Is Big Data processing really expensive ?

Want to process Big Data, but worried about the Big Data processing cost? If yes, then I am going to explain the inside story!

Customers always want to estimate a total solution cost for any data analytic problem. It should include all system as well as software related cost for that solution.

As you know that big data is a collection of huge unstructured and semi structured data. There are different steps for processing big data from data integration to present the results to clients. Big Data processing needs several resources in which you will have to invest money. It needs several hardware, software and human to analyse. We will see the cost for each and every resource in detail below,

Hardware:

You need storage to store data in it. Hardware using for storage are costly. If you will use commodity hardware for building reliable storage then it is about USD 1 per gigabyte.

Software:

You will need different software to store, manage and analysis purpose. Some are open source but others are not. You will need to pay huge amount for them.

There are two major frameworks for storing big data processing: the data warehouse and Hadoop. You may choose any of them according to your requirements. An established company uses data ware house technology in which they create a database to capture and store the data comes from different sources to process it. Hadoop is used by newer company. It is an open source framework to process large set of data using distributed computing. Hadoop is most popular than data warehouse due to its low cost. Before using Data ware house as storage you need to understand the cost these storage facilities can generate

1. Cost for finding appropriate big data solution. This cost is called entry cost
2. Cost for data integration and migration. In this process the data is moving to the new system.
3. Cost of different tools to handle those solutions. You will need a huge space for storage and it should be at least three times greater than the amount of data for replication purpose.
4. Cost for data development. It includes the cost of different experts who will help you in different phase of data processing. Those are data analyst, business analyst, Visualization experts etc.
5. Cost of different hardware and software for analysing the data. The needs of different tools and techniques for different types of problems for which you will analyse the data

The cost of a Hadoop cluster is less than $1,000 per TB i.e. several times lower than the average price for a data warehouse platform.

A research by ‘WinterCorp’ compared both the platform architecture for 500TB of stored data. The five-year ‘total cost of data’s shows Hadoop ($9.5 million) is far more cost effective solution than data ware house ($30 million).

You will need to set up a lab in which all the operations can be done. You will need different hardware and software for executing different steps. First you will need Hardware and software for data integration. So you can collect the data from different sources. After collecting these data you will need to have a huge storage where these data can be stored. Then you will need to go for data development phase. In this phase data will be transforming in the readable format so it can be easily read by the processing software. You will need some developer who can store and arrange those data in readable format. After these processes you have data ready for analysing. You will need a team of skilled analyst for the analysis of data. Hiring big data analyst is costly. You will need to expense huge amount on them for analysing. After analysing the data you will need some Visualization experts to represent the analysed result in visual format like graph, chart and animation etc. for the client.

These are the area of expenses in big data processing. Now companies will have to calculate the overall expenses & decide to go for outsourcing or setup a lab for the big data processing.