Overview to Big data Analytics

What is big data analytics?

As one of the most “hyped” terms in the market today, there is no consensus as to how to define big data. The term is often used synonymously with related concepts such as Business Intelligence ( BI) and data mining. It is true that all three terms is about analyzing data and in many cases advanced analytics . But big data concept is different from the two others when data volumes, number of transactions and the number of data sources are so big and complex that they require special methods and technologies in order to draw insight out of data (for instance, traditional data warehouse solutions may fall short when dealing with big data). This also forms the basis for the most used definition of big data, the three V: Volume, Velocity and Variety · Volume: Large amounts of data , from datasets with sizes of terabytes to zettabyte. · Velocity: Large amounts of data from transactions with high refresh rate resulting in data streams coming at great speed and the time to act on the basis of these data streams will often be very short . There is a shift from batch processing to real time streaming. · Variety: Data come from different data sources. For the first, data can come from both internal and external data source. More importantly, data can come in various format such as transaction and log data from various applications, structured data as database table , semi-structured data such as XML data, unstructured data such as text, images, video streams, audio statement, and more. There is a shift from sole structured data to increasingly more unstructured data or the combination of the two. get more through big data online training

This leads us to the most widely used definition in the industry. Gartner (2012) defines Big Data in the following. Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. It should by now be clear that the “big” in big data is not just about volume. While big data certainly involves having a lot of data, big data does not refer to data volume alone. What it means is that you are not only getting a lot of data. It is also coming at you fast, it is coming at you in complex format, and it is coming at you from a variety of sources. It is also important to point out that there might not be too much value in defining an absolute threshold for what constitutes big data. Today’s big data may not be tomorrow’s big data as technologies evolve. It is, by and large, a relative concept. From anyone’s given perspective, if your organization is facing significant challenges (and opportunities) around data’s volume, velocity and variety, it is your big data challenge. Typically, these challenges introduce the need for distinct data management and delivery technologies and techniques. you can become expert in big data through big data online couse