Fast data: real-time analysis of big data

Fast data refers to the application of real-time big data analysis to smaller datasets. Over the years, the number of data sets has grown steadily, and they need to be analysed more and more quickly. Here is what you need to know about fast data.


Real-time analysis

The idea of fast data comes from the concept of big data. It is a further step in the field of large data sets. Fast data is designed to analyse information and assemblies where the data is small in number but still requires real-time analysis. To avoid complications, even if the number of data sets is small, fast data allows analysis while the data is valuable.

All values can be involved in fast data, regardless of whether they are structured or unstructured values. Structured data can be used and exploited to the full, while unstructured data represents everything that is not organised, but still has value. Fast data has been created to manage all these aspects.

Structured data is framed by specific tags. Thanks to them, the data can be interpreted. The transmitted subject can be deciphered and made intelligible. As for unstructured data, it is linked to everything outside a structure. Unstructured data can be non-textual. Fast data integrates all these types of values.

Fast data is becoming increasingly relevant with the growing use of the Internet of Things (IoT) and the Cloud. The Internet of Things is increasingly an important object of study since it represents all connected objects in addition to the telecommunication networks and processing platforms that are linked to these connected objects. The Internet of Things is set to take on an increasingly important role.

All companies and organisations will sooner or later be called upon to absorb an increasing flow of data. The speed of decision making becomes essential at a time when everything is constantly changing, and values are rapidly transforming. It is no longer enough to simply store data. It becomes essential to analyse them in the most efficient way possible. Information overload is one of the most important challenges of our time.

Research based on data used to be very time-consuming. Fast data facilitates the work of many companies and organisations, whether it is managing airline bookings, booking hospital appointments, or taking out insurance policies. Larger databases also need fast analysis, for government agencies or universities, for example.

One step further

The abundance of data has made it necessary to move from big data to fast data. This extra step has become essential. The value of data now exceeds anything a company has in terms of assets and capabilities. Decision-making must be fast and requires automatic analysis of the information. Even a few seconds can play an important role in this decision making.

In the business context, a delay in decision making can result in the loss of very large sums of money. In the age of computer viruses, reaction time is essential. Fast data can help solve a large-scale hacking problem by raising the alarm and providing solutions without delay.

The need to store and analyse information very quickly is well established. Time has become the key factor in decision making and data analysis. The aim is also to require as little archiving as possible. The time for keeping an overloaded history is over. Fast data allows for analysis and results that are updated each time a query is made. The data is constantly integrated and enriched.

The financial benefits are really considerable with the very reliable infrastructure that allows errors to be avoided and to function despite potential breakdowns. As soon as one machine has a malfunction, another can take over. Nothing stands in the way of real-time data analysis, regardless of the volume of data.

The progress in analysis speed is immeasurable. It used to take months for an analysis that now only takes seconds. So, the data has lost none of its value. The more time passes, the faster data becomes an integral part of the decision-making process. Especially in the financial sector, without fast data it is difficult to compete. This is also true in most areas.


The tools needed to use fast data

To use fast data, a streaming system is required. Streaming allows audio and video files to be broadcasted. They are transmitted over a network so that they can be played back in real time. The data is therefore transmitted as soon as it is generated.

In order to use fast data, it is also necessary to have a data warehouse. This is a storage system that allows data to be centralised. Data can come from both internal and external sources. Thanks to this system, it is possible to quickly access the information you need. The data is stored for analysis and decision-making.

The data warehouse allows the storage of historical data related to the subject to be analysed. The data stored there provides an overview of all types of transactions, which the company previously had to carry out, and is easily accessible to users.

More and more companies are making use of fast data and therefore need these tools to use data quickly. It's all about fast data processing in real time. With fast data, it is possible to analyse millions of facts in one second, which is a clear advantage for decision making.

Concrete applications

Fast data enables the necessary decisions to be taken for a company or organisation. Fast data is of course very useful in the field of marketing, especially in order to identify potential customers more and more precisely.

Thanks to fast data, data and information are collected and analysed very quickly, and in the case of an advertising campaign, an automatic response can be obtained.

Fast data offers many other possibilities, including surveillance from security cameras. Appropriate responses can be triggered automatically from the recorded data and analysed in real time. Fast data has a lot to offer in many areas. It is now increasingly being used for real-time analysis of the power consumption of connected objects.

Fast data is also particularly useful for native cloud applications, which are highly dependent on the speed of data transmission over storage media.

Do not hesitate to contact Ryax to optimise the processing of your data and make even more profitable use of the information you retrieve.

The Ryax Team.