This document discusses data streams and stream management systems. It defines data streams as continuous high-volume flows of data that arrive sequentially in real-time, such as social media feeds and sensor data. It describes the characteristics and challenges of managing data streams, including high volume and velocity, real-time processing, and limited resources. The document then defines stream management systems as software that handles processing, analyzing, and storing data streams efficiently in real-time. It lists the key features and architectures of stream management systems and provides examples of their use cases. Finally, it mentions some popular stream management system platforms.
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Data Streaming and Stream management system
1. Introduction to Data Streams and
Stream Management Systems
SHAIKH RIZWAN ASRAR
190105231018
B.TECH AIML B.E
ADVANCE DATABASES
GUIDED BY: DR. PRASANNA KAPSE
2. Definition: Data streams are continuous and high-volume flows of data that arrive
sequentially or in real-time.
Examples: Social media feeds, sensor data from IoT devices, stock market data, web server
logs.
What are Data Streams?
3. Continuity: Data streams are continuous and never-ending.
High Volume and Velocity: Streams can generate large amounts of data at high speeds.
Sequentiality: Data arrives in an ordered sequence and must be processed in real-time.
Time-Sensitive: Analysis and decision-making must be performed in near-real-time.
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Characteristics of Data
Streams
4. Data Volume and Velocity: Handling and processing large volumes of data at high speeds.
Real-time Processing: Analyzing and extracting insights from data as it arrives.
Limited Resources: Managing and allocating system resources efficiently.
Data Quality: Dealing with noisy or incomplete data in real-time.
Scalability: Ensuring the system can handle increasing data volume and stream complexity.
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Challenges in Managing Data Streams
5. Definition: A Stream Management System is a software framework or platform that
handles the challenges of processing and managing data streams.
Purpose: Collect, process, analyze, and store data streams efficiently in real-time.
What is a Stream Management
System (SMS)?
6. Stream Ingestion: Ability to receive and collect data streams from various sources.
Stream Processing: Real-time analysis and computation on the incoming data.
Stream Querying: Capability to query and retrieve specific information from the stream.
Stream Storage: Efficient storage and retrieval of data streams.
Stream Integration: Integration with external systems and databases.
Fault Tolerance: Resilience to failures and ability to recover from errors.
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Key Features of Stream Management
Systems
7. Stream processing can be done using various architectures, including:
Event-driven architectures (EDA)
Message queueing systems (MQS)
Complex event processing (CEP)
Lambda architectures
Microservices-based architectures
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Stream Processing Architectures
8. Fraud Detection: Real-time monitoring of transactions for suspicious activities.
Predictive Analytics: Analyzing streaming data to make predictions and recommendations.
IoT Data Processing: Handling and processing sensor data from IoT devices.
Social Media Monitoring: Analyzing social media feeds for sentiment analysis and trending
topics.
Network Monitoring: Real-time analysis of network traffic for security and performance
monitoring.
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Use Cases of Stream Management
Systems
9. Mention some popular SMS platforms:
Apache Kafka
Apache Flink
Apache Samza
Amazon Kinesis
Google Cloud Pub/Sub
Microsoft Azure Stream Analytics
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Popular Stream Management Systems
10. Data streams are continuous flows of data that require specialized management systems.
Stream management systems provide capabilities for real-time processing, analysis, and
storage of data streams.
SMS platforms play a crucial role in various domains like finance, IoT, social media, and
more.
Conclusion