There's a lot of talk about Big Data—estimates are that the total amount of digital information in the world is increasing ten times every five years, with 85 percent of this data coming from new data types for example, sensors, RFIDs, web logs, and so on. This presents a huge opportunity for businesses that tap into this new data to identify new opportunity and areas for innovation.

However, having a platform that supports the data trend is only a part of today's challenge; you need to also make it easier for people to access so that they can gain insight and make better decisions. If you think about the user experience, with everything we are able to do on the Web, our experiences through social media sites, how we're discovering, sharing, and collaborating in new ways, user expectations of their business, and productivity applications are changing as well.

One of the first questions we should set out to answer is a simple definitional one: how is Big Data different from traditional large data warehouses? International Data Corporation has the most broadly accepted theory of classifying Big Data as the three Vs:

Today, every organization finds it difficult to manage and track the right dataset within itself, the challenge is even greater when they need to look out for data which is external to the system. A typical analyst spends too much time searching for the right data from thousands of sources, which adversely impacts productivity. We will move from a world of search to one of discovery, where information is brought to the user based on who you are, and what you are working on. There has never been such an abundance of externally available and useful information as there is today. The challenge is how do you discover what is available and how do you connect to it?

To answer today's types of question, you need new ways to discover and explore data. By this we mean, data that may reside in a number of different domains such as:

You could derive much deeper business insight and trends by combining the data you need across personal, corporate, community, and world data. You can connect and combine data from hundreds of trusted data providers—data includes demographic data, environment data, financial data, retail and sports data, social data such as twitter and facebook as well as data cleansing services. You can combine this data with your personal data through self-service tools, for example, PowerPivot, you can use reference data for cleansing your corporate data with SQL Server 2012, or you can use it in your custom applications.

Existing RDBMS solutions as SQL Server are good in managing challenging volumes of data, but it falls short when the data is unstructured or semi-structured with variable attributes such as the ones discussed previously. The current world seems almost obsessed with social media sentiments, tweets, devices, and so on; without the right tools, your company is adrift in a sea of data. You need the ability to unleash the wave of new value made possible by Big Data. It's all and every bit of data that you should be able to easily monitor and manage regardless of type or structure. That's why organizations are trending to build an end-to-end data platform for nearly all data and easy-to-use tools to analyze it. Regardless of data type, location (on-premises or in the cloud), or size, you have the power of familiar tools coupled with high-performance technologies to serve your business needs from data storage, processing, and all the way to visualization. The benefits of Big Data are not limited only to Business Intelligence (BI) experts or data scientists. Nearly everyone in your organization can analyze and make more informed decisions with the right tools.

In a traditional business environment, the data to power your reporting mechanism will usually come from tables in a database. However, it's increasingly necessary to supplement this with data obtained from outside your organization. This may be commercially available datasets, such as those available from Windows Data Market and elsewhere, or it may be data from less structured sources such as feeds, e-mails, logfiles, and more. You will, in most cases, need to cleanse, validate, and transform this data before loading it into an existing database. Extract, Transform, and Load (ETL) operations can use Big Data solutions to perform pattern matching, data categorization, deduplication, and summary operations on unstructured or semi-structured data to generate data in the familiar rows and columns format that can be imported into a database table. The following figure will give you a conceptual view of Big Data:

Big Data requires some level of machine learning or complex statistical processing to produce insights. If you have to use non-standard techniques to process and host it; it's probably Big Data.

The data store in a Big Data implementation is usually referred to as a NoSQL store, although this is not technically accurate because some implementations do support a SQL-like query language. NoSQL storage is typically much cheaper than relational storage, and usually supports a write-once capability that allows only for data to be appended. To update data in these stores you must drop and recreate the relevant file. This limitation maximizes performance; Big Data storage implementations are usually measured by throughput rather than capacity because this is usually the most significant factor for both storage and query efficiency. This approach also provides better performance and maintains the history of changes to the data.