BIG DATA HADOOP PRACTICE TEST

Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, techniques and frameworks.

Thus Big Data includes huge volume, high velocity, and extensible variety of data. The data in it will be of three types.

• Structured data − Relational data.

• Semi Structured data − XML data.

• Unstructured data − Word, PDF, Text, Media Logs.

Big Data Challenges:

The major challenges associated with big data are as follows −

• Capturing data

• Curation

• Storage

• Searching

• Sharing

• Transfer

• Analysis

• Presentation

To fulfill the above challenges, organizations normally take the help of enterprise servers.

Big Data Technologies:

Big data technologies are important in providing more accurate analysis, which may lead to more concrete decision-making resulting in greater operational efficiencies, cost reductions, and reduced risks for the business.

To harness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in realtime and can protect data privacy and security.

There are various technologies in the market from different vendors including Amazon, IBM, Microsoft, etc., to handle big data. While looking into the technologies that handle big data, we examine the following two classes of technology.

Operational Big Data:

This include systems like MongoDB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored.

NoSQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement.

Some NoSQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure.