Data Streams Types
In simple words, data streams are dynamic data that is generated on a continual basis. This allows you to analyse data in real-time and gain insights on a wide range of scenarios. Few example use cases of data streams are :
- Sensor readings from machines
- e-Commerce purchase data
- Stock exchange data to predict the stock price
- Credit card transactions for fraud detection
- Social media sentiment analysis
These data can be processed to get better insights for decision making. The data can be processed either as a bounded stream or an unbounded stream.
1. Bounded Stream
The bounded stream will have a defined start and an end. While processing a bounded stream, we can ingest the entire data-set before starting any computation. Hence, we could perform operations such as sorting and summarize data. The processing of bounded stream is referred to as Batch processing.
2. Unbounded Stream
The unbounded stream will have a start but no end. Hence, the data needs to be continuously processed when it is generated. Data is processed based on the event time (Event time is the time that each individual event occurred on its producing device). The paradigm of processing unbounded stream is referred to as Stream processing.
Apache Flink is a powerful data processing framework which supports both batch processing and stream processing.
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