Streaming in Python

• Rather than attempting to load the complete dataset into memory at once, streaming is a technique for processing data piece by piece. 
• This method is ideal for managing huge datasets that are too large to store into memory since it reads, manipulates, and writes data piece by piece. 
• You can significantly lower the amount of memory utilized and improve the program's overall performance by using streaming.

Types of Data Streams :

Data stream –A data stream is a sequence of tuples that may be unchained. Each tuple is made up of a set of attributes, which is comparable to a row in a database table.

• Transactional data stream –It is a log interconnection between entities

1. Credit card – purchases by consumers from producer.
2. Telecommunications – phone calls by callers to the dialed parties.
3. Web – accesses by clients of information at servers.

Measurement data streams –

1. Sensor Networks – a physical natural phenomenon, road traffic.IP Network – traffic at router interfaces.Earth climate – temperature, humidity level at weather stations

Streaming is crucial for managing data for various reasons:

1. Large dataset size: Streaming enables you to handle data efficiently without exceeding the machine while working with datasets larger than the available memory.
2. Memory constraints: By processing data within the bounds of the available memory, streaming reduces the possibility of encountering memory overflow faults.
3. Error handling: Occasionally, you may need to use streaming because reading all of the data at once results in errors related to specific rows in the data. Streaming makes it easier for you to recognize and address these mistakes.
4. Real-time processing: Applications that need to process data in real-time, including gathering information from sensors or examining social media feeds, depend on streaming.