Stream Processing
Stream processing is a method for continuously analyzing and processing incoming data streams in real-time.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityAdvanced
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Errors in data processing
- Lack of data quality
- Security risks
- Regular monitoring of system performance.
- Establishment of clear data management policies.
- Conduct training measures for staff.
I/O & resources
- Raw data sources
- Processing rules
- Resource capacities
- Processed data streams
- Real-time dashboards
- Alerts and notifications
Description
Stream processing enables organizations to conduct real-time data analytics for immediate decision-making. This method is often used in big data applications to process and analyze large volumes of data efficiently.
✔Benefits
- Fast decision making
- Optimization of business processes
- Improved user satisfaction
✖Limitations
- Complexity of implementation
- High costs of infrastructure
- Dependence on real-time data
Trade-offs
Metrics
- Processing time per message
The average time taken to process a message.
- Throughput
The number of messages processed within a given time frame.
- Error rate
The percentage of erroneous messages during processing.
Examples & implementations
Real-time Data Analytics in a Large Online Shop
This online shop uses stream processing to analyze customer behavior in real-time.
Monitoring Bank Transactions
A bank implements stream processing to monitor transactions in real-time.
Real-time Analysis of Social Media
This company uses stream processing to analyze real-time data from social media.
Implementation steps
Setting up the infrastructure components.
Integration of data sources.
Implementation of processing rules.
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated data processing tools
- Insufficient documentation
- Insufficient maintenance of systems
Known bottlenecks
Misuse examples
- Usage of unvalidated data streams
- Ignoring real-time feedback
- Overloading the system with too much data
Typical traps
- Insufficient testing before implementation
- Lack of understanding of user requirements
- Ignoring performance metrics
Required skills
Architectural drivers
Constraints
- • Maximum data rate
- • Infrastructure costs
- • Compliance requirements