Big Data-Analytics

Big Data-Analytics

Big Data is the term for a collection of data sets so large and complex that it becomes difficult to process using traditional relational database management systems [RDBMS].

The challenges include but not limited to capture, storage, search, sharing, transfer, analysis, and visualization.

    Big Data Platform consists:

  • Analyse Streaming Data
  • Unlock Big Data
  • Reduce Costs with Hadoop
  • Analyse Raw Data
  • Simplify the Data Warehouse

Gartner defines Big Data as high volume, velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.

According to IBM, 80% of data captured today is unstructured, from sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals, to name a few. All of this unstructured data is Big Data.

The big data not limited to a particular sector and businesses in every sector will have to grapple with the implications of big data turning the big data into business growth.

Data scientists, predictive modelers and analytics professionals analyses the large volumes of transactional and other forms of data that is generated not only through traditional channels such as customers, partners, employees etc but also through social networks and search engines such as Twitter Facebook, LinkedIn, yahoo, Bing and google etc. This analysis cannot be achieved by the conventional business intelligence tools hence Big data concept evolved with that tools, appliances and software that addresses this business need. As a result, organizations looking to collect, process and analyse big data using newer class of technologies that includes Hadoop and related tools such as YARN, MapReduce, Spark, Hive and Pig as well as NoSQL databases.