As days pass on technology is increasing exponentially. Consider a scenario by a taking an example of a Sales company. In order to know the periodical ( weekly/monthly/quarterly ) sales report of a company, it is not enough if we get the data from the particular department.We need to get the report on sales of all the products sold in that company.Moreover, we also need to coordinate the reports of both online sales and offline sales to get that report. In some cases, there may be a case of coordinating the several departments. In such cases, if we use traditional databases like SQL, to generate the whole report, we need to do a query on each and every department and integrate the result of that query. This is not possible when data is increasing day – to –day exponentially. To overcome this problem, we need a solution, then Data warehouse came into existence as a solution.
A Data Warehouse is a central repository for all the data that the management collects the data from various departments. The repository may be physical or logical. This Data warehouse is used when there is a need for analyzing the data from various data sources. This works effectively where is a need for storing and analyzing the bulk amounts of data. Lets us know how it handles the bulk amount of data through its architecture.
Before going to know about the architecture .let us discuss the ETL tool
ETL stands for Extract, Transform and Load. The usage of this tool is to extract the data from various sources, transform into a required format and load into the required chamber. The major advantage of this tool is it can extract the data from various sources and aggregates.It provides an easy way to retrieve the data in a quick and efficient manner as per client requirements.Informatica is an example of ETL tool.
The architecture of Data warehouse is explained by MSBI Online Training in Hyderabad in the following manner:
As shown in the figure below, the data into the data warehouses can be from the various sources as explained below.
The data from all the sources as explained above comes into a data Integration area. The data staging area contains two types of data.
The first type of data is called data staging area. This data is further divided into two types of data.
This area contains the data from various data sources in various formats
The data presented in this layer is transformed data of the Non – persistent data in a certain mono format using ETL tools.
The second type of data in the Integration area is called operational Data Store. The data which is about to be transformed into the warehouse was placed here using the ETL Tool.
It is a place where the data from different resources after manipulation stores here. From this data can be extracted by the clients as per requirements. It has the ability to store PETA bytes and retrieve the data instantly.
It also contains but, it contains the aggregated data from the data warehouse which works on a star topology. There can ‘n’ number of data marts for the data warehouse.
The results of these aggregated data ware seen on reports through Online Analytical Processing layer(OLAP).
Business productivity can be increased as data in data warehouse can be retrieved efficiently and quickly
Customer relationship can be managed as data in the data warehouse provides a consistent view of the customers.
A data warehouse also helps in reducing the cost by providing trends and patterns over a particular period.
Prerequisites: It’s better to have a fundamental knowledge of keeping up and examining them for implementation. No Programming abilities were required to take in this course. It’s great to have knowledge of the utilization of Excel or the worksheets to understand the whole design of MSBI.
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