bigdata warehouse

BIGDATA WAREHOUSE

Group Members

R.Sebasteen Kishore 12PCA118

J.Kalaimani 12PCA120

Source :

Big data for dummies – Alan Nugentwww.slideshare.com

BIG DATA

Big data is the capability to manage a huge volume of disparate data, at the right speed, and within the right time frame to allow real-time analysis and reaction.

Big data characteristics:

Volume : How much data

Velocity : How fast that data is processed

Variety : The various types of data

VOLUME

VELOCITY

VARIETY

Big Data Warehouse :

A process of transforming data into information and making it available to users in a timely enough manner to make a difference

Data had to be gathered from a variety of relational database sources ,

And then ensured that the metadata was consistent, and that the data itself was clean and then well integrated.

Data warehouse included the following characteristics:

It should be organized so that related events are linked together.

The information should be non-volatile so that it cannot be

inadvertently changed.

Information in the warehouse should include all the applicable

operational sources. The information should be stored in a way that has consistent definitions and the most up-to-date values.

Big data and data warehousing share the same basic goals : to deliver business value through the analysis of data.

However, big data and data warehousing differ in the scope of their data

Big data is in many ways an evolution of data warehousing. To be sure, there are new technologies used for big data, such as Hadoop and “nosql” databases.

The majority of business users will access the data in this information architecture from the data warehouse, using SQL-based environments.

The Evolution of data warehousing :

Traditional Data Warehouse :

Complete record from transactional system.

All data centralized

Addition every month/day of new data

Analytics designed against stable environment

Many reports run on a production basis

Data flows for traditional warehouse :

Changing the Role of the Data Warehouse :

It is useful to think about the similarities and differences between the way data is managed in the traditional data warehouse and when the warehouse is combined with big data.

Similarities between the two data management methods include :

Requirements for common data definitions Requirements to extract and transform key data sources The need to conform to required business processes and

rules

Differences between the traditional data warehouse and big data include :

The distributed computing model of big data will be essential to allowing the hybrid model to be operational.

The big data analysis will be the primary focus of the efforts, while the traditional data warehouse will be used to add historical and transactional business context.

Big data stores will provide the capability to analyse huge volumes of data in near real time.

A big data store will take the results of an analysis and provide a mechanism to match the metadata of the big data analysis to the requirements of the data warehouse.

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