As Internet of Things (IoT) invades every aspect of our lives, big data analytics is likely to be utilized for many more things other than solving business problems. This growing popularity of big data analytics, which is altering the way businesses run, has given birth to a new term- ‘ Analytics of Things’.
Much before big data was identified as the most valuable asset for businesses, enterprises had expressed need for a system that could handle an ‘information explosion’. In 2006, an open source distributed storage and processing system was developed. This system called Hadoop spread across commodity hardware and encouraged the nurturing of many more open source projects that would target different aspects of data and analytics.
Growth of Hadoop:
The primary objective with which Hadoop was developed was storing large volumes of data in a cost effective manner. Enterprises were clueless how to handle their ever increasing volumes of data. So, the first requirement was to dump all that data in a data lake and figure out the use cases gradually. Initially, there used to be a standard set of open source tools for managing data and the data architecture lacked variety.
Prior to adopting big data, companies managed their reporting systems through data warehouses and different types of data management tools. The telecom and banking industry were among the first to step into big data. Over time, some of them completely shifted their reporting work to Hadoop.
Evolution of big data architecture:
Big data tools have witnessed drastic evolution. This encouraged enterprises to employ a new range of use cases on big data using the power of real-time processing hubs. This includes fraud detection, supply chain optimization and digital marketing automation among other things. Since Hadoop’s birth in 2006, big data has developed a lot. Some of these developments include intelligent automation and real-time analytics.
To keep up with the demands for better big data architecture, real-time analytics was incorporated in Hadoop and its speed was also improved. Different cloud vendors developed Platform as a Service (PaaS) component and this development was a strong driving force behind big data architectures becoming more diverse.
As companies further explored ways to extract more meaning from their data, it led to the emergence of two major trends: Analytics as a service (AaaS) and data monetization.
AaaS platforms provided a lot of domain experience and hence gave generic PaaS platforms a lot more context. This development made big data architecture more compact.
Another important development came with data monetization. Some sectors, like healthcare and governance, depend heavily on data collected through a range of remote IoT devices. To make these processes speedier and reduce network load, localized processing was needed and this led to the emergence of ‘edge analytics’. Now, there is good sync between edge and centralized platforms, which in turn enhances the processes of data exchange and analysis.
The above mentioned developments show how much big data has evolved and that currently a high level of fine-tuning is possible in its architecture.
Often enterprises struggle with successful implementation of big data. The first step is to define your big data strategy. Instead of going for full blown implementation, undertake shorter implementation cycles.
It is highly likely that our future will become completely driven by big data and ground-breaking innovations like automated analysts and intelligent chatbots. Don’t be left behind. Enroll for big data Hadoop certification courses and take full advantage of the power big data holds in today’s world of work. The big data Hadoop training in Gurgaon ensures that every student becomes proficient enough to face real challenges in the industry. Enroll now and get flat 10% discount on all big data certification courses.
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