Streaming data processing

Today a variety of spheres deals with huge data arrays that arrive constantly. What kinds of data are these?

Just a few examples:

• Data received from sensor networks with embedded devices within them;
• Info that circulates on the stock exchange;
• User activity in social networks.

In many cases, a user of this or that system needs an immediate response. In addition, there may be no possibility to store such enormous data amounts. Therefore, these streams should be handled here and now. E.g., data stream processing in sensor networks must be carried out in real time to ensure that a system doesn’t miss out that the indicators reach the critical level, and promptly notifies an owner.

Streaming data processing is a very complicated area in the programming field. Why?

• Data may arrive from several sources;
• The formats of info sets received from various places may vary;
• More than one stream may need to be handled simultaneously.

How does our team cope with the difficulties mentioned above?

Depending on a stream type, certain approaches and instruments can be utilized. There are two types for the most part:

• Big Data;
• Audio/video.

When working with Big Data, we apply streaming processing. As a rule, ISS Art guys apply Apache Spark data stream processing engine to accomplish it successfully. This tool works perfectly with Scala and Python languages. Being one of the most robust and up-to-date products in this field, Spark ensures data stream processing in the cloud environment, too.

Working with audio/video streams involves another set of issues. One of them is connected with broadcasting. It is quite difficult to build a server when you have one data source and a great user base. To overcome this obstacle, an open source media server such as Kurento can be utilized. Most programmers use C++ language when working with audio/video.

Data stream processing applications may have any database – there are no special standards toward it to meet. Most processing tools have built-in connectors.

What are the recommended criteria for streaming data processing systems to meet?

• First, an app should keep a good performance level;
• Second, a system should be scalable – that is, with growing amounts of info and number of users, an app should adjust accordingly;
• Third, it is important that a delivered solution will be further used and supported;
• Finally, it is crucial to strike a balance between code understandability (readability) and an algorithm performance.

Planning to build an advanced solution that will be capable of handling constantly incoming data arrays? We are more than happy to assist you. Get in touch with us to discuss it.