Another buzzword in Data Science? Is fast data another hyped word or a phenomenon that is destined to change the business industry?
What is Fast Data?
Fast data is a part of big data which uses analytics in real-time to gain a higher business value. Its analysis is used to improve business value and achieve higher goals of profit and growth. In today’s competitive and fast-moving society, fast data is a significant player. Fast data is particularly used in real-time applications. Big data along with fast data is changing the business format and pushing boundaries to innovate and discover in this area. Uber is one of the most talked about users of fast data analytics. It uses fast data to make sure that the driver takes the request from anywhere in the world. It also uses it to calculate the route cost of the trip. As we now know that we need to deliver faster results and analytics in the day today. We are witnessing a shift in doing business which is visible by increasing numbers of jobs in data analytics from recruiters such as The Barton Partnership. The stakeholders today are converting to insight-driven data solutions rather than the other types. This is certainly the rise of fast data.
What is Fast Data Analytics?
Analytics in fast data is used to gain business value by discovering performance gains by scaling and analyzing the available data. Its time-sensitive nature helps us to provide more accurate results. It can also be combined with predictive analysis on developing large-scale high-quality solutions. The basic principle on which it works is that once a huge amount of data is collected in real-time, the processing afterward produces information about those events where the data originated.
From the Apache ecosystem, Apache Kudu is one of the most used software for analytics in fast data. It is specifically designed for fast analytics. This is possible with the help of various next-generation techniques and methods that reduce latency and enhances performance. Licensed under ‘Apache 2 license’, this is also responsible for fast data insertions and updation which gives architects to get multiple options to work for. Spark is another much-talked-about framework that is used as an open cluster computing environment for fast data. Also, Apache Spark’s ability to support fast data paradigms has arisen hopes for fast data solutions.
What are its implications?
The primary aim of fast data is to collect structured and unstructured data to analyze it for getting insights. Organizations are trying to gain faster insights from data at an unimaginable speed.
With the availability of many software platforms in today’s market, it should be easy to choose from. Certain architectures and designs are useful in designing data analysis systems that can be learned in specific software. Large corporations like Amazon and Google are also working on fast data technologies creating solutions in different areas of the information industry. Business stakeholders and executives need faster data to cope with the growing demands of customers and clients.
What are its applications?
As we know that the future holds in the current work trends in fast data, some potential applications of fast data are:
1. M2M and IoT
M2M or Mobile to Mobile communication is the future of mobile computing. It will certainly change our way of using smartphones. Its use in this industry provides them with real-time insights to get steady growth. Fast data along with streaming data also plays an important role in developing IoT-enabled devices. With the growing demands of low latency applications, fast data has already urged its success in the IoT industry. This is a trending application as more companies want faster data processing and growth analytics.
Surveillance is the key factor in the security and management of a company. Smart surveillance is a new solution that delivers actionable results in less amount of time. It offers reliable security solutions for organizations looking for high-end security systems. Smart surveillance is smart because it is uninterrupted and is done with advanced cameras to record without stopping. The recording done by such surveillance systems undergoes predictive analytics. If any anomaly or outlier event occurs, it is immediately reported.
3. Automotive Industry
The world is becoming connected and so are our cars. It is suited to overcome marketing and sales challenges. A number of algorithms are being developed in the automotive industry which can perform predictive analytics on the data collected by the numerous sensors and processors embedded in vehicles of the current age. Various data forms like vehicle mileage, maps, etc. can be used to improve the connectivity of the driver to the world as well as the management of fleets, roads, traffic, and other parameters of the transportation systems. Connected cars, just like social networks, have already made a huge impact on the automotive industry.
As we approach a new millennium, more and more companies are converting to big data solutions. As data technologies are becoming more complex and extensive. Companies are eager to make faster growth and conquer the market. Out of all the competition, they are now focusing their work on fast data solutions in order to change, discover and benefit from their data. By watching the current trends, it is easy to say that it will be interesting to see how the concept of fast data will shape the world in the upcoming years.
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