Processing big data can be a very difficult task because of extreme complexity due to size and diverse data types across businesses.It can be broadly divided into three main types according to their source, which are structured, semi-structured or not structured at all i.e. unstructured. However, a big data is perfectly analysed only when a quick action on it is taken. Also, as big data comes from a variety of sources and has a lot of variety according to its parameters, the business handling it will have to perform certain tasks to use the whole information in a wise manner. Some of these tasks, or pain points in the language of business, are as follows:

Quickly handling large amounts of data with accuracy.

Processing the unprocessed data fully to get the most out of it.

Representing the data in form of visual charts, so that it becomes easy to understand and use.

Using the data on a large scale successfully.

Choosing the best tools to handle, analyse and process the big data.

Proper deployment of the production.

Doing all this while ensuring security and maintaining regular backups/recovery.

Quickly handling large amounts of data with accuracy

About 2.5 quintillion bytes of data get produced from innumerable sources every day. Some main data sources are cell phones, sensors, social media, websites and even online transactions. Because of such a large amount of data, each and every organisation and business is completely overloaded. Even the best analytics tools cannot utilise this data fully due to large amounts of time needed to process this data.

A major challenge for a business is to process all these data in real time and that too in an economical and less time consuming manner. The solution of this problem depends on a few important factors. Whenever we think about the right tool to analyze the big data with efficiency, the first name that comes in our mind is the Hadoop, designed by Apache software. Hadoop has a function called MapReduce. This function reduces the whole data into smaller and more readable fragments. The software then efficiently processes each fragment with the means of the single node of a cluster. Hadoop is one of the most frequently used data analysing tool and has many features too, but we should not forget the fact that for effective data analysing, a business needs a powerful tool that can store and process large amounts of both unstructured and structured data without any kind of lag in the system for speed. Besides that, Hadoop also presents challenges in data sharing in real time, resource sharing in real time, scheduling and also management of clusters.

The challenges presented by Hadoop can be broadly divided in the following:

Challenge of managing clusters.

Challenge of efficient scheduling of different jobs.

Challenge of managing big data.

Challenges of sharing resources.

The Infosphere BigInsight, built by IBM is another good big data analytics tool. It can easily help a business meet core requirements while maintaining compatibility of the data.

Processing unprocessed data to get the most out of it

Cleaning unprocessed data is a very important step of big data analysing. And it takes the most amount of time compared to the other steps. For a successful statistical analysis of the big data, the following data types are produced in a step-wise manner:

Raw/unprocessed data

Data which is technically correct

Constant data

Correct results according to the stats

Completely processed information

The first three data types are actually the main parts of the process of cleaning of big data. The rest represent outputs of data processing and analysing.

Raw data: This is the type in which the original data is received. It is very complex and has strange and unknown encoding. It also may have incorrect data and may lack correct headers too. So, basically, it needs to be refined for results.

Data which is technically correct: After the raw data is refined partially, it can be called ‘technically correct data’. Now, the character encoding is understandable and the data also have suitable headers.

Constant data: After all this, the data is ready to be utilised for any kind of statistical analysis. So, this data is a starting point for analysis.

Statistic and output: The statistic result, after being obtained, can be stored for later use. The results can also be tabulated if the user wants to show it in the form of a report.

Representing the data graphically

Representing data graphically is very useful as it enables audience to easily read and understand data. However, unstructured data can be a big pain to process and express graphically, especially when the future would bring in even more data that needs to be processed.

Data can be simplified with the use of visual charts and graphs. There are many different types of graphs. The best type of graph is decided according to the type of data. Mainly, graphical data can be categorised into two main types:

Bar charts, line graphs and pie chart: These types of graphs are used to express Boolean responses like categorical data.

Histograms: Histograms are the best solution when the data to be represented are continuous data, for example exam results or weight of a person.

Using the data on a large scale

As the amount of data to be processed by the organisations increases day by day, they face a major problem while making the data scalable

Services for data have to be deployed on many different stacks. Apache or PHP in the front-end and Programming languages like Java/Scala which have to interact properly with the front end and the Database.

There shouldn’t be any delay while the data services are being deployed.

Choosing the best tools to handle, analyse and process big data

A good analytic tool is extremely important, as a bad one can turn all efforts, put in to collect and process the data, unusable. The importance of the right tool in the first go is increased because it becomes extremely difficult to change the tool afterwards

Proper deployment of the product

It is generally noticed that many products cannot gain success due to the lack of proper deployment strategy. Deployments includes the process of the integration of the existing system of production with newer system.

Backup/Recovery and security

Regular backups have to be made for recovering data during emergencies. Also, emergencies must be tried to be avoided. Oracle R is a good tool that ensures both of them.