What and How Big Data in RDBMS

Big Data, as the name suggests, is about data that is BIG in nature.

The data is BIG in terms of size, and it is difficult to manage such enormous data with relational database management systems.

As much as 80% of the world’s data is now in unstructured formats (which is created and held on the web).

Data at this massive scale is cumbersome to deal with and does not fit well in traditional relational database models.

Organizations who wish to analyze all this data can no longer just depend on SQL based solutions like MySQL. Instead they have to turn to newer technologies like Hadoop, Cassandra, HBase, and Pig.

Big Data refers to data of massive scale and complexity. Big data is also about better analytics on this broad spectrum of data.

Sources of Big Data
Big Data is not just about being large in size, it is also about the variety of the data that differs in form or type. Some Sources of Big Data are given below:

• Enterprise data (emails, word documents)
• Transactions
• Social Media
• Sensor Data
• Public Data (energy, world resource, labor statistics)
• Scientific data related to weather and atmosphere, Genetics etc.
• Data collected by various medical procedures, such as Radiology, CT scan, MRI etc.
• Data related to Global Positioning System.
• Pictures and Videos
• Radio Frequency Data
• Data that may vary very rapidly like stock exchange information.
• Apart from difficulties in managing and storing such data, it is difficult to query, analyze and visualize it.

Big Data Characteristics

The characteristics of Big Data can be defined by four Vs:

Volume:

• It simply means a large volume of data that may span Petabyte, Exabyte and so on.
• However it also depends organization to organization that what volume of data they consider as Big Data.

Variety:

• As discussed above, Big Data is not limited to relational information or structured Data.
• It can also include unstructured data like pictures, videos, text, audio etc.

Velocity:

• Velocity means the speed by which data changes.
• The higher is the velocity, the more efficient should be the system to capture and analyze the data.
• Missing any important point may lead to wrong analysis or may even result in loss.

Veracity:

• It has been recently added as the fourth V, and generally means truthfulness or adherence to the truth.
• In terms of Big Data, it is more of a challenge than a characteristic.
• It is difficult to ascertain the truth out of the enormous amount of data and the one that has high velocity.
• There are always chances of having un-precise and uncertain data.
• It is a challenging task to clean such data before it is analyzed.

Big Data Analytics

• Big Data analytics are the natural result of four major global trends:
• Moore’s Law (which basically says that technology always gets cheaper)
• Mobile Computing (that smart phone or mobile tablet in your hand)
• Social Networking like Facebook , Foursquare, Pinterest etc.
• Cloud Computing (you don’t even have to buy hardware or software anymore; you can rent or lease someone else’s).

The Importance of Big Data

When big data is distilled and analyzed in combination with traditional enterprise data, enterprises can develop a more thorough and insightful understanding of their business, which can lead to enhanced productivity, a stronger competitive position and greater innovation – all of which can have a significant impact on the bottom line.

For example, in the delivery of healthcare services, management of chronic or long-term conditions is expensive. Use of in-home monitoring devices to measure vital signs, and monitor progress is just one way that sensor data can be used to improve patient health and reduce both office visits and hospital admittance.

Manufacturing companies deploy sensors in their products to return a stream of telemetry. In the automotive industry, systems such as General Motors’ OnStar deliver communications, security and navigation services.

Perhaps more importantly, this telemetry also reveals usage patterns, failure rates and other opportunities for product improvement that can reduce development and assembly costs.

The proliferation of smart phones and other GPS devices offers advertisers an opportunity to target consumers when they are in close proximity to a store, a coffee shop or a restaurant.

This opens up new revenue for service providers and offers many businesses a chance to target new customers.

Retailers usually know who buys their products. Use of social media and web log files from their ecommerce sites can help them understand who didn’t buy and why they chose not to (information not available to them today).

This can enable much more effective micro customer segmentation and targeted marketing campaigns, as well as improve supply chain efficiencies through more accurate demand planning.

Finally, social media sites like Facebook and LinkedIn simply wouldn’t exist without big data.

Their business model requires a personalized experience on the web, which can only be delivered by capturing and using all the available data about a user or member.