Data Warehousing in Data Analytics Company

Data Warehousing in Data Analytics Company

Introduction

The main business of the company is gathering and analysis of data. The company is operating for last 2 years. In past few years the growth related to organization data collection has become inevitable. This growth, combined with the growing requirements of IT and business security, compliance and flexibility, can present significant challenges (Thusoo et.al, 2010). The organization is currently balancing several issues:

Increasing importance of information in support of decision-making and new revenue streams flows

Requirements for regulatory compliance retention and data availability

Increased storage capacities growing volumes of unstructured content

Managing increasingly complex infrastructure

Pressures in relation to costs, skills and resources

Data warehousing will provide the company with simplicity, low cost and resilience in their storage environments and data. An infrastructure without these features can inhibit the ability of the company to make informed, responsive customer meet, to support employee productivity, comply with regulations and implement new business applications. The company would continue to review its approaches to managing storage and data as the IT and business needs evolve.

Data Warehouse Schema for the Company



This scheme is ideal for the company due to its simplicity and speed to be used for multidimensional analysis (OLAP, Data marts, EIS). Allows access both aggregate and detail data. The star schema design allows you to implement the functionality of a multidimensional database using a classical relational database (more widespread than multidimensional).

Another reason for using star schema is its simplicity from the point of view of the end user. Consultations are not complicated, since the conditions and connections needed only involve the fact table and dimension, no need to be chained by unions and condition levels to two or more case in a snowflake schema. In most cases are preferred for their simplicity star regarding snowflake to be easier to handle. Finally, is the best option for performance and speed to index dimensions individually without impacting the performance of the database as a whole.

Data Flow Diagram for Data Warehousing

Companies increasingly recorded information about their customers, suppliers and employees, analyze and establish profiles with similar needs and tastes. But not only is there more data, but data that are entirely new, and it requires tools to extract knowledge are increasingly sophisticated, making it difficult an adaptation parallel regulation on privacy policy and safety. Information has become a commodity of great value, being the data a new class of financial asset, such as currency or oil. The mass data storage and new sources of obtaining the same as Google or Facebook, not only affect the business world, but also to academia and the public administrations.

Currently existing mass storage solutions that allow profiling data and make data classifications (data warehousing, analytical software, business intelligence and cloud computing, among others). However, new storage mechanisms analytical data are revolutionizing the market because they treat information in a differentiated way, is known as Data Warehousing. This mechanism makes the processing and analysis of information from huge servers or large data warehouse dimension, which allow ...