Analyze and understand how to combine data warehouse with business intelligence tools, and other useful information or tools to visualize KPIs are critical factors in achieving the goal of raising competencies and business results of an organization. This article reviews data warehouse concepts and their appropriate use in business intelligence projects with a focus on large amounts of information. Nowadays, data volume is more significant and critical, and proper data analysis is essential for a successful project. From importing data to displaying results, there are crucial tasks such as extracting information, transforming it analyzing, and storing data for later querying. This work contributes with the proposition of a Big Data Business Intelligence architecture for an efficiently BI platform and the explanation of each step in creating a Data Warehouse and how data transformation is designed to provide useful and valuable information. To make valuable information useful, Business Intelligence tools are presented and evaluates, contributing to the continuous improvement of business results.

An Evaluation of How Big-Data and Data Warehouses Improve Business Intelligence Decision Making.

Analyze and understand how to combine data warehouse with business intelligence tools, and other useful information or tools to visualize KPIs are critical factors in achieving the goal of raising competencies and business results of an organization The main objective of this paper is to present the development of a BI platform, using DW tools to create graphs and detailed reports for the Constructel company. The development of this work was thought and developed in stages, starting with the analysis of the theme, analyzing the literature review to support the case study; used tools; requirements gathering; architectural design; and ending with the development and implementation of a platform with dashboards and reports for the organization’s management. With the availability of this platform, it is intended that business managers will be able to identify solutions and anomalies in a more insightful and faster way, thus allowing to improve productivity and business quality, without neglecting the satisfaction of the organization’ internal employees, greater flexibility and availability so that managers can deal with other situations that are more technical and linked to the business itself.

BigData oriented to business decision making: a real case study in constructel

Over the past decade’s several new concepts emerged to organize and query data over large Data Warehouse (DW) system with the same primary objective, that is, optimize processing speed. More recently, with the rise of BigData concept, storage cost lowered significantly, and performance (random accesses) increased, particularly with modern SSD disks. This paper introduces and tested a storage alternative which goes against current data normalization premises, where storage space is no longer a concern. By de-normalizing the entire data schema (transparent to the user) it is proposed a new concept system where query execution time must be entirely predictable, independently of its complexity, called, SINGLE. The proposed data model also allows easy partitioning and distributed processing to enable execution parallelism, boosting performance, as happens in MapReduce. TPC-H benchmark is used to evaluate storage space and query performance. Results show predictable performance when comparing with approaches based on a normalized relational schema, and MapReduce oriented.

José Cecílio

Papers

SINGLE vs. MapReduce vs. Relational: Predicting Query Execution Time