Month: November 2018

Big Data Analytics – Statistical Methods

Big Data Analytics – Statistical Methods

When analyzing data, it is possible to have a statistical approach. The basic tools that are needed to perform basic analysis are − Correlation analysis Analysis of Variance Hypothesis Testing When working with large datasets, it doesn’t involve a problem as these methods aren’t computationally intensive with the exception of Correlation Analysis. In this case, … Continue reading Big Data Analytics – Statistical Methods

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Can Hadoop Replace a Data Warehouse?

Can Hadoop Replace a Data Warehouse?

The users I spoke with ranged from seasoned data warehouse professionals to professionals who are better described as application developers who have limited data experience. Given the diversity of users (who come from diverse organizations with diverse requirements), I got diverse ideas about what a warehouse is (and is not), plus whether or not Hadoop … Continue reading Can Hadoop Replace a Data Warehouse?

Data Exploration vs. Data Discovery

Data Exploration vs. Data Discovery

Much has been made of the term “data discovery.” It is used profusely in the BI market and describes a fundamental transition in BI tools as emphasis has shifted from reporting to looking for new trends. Companies such as Tableau and Qlik altered the BI landscape with their ability to help business analysts discover new … Continue reading Data Exploration vs. Data Discovery

Big Data Analytics – Data Exploration

Big Data Analytics – Data Exploration

Exploratory data analysis is a concept developed by John Tuckey (1977) that consists on a new perspective of statistics. Tuckey’s idea was that in traditional statistics, the data was not being explored graphically, is was just being used to test hypotheses. The first attempt to develop a tool was done in Stanford, the project was … Continue reading Big Data Analytics – Data Exploration