The Importance of Streaming

What is Stream Processing and why is it important? It is a technology that makes it possible for users to query continuous data stream and detect any condition within a short time, mostly in a matter of seconds. For instance, streaming processing can be used to receive alerts from a temperature sensor by querying data stream to detect the behavior of the temperature. Streaming processing is also referred to as real-time analytics, streaming analytics and complex event processing among others. Although initially some of these terms differed, the term stream processing is commonly used. Conceptualized by the Apache storm, the technology has now become popular in the technological world.

Importance of Stream Processing

It was established that data processing provide valuable insights. However, the value of the insights varies. For instance, some insights are only valuable immediately the data is processed but lose value very fast. This is where stream processing comes in due to its ability to process data very fast. Below are some of the reasons why stream processing is important.

  • Normally, data flows in a continuous stream. In case of batch processing, the user is required to first stop the data stream, store and process it. There is the risk of aggregating multiple batches if the process is repeated severally. However, stream processing handles data seamlessly. It enables for the detection of patterns, inspection of results, focuses on multiple levels and simultaneous observation of data from multiple streams. In addition, over time, stream processing fit flawlessly with time series data and detecting patterns. For example, it can be very helpful when trying to determine the length of a web session in a continuous stream. It is important to note that most IoT data are time series data e.g. health sensors, transaction logs etc. and using batch processing can be very hectic.

 

  • While batching only process data after building up for some time, stream processing process data in real time spreading it over time. Therefore, unlike batch processing, stream processing requires less hardware. In addition, stream processing facilitates approximate query processing through systematic load shedding. It is therefore helpful in situations where approximate answers are adequate.

 

  • Batch processing first has to store data before processing it. Sometimes the data can be so huge to be stored. However, on the other hand, stream processing does not require data storage allowing handling of large data.

 

  • The IoT is projected to grow rapidly in the near future. In the same way, streaming data e.g. websites visit is also expected to increase due to the continued advancement of IoT. Stream processing is the best model that will naturally fit with the expansion of technology.

 

It is worth noting that stream processing cannot be applied at all the time. For instance, it is difficult to use stream processing where processing requires multiple passes through a set of data or in case of random access. Additionally, stream processing cannot be used in machine learning algorithms to train models. However, stream process is perfect when processing is done with a single pass or in case of temporal locality.

 

How to Do Stream Processing

There are several things you need to consider before deciding to build an application that handles stream processing. For instance, how much you want to scale, reliability and tolerance. An easy way is to place events in a message broker topic, write code that receives the events from the broker topic and finally publish results back in the broker. The code becomes your stream and is referred to as an actor. Alternatively, you can use a stream processing framework such as the event stream processor. The framework enables you to write logic for each actor, wire the actors and hook up the edges to the data source. The events can be sent either directly to the stream processor or through the broker. In this way, the event stream processor will collect the data and deliver to each actor. Furthermore, the event processor ensures that the actors run in the right order, collecting results and handling any possible failures.

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