Around Big Data in Eight Minutes: Everything You Need to Knowby Adilin Beatrice September 2, 2020
The term ‘big data’ represents the unmanageable large datasets
The major asset of today’s tech world is Big Data. When the Covid-19 pandemic hit the economy and workspace, and pushed everyone to do remote professionalism, it is the big data that stood as a complement. Big data paved the way and accelerated the working strategy without pause.
Large datasets that need to be gathered, organised and processed is unprofessionally called big data. The issue of overload of data is not new. But technology has brought a solution to the increasing chaos in the computing sector.
What is Big Data?
Big data is basically referred to a large dataset or the category of computing strategies and technologies that are used to handle large datasets. It defines both structured and unstructured data that inundates a business on an everyday basis. Big data is the high potential of a company that uses insight and analysis to predict the future and detect accurate solutions and answers, and take apt decisions.
The large overflowing data is stored in various computers. The data set storage defers from organisations on their capacity and strategy of maintaining it.
History of big data
The term ‘big data’ represents the large datasets that are unmanageable. Remarkably, it is not the amount of data that is taken into account when an AI mechanism values it. The features of data are provided by the techniques used by the employees and the technology input to acquire a profitable outcome.
The concept of big data gained wide-range of recognition in the early 2000s when Gartner’s industry analyst Doug Laney articulated the now-mainstream definition of big data as the three V’s. He differentiated the three V’s from other data processing.
The collection of sources including business transactions, smart (IoT) devices, industrial equipment, videos, images, social media stuff and much more are collected in the form of data. Since the storage would be heavy, it becomes a challenge of polling, allocating and coordinating resources from groups of computers. The technological invasion of cluster breaking the large data into small pieces for management and algorithms became noticeable.
The addition of data cannot be stopped. Every day, millions of data inputs are being added to a stream which is further massaged, processed and analysed in order to keep up with the influx of new information and to surface valuable information early when it is most important.
Time and speed of data input play an important role. Organisations expect data to be in real-time to gain insights and update the current understanding of the system. But to cope with the fast inflow, the organisation needs robust systems with highly available components and storage to guard against failures along the data pipeline.
Data inputs are in all kinds of formats. A drawback about big data is that the wide range of data being processed and their relative quality are mixed. Data comes from various sources like applications, server logs, social media feeds and other external APIs like physical device sensors, and from other providers. They come in the form of unstructured text documents, emails, videos, audio, stock ticker data and financial transactions. A text file is stored in a similar way to a high-quality image. Almost all data transformations and changes to the raw data will happen in memory at the time of processing.
After the figuring of three V’s, various organisations started to find that there are more in big data. They have added two more dimensions to its usage.
Variability- Data flows are often unpredictable, changing and varying according to the wide range it posses. An additional dimension is needed to diagnose and filter the low-quality data and process it separately.
Veracity- Veracity refers to the quality of data input in real-time. Data comes from various sources and it is difficult to link, match, cleanse and transform data across systems. The cleaning and sorting of data are important because it impacts the data analysis outcome. Poor data ruin the effort of employees to gain data predictions.
Value- Acquiring data and delivering accurate value results is a struggle when the input is unorganised. The system and the process are complex adding to the struggle.
Why is big data important?
The data gains importance on the stance of how much data is stored and the way it is utilised. However, big data are remarkably known for its efficiencies like
•New product development through stored data and optimized offerings
•Smart and accurate decision making
Big data is a cycle process
Most big data solutions employ cluster computing. This leads way to the beginning of the technological invasion in the life cycle of big data analysis.
As the major problem of data from various sources is unsolved, cluster computing plays a major role in filling the gap. It will be difficult for individual computers to sort the data by itself. So companies seek the help of cluster computers where the software combines the resources of many small machines, seeking to provide several benefits.
Resource pooling- The combination and sharing of CPU, memory and large data is added for a beneficial purpose. Large data can’t be stored in a single space and it will be inadequate to do so.
High availability- Hardware and software failures are prevented when the data is shared in the storing purpose. The failure could affect the access to data and processing killing the concept of real-time analytics.
Easy scalability- The system can react to changes in resources required without expanding the physical resources on a machine when the scaling is done horizontally.
The general category of movement in data and its process can be divided into four categories.
Ingesting data into the system
The first step towards data storage is data ingestion. The process involves taking raw data and adding it to the system. Some obstacles that the system encounters during the input are the format and quality of data sources. There is a back door called ingestion tools which could be used to sort the trouble.
Persisting the data in storage
Persisting means leveraging a distributed file system for raw data storage. The management of data storage after ingestion to make it a reliable disk is persistence storage. The operation takes up the volume of incoming data, the requirements for availability, and the distributed computing layer to make more complex storage systems necessary.
Computing and analyzing data
The most important processing takes place in computing and analysing the data to get an outcome. The computing layer is the diverse part of the system as the requirements and best approach lead to better accurate answers through detailed analysis.
Visualizing the result
Presenting the data in an easily adaptive and attractive way will lead to better understanding. Recognising trends and changes in data over time is often more important than the values themselves. Visualizing is the final touch that complements the whole cycle of big data.
Many organisations are adopting big data for certain types of workloads and using it to supplement their existing analysis and business tools to maximize the revenue. Even when big data doesn’t suit all working style, it is still important to gather and store them at all means. May be not now, but one day the stored data will turn to be an invaluable asset.