (like running totals or session windows) across events and emit results continuously. Streaming enables use cases that require immediate action: detecting fraud seconds after a transaction, alerting on anomalies in real-time, or updating dashboards within seconds.
Stream processing became practical at scale through distributed platforms that handle failures gracefully, maintain exactly-once semantics (prevent duplicate counts), and support stateful operations. Trade-offs exist: streaming requires more complex infrastructure than batch, and keeping persistent state in distributed systems is challenging. Most organizations use hybrid approaches: batch for bulk historical processing, streaming for operational dashboards and real-time decision-making.
In practice, streaming systems often run alongside batch platforms: Kafka captures all events, stream processors compute real-time dashboards and alerts, and separate batch jobs handle the authoritative analytics view. The separation allows different teams to choose tools suited to their latency requirements.
Key Characteristics
- ▶Processes events individually or in time windows as they arrive
- ▶Maintains state across events (aggregations, session context)
- ▶Provides low-latency results, often sub-second
- ▶Handles unbounded data (no natural "end" to the dataset)
- ▶Must manage exactly-once and at-least-once processing semantics
- ▶Integrates with event sources like Kafka, cloud message queues
Why It Matters
- ▶Enables real-time dashboards and operational insights
- ▶Detects anomalies and fraud immediately for quick response
- ▶Reduces cost by avoiding repeated batch processing
- ▶Supports responsive user experiences through immediate data availability
- ▶Enables stateful computations like session tracking and running totals
- ▶Feeds machine learning models with fresh feature updates
Example
A payment processor streams transactions from Kafka: fraud_detector processes each transaction against recent patterns, computes velocity (transactions per customer per minute), and flags suspicious activity within milliseconds; dashboard_aggregator maintains running counts of transactions by merchant category, updating Grafana in real-time; recommendation_engine streams customer events (views, purchases) to a feature store, immediately available for downstream ML models. Historical batch jobs run nightly for comprehensive reconciliation.
Coginiti Perspective
Stream processing generates data that still requires governed semantic definitions before it reaches analysts and AI systems. Coginiti's semantic layer applies consistent business definitions to streaming outputs alongside batch-produced data, so metrics mean the same thing regardless of how the underlying data was processed. This prevents the definitional fragmentation that often occurs when real-time and batch pathways evolve separately.
Related Concepts
More in Core Data Architecture
Batch Processing
Batch Processing is the execution of computational jobs on large volumes of data in scheduled intervals, processing complete datasets at once rather than responding to individual requests.
Data Architecture
Data Architecture is the structural design of systems, tools, and processes that capture, store, process, and deliver data across an organization to support analytics and business operations.
Data Ecosystem
Data Ecosystem is the complete collection of interconnected data systems, platforms, tools, people, and processes that organizations use to collect, manage, analyze, and act on data.
Data Fabric
Data Fabric is an integrated, interconnected architecture that unifies diverse data sources, platforms, and tools to provide seamless access and movement of data across the organization.
Data Integration
Data Integration is the process of combining data from multiple heterogeneous sources into a unified, consistent format suitable for analysis or operational use.
Data Lifecycle
Data Lifecycle is the complete journey of data from creation or ingestion through processing, usage, governance, and eventual deletion or archival.
See Semantic Intelligence in Action
Coginiti operationalizes business meaning across your entire data estate.