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Streaming Integration
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Streaming Integration
by Steve Wilkes, Alok Pareek
Streaming Integration
Preface
Acknowledgments
Introduction
A Batch of Problems
Under Pressure
Time Value of Data
The Rise of Real-Time Processing
1. Streaming Integration
Real Time
Continuous Collection
Continuous Movement
Any Enterprise Data
Extreme Volumes
At Scale
High Throughput
Low Latency
Processing
Filtering
Transformation
Aggregation and Change Detection
Enrichment
Implementation Options
Analysis
Time-Series and Statistical Analysis
Event Processing and Pattern Detection
Real-Time Scoring of Machine Learning Algorithms
Correlation
Continuous Delivery
Value
Visibility
Reliable
Verifiable
A Holistic Architecture
2. Real-Time Continuous Data Collection
Databases and Change Data Capture
CDC Methods
Log-Based CDC Best Suited for Streaming Integration
Files and Logs
Data Collection from Filesystems
Messaging Systems
Data Collection from Messaging Systems
Cloud and APIs
Devices and the IoT
Data Collection from IoT Devices
IoT Scalability Considerations
3. Streaming Data Pipelines
Moving Data
The Power of Pipelines
Persistent Streams
4. Stream Processing
In-Memory
Continuous Queries
SQL-Based Processing
Consider the Users
User Interface–Based Processing
Multitemporality
Transformations
Filtering
Filtering for Data Reduction
Filtering for Writing
Analytics
Windows
Enrichment
Distributed Caches
Correlation
5. Streaming Analytics
Aggregation
Pattern Matching
Statistical Analysis
Integration with Machine Learning
Anomaly Detection and Prediction
6. Data Delivery and Visualization
Databases and Data Warehouses
Files
Storage Technologies
Messaging Systems
Application Programming Interfaces
Cloud Technologies
Visualization
7. Mission Criticality
Clustering
Scalability and Performance
Failover and Reliability
Recovery
Exactly-Once Processing
Security
Access Control
Encrypting Data in Flight
Encrypting Data at Rest
8. Conclusion
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Streaming Integration
Streaming Integration
A Guide to Realizing the Value
of Real-Time Data
Steve Wilkes and Alok Pareek
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