All the existing data-intensive computing applications mainly focus on how to handle the challenges of massive data sets. However, there is a growing need for a clear definition of the requirements of these applications. This is because they play an important role in data intensive computing.

Informatics world is set to be changed by data-intensive computing as well as the way in which information is gathered and processed right from the hardware and algorithms to the way the user gets the interpreted data.

Many applications in data intensive computing are driving the shift in emphasizing the focus of large datasets to the wider realm of issues.

They also determine the time used to come up with a decision when data handling capacity is a critical matter, like the time required to process massive data volumes. However, data intensive computing is being faced by numerous challenges. Some of these challenges include:

Technological challenges

Some challenges join both computer intensive and data intensive challenges. However, others fit only on one of the two. Data intensive computing challenges happen when the methods used by researchers influence the size or complexity of the information source.

Data intensive computing begins with analyzing and interpreting large amounts of data. There are numerous tools which can be used in managing data intensive computing.

However, there are gaps in the remaining and existing capabilities since they are not integrated or cannot adapt to the domains easily. These challenges are worsened by the data intensive computing requirements like computing power which can only be accessed from high-performance systems.

Data management

Incoming data is one of the greatest challenges in data intensive computing. This is because most of these challenges happen because the data is usually obtained from different sources and locations, the types and scales differ as well as the quality and reliability.

The data can also be either highly relevant or unreliable. Also, the data may be found in large amounts of unnecessary information and might require evaluation by humans for better analysis.

Other requirements in the data might complicate the entire data ingestion process. Data management architecture is thus required to come up with plans on how data intensive applications can satisfy these requirements thus making the entire process complicated. Also, some of these applications may fail if they are used for the wrong purpose.


In data intensive computing, the data storages and their analysis success depend partly on the fact that the data they can collect and analyze data on a single logical file system. Efficient data access is possible as the analysis process can be deployed on local data processors and single programming model is used to design programs.

However, this cannot be a simple assumption in many data-intensive computing applications. This is because of the nature of the data they need to process which is distributed naturally, and the codes used in the legacy analysis should not be the same with the programming models, languages and execution platforms.

In recent years, distribution computation has been facilitated mainly by web services. SOAP protocol is used to transmit request and results in message form.

These messages can only be secured through leveraging existing security investments through web services security. However, large payloads cannot be transmitted through the same platform.


Computing resources required depends on the size of the data, the more it increases, the more the resources which are required. In most extreme cases, the tools used in analyzing the data are almost the same with the data, and this is in line with the techniques used to analyze the data.

As the data increases, the algorithms used in its analysis take much longer to execute and consume resources on high-performance platforms. Any fault in this can also hinder the analysis process, resulting in data which is not accurate.

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