Introduction to streams concepts
Data Stream is a real-time, continuous, ordered sequence of items generated from multiple
sources.
A data stream is a continuous, rapid, and time-varying flow of data that keeps coming without
end. Unlike traditional datasets stored in databases, streams cannot be stored fully because they
are huge and unbounded.
The stream concept means processing continuous, real-time data to find useful patterns quickly
with limited memory and time.
Examples: stock market prices, sensor readings, Twitter feeds, online transactions.
Why special handling?
1. Data comes very fast.
2. Cannot store all data.
3. Need one-pass, real-time processing.
Stream Computing: analyzing this continuous data on-the-fly instead of storing it first.
Characteristics of Data Streams
1. Continuous – Data keeps arriving non-stop.
2. Rapid – Data arrives at a very high speed.
3. Unbounded – Potentially infinite (cannot be fully stored).
4. Time-varying – Values change with time.
5. Transient – Once data passes, it may not be stored (must be processed immediately).
Why Data Streams are Important?
Traditional DBMS cannot handle huge, fast, and real-time data.
Many applications (finance, healthcare, IoT, fraud detection) require immediate processing.
,Example:
Credit card fraud detection → must analyze stream in real time.
Traffic monitoring → must react instantly.
Challenges in Stream Processing
1. Memory Limitations → Cannot store the entire stream.
2. Single Pass Processing → Only one chance to process data.
3. Real-time Processing → Results must be produced quickly.
4. Concept Drift → Patterns in data may change over time (e.g., user behavior).
5. Accuracy vs. Speed Trade-off → Must balance fast decisions with reliable results.
Stream Processing Models
Window-based processing – Only consider a subset (window) of the stream:
Sliding window
Tumbling window
Decaying window
Approximate algorithms – Use estimation instead of exact calculation (sampling, counting).
Applications of Stream Processing
1. Financial services – Fraud detection, stock market analysis.
2. Telecom – Call records, network monitoring.
3. Social Media – Real-time sentiment analysis.
4. IoT & Sensors – Smart cities, health monitoring.
5. Cybersecurity – Intrusion detection.
, Stream vs. Traditional Data
Aspect Traditional Data (DBMS) Data Streams
Data Type Finite stored Infinite continuous
Processing Style Batch (offline) Online (real-time)
Storage Can be stored completely Cannot be stored fully
Examples. Bank records, student DB. Stock market, IoT data
Stream data model and architecture
Stream Data Model
A Stream Data Model is a framework that represents and processes continuous, real-time, and
unbounded sequences of data items. Unlike traditional databases, stream data arrives
continuously and must be processed instantly, often in a single pass.
A Stream Data Model defines how continuous data streams are represented and processed.
Features of Stream Data Model
• Continuous – Data flows without end.
• Unbounded – Cannot be stored fully.
• Transient – Processed once and discarded.
• Append-only – Data only grows, no updates.
• Low-latency – Must be processed in real-time.
Data Stream is a real-time, continuous, ordered sequence of items generated from multiple
sources.
A data stream is a continuous, rapid, and time-varying flow of data that keeps coming without
end. Unlike traditional datasets stored in databases, streams cannot be stored fully because they
are huge and unbounded.
The stream concept means processing continuous, real-time data to find useful patterns quickly
with limited memory and time.
Examples: stock market prices, sensor readings, Twitter feeds, online transactions.
Why special handling?
1. Data comes very fast.
2. Cannot store all data.
3. Need one-pass, real-time processing.
Stream Computing: analyzing this continuous data on-the-fly instead of storing it first.
Characteristics of Data Streams
1. Continuous – Data keeps arriving non-stop.
2. Rapid – Data arrives at a very high speed.
3. Unbounded – Potentially infinite (cannot be fully stored).
4. Time-varying – Values change with time.
5. Transient – Once data passes, it may not be stored (must be processed immediately).
Why Data Streams are Important?
Traditional DBMS cannot handle huge, fast, and real-time data.
Many applications (finance, healthcare, IoT, fraud detection) require immediate processing.
,Example:
Credit card fraud detection → must analyze stream in real time.
Traffic monitoring → must react instantly.
Challenges in Stream Processing
1. Memory Limitations → Cannot store the entire stream.
2. Single Pass Processing → Only one chance to process data.
3. Real-time Processing → Results must be produced quickly.
4. Concept Drift → Patterns in data may change over time (e.g., user behavior).
5. Accuracy vs. Speed Trade-off → Must balance fast decisions with reliable results.
Stream Processing Models
Window-based processing – Only consider a subset (window) of the stream:
Sliding window
Tumbling window
Decaying window
Approximate algorithms – Use estimation instead of exact calculation (sampling, counting).
Applications of Stream Processing
1. Financial services – Fraud detection, stock market analysis.
2. Telecom – Call records, network monitoring.
3. Social Media – Real-time sentiment analysis.
4. IoT & Sensors – Smart cities, health monitoring.
5. Cybersecurity – Intrusion detection.
, Stream vs. Traditional Data
Aspect Traditional Data (DBMS) Data Streams
Data Type Finite stored Infinite continuous
Processing Style Batch (offline) Online (real-time)
Storage Can be stored completely Cannot be stored fully
Examples. Bank records, student DB. Stock market, IoT data
Stream data model and architecture
Stream Data Model
A Stream Data Model is a framework that represents and processes continuous, real-time, and
unbounded sequences of data items. Unlike traditional databases, stream data arrives
continuously and must be processed instantly, often in a single pass.
A Stream Data Model defines how continuous data streams are represented and processed.
Features of Stream Data Model
• Continuous – Data flows without end.
• Unbounded – Cannot be stored fully.
• Transient – Processed once and discarded.
• Append-only – Data only grows, no updates.
• Low-latency – Must be processed in real-time.
