Analyzing How Big Data Analytics Is Used For Today 's Data Driven World Essay

Data is ever increasing. We need a system to represent, store and manipulate complex information, detect correlations and patterns, construct data models etc. Furthermore, being independently maintained, data can change in time or even change its base structure, making it difficult for modelling systems to accommodate these changes. Current representation and storage systems are not very flexible in dealing with structural changes and also they are not powered with the ability of performing complex data manipulations of the sort mentioned above.

Data is information that is stored and organized by fields and records. A field which can also be known as an attribute is a single unit of information, like a surname of an IBM employee. A record or tuple is a collection of related fields. For example, an employee record contains all information fields that are relevant to a specific IBM employee. Additionally, a file (also known as a table) has multiple records that are pertaining to a specific topic. “To signify, an employee file of a hotel contains all employee records (Rob, 2010). Lastly, a database comprises all related files. A hotel database, among others, consists of employee files, room files, customer files, and payment files.”

(Ian, Frank & Hall 2011) The world testifies to the increasing gap between the development of data and our understanding of it. Data mining is defined as the analysis of big observational data sets to establish unsuspected relationships that summarize the data in a novel, understandable and useful way to the data owner (David, Heikki & Padhraic 2001). These relationships and summaries are referred to as models or patterns. Patterns comprise sets of co-occurring attribute values referred to as item sets or more complex patterns that include sequences, which consider explicit precedence relationships that are either positional or temporal, and graphs considering arbitrary relationships between points (Mohammed and Wagner 2014). Other types of additional summarised relationships include rules, clusters, tree structures among others.

If you change your application, you have to change your database schema as well. This fixed schema used by RDBMS make them impossible to quickly incorporate new types of data. Also, it is a poor fit for unstructured and semi-structured data [14, 17].

Big data is a term that describes a large volume of data. This data comes in the form of structured and unstructured data. Structured data is information taken and sorted in rows and columns while unstructured data is pictures, tweets, videos, and location-based data. It is not surprising to see many businesses today utilizing data for financial gain. Businesses are harnessing data and using it to make investment decisions, marketing strategies, fraud reductions, and much more. These businesses and organizations can expect to become more profitable, effective, and efficient, but pushes the limits

In this note, we tackle both the hype and practical matters surrounding big data, including questions

Column oriented databases: In this data is stored in column instead of rows.Examples of this databases are Google’s Big table. HBase and Cassandra are derived from big table.

Data is either structured or unstructured. Where structured data is simply the data that can be stored in tables and these tables can be stored in conventional databases, while unstructured is a new type of data coming from tweets, likes, comments, text messages, google searching, photos, videos, Global

In this model, data is stored in sections of columns instead of rows. This is almost an inverse of a relational model. The names of the column need not be predefined, i.e. the structure isn 't fixed, which helps in great scalability and performance. Columns in a row are stored in order according to their keys. A super-column also might be used which is nothing but a column containing nested sub-columns.

Big Data, the term big data is very popular now a days. Big Data, from its name it can implies

Big data is an extensive collection of structured and unstructured data. It is a modern day technology which is applied to store, manage and analyze data that are not possible to manage, store and analyze by using the commonly used software or tools. Since all of our daily tasks are overtaken by the modern technologies and all the businesses and organizations are using internet system to operate, the production of data has increased significantly in past

Live chat conversation data is just one category of unstructured data that enterprises need to harness for growth. The already vast amount of unstructured data continues to grow, and less than 1% of it is analyzed. Unlike structured data, which exists in organized columns and rows that make search and data analysis an easy task, unstructured data tends to be messy. It's of no use until it's properly processed, analyzed and evaluated.

In 2013 the overall created and copied data volume in the world was 4.4 ZB and it is doubling in size every two years and, by 2020 the digital universe – the data we create and copy annually – will reach 44 ZB, or 44 trillion gigabytes [1]. Under the massive increase of global digital data, Big Data term is mainly used to describe large-scale datasets. Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making [2]. Volume of Big Data represents the magnitude of data while variety refers to the heterogeneity of the data. Computational advances create a chance to use various types of structured, semi-structured, and

The 1990s saw the emergence of ODBMS as the dominant data modeling application software. It began in the 1960s and continued during the 1980s as technologists were searching for a more flexible and capable way of dealing with data that fundamentally changed the way applications were being designed. Object technology depicted data in terms of “classes” instead of representing it in the strict table format of relational databases. A class is defined as the structure and code provided by the programmer. It includes a description of the type of data, how it is stored, and its code. However,

Document databases - Document databases pair each key with a complex data structure known as a document. Documents can contain many different