Data Warehousing : A Data Warehouse

What information is accessible? The data warehouse offers possibilities to define what’s offered through metadata, published information, and parameterized analytic applications. Is the data of high value? Data warehouse patrons assume reliability and value. The presentation area’s data must be correctly organized and harmless to consume. In terms of design, the presentation area would be planned for the luxury of its consumers. It must be planned based on the preferences articulated by the data warehouse diners, not the staging supervisors. Service is also serious in the data warehouse. Data must be transported, as ordered, promptly in a technique that is pleasing to the business handler or reporting/delivery application designer. Lastly, cost is a feature for the data

Data warehouse has different concepts of data. Each concept is divided into a specific data mart. Data mart deals with specific concept of data, data mart is considered as a subset of data warehouse. In Indiana University traditional data warehouse is unable to create large data storage. Further it shows any errors and imposed rules on data. The early binding method is disadvantage. It process longer time to get enterprise data warehouse (EDW) to initiate and running. We need to design our total EDW, from every business rule through outset. The late binding architecture is most flexible to bind data to business rules in data modeling through processing. Health catalyst late binding is flexible and raw data is available in data warehouse. It process result by 90 days and stores IU data without any errors.

A data warehousing is defined as a collection of data designed to support management decision making. Data warehouses contains a wide variety of data that present a coherent picture of the business conditions at a single point in time. Development of a data warehouse includes development of the systems that extract data from operating systems plus the installation of the warehouse database system that provides managers flexible access to the data. The term data warehousing generally refer to the combination of many different databases across an entire enterprise. (webopidia)

A data warehouse is a large databased organized for reporting. It preserves history, integrates data from multiple sources, and is typically not updated in real time. The key components of data warehousing is the ability to access data of the operational systems, data staging area, data presentation area, and data access tools (HIMSS, 2009). The goal of the data warehouse platform is to improve the decision-making for clinical, financial, and operational purposes.

Google is able to use data warehousing to improve its business. A data warehouse is a logical collection of data that supports business analysis activities and decision making tasks. Google can use a data warehouse to store information just like a database is able to, but in an aggregated form more suited to supporting decision-making tasks.

Enterprise Data Warehouses (EDW) have become the foundation of many enterprises' systems of record, serving as the catalyst of strategic initiatives encompassing Customer Relationship Management (CRM), Supply Chain Management SCM) and the pervasive adoption of analytics and Business Intelligence (BI) throughout enterprises. The role of databases continues to be an ancillary one, supporting the overall structural and data integrity of the EDW and increasing its value to the overall enterprise (Phillips, 1997). The advances made over the last decade in the areas of Extra, Transact & Load (ETL) have made it possible to create EDW frameworks and platforms more efficiently, creating greater accuracy in overall database and data warehouse performance as a result (Ballou, Tayi, 1999). The creation and use of an EDW to further drive an organization to its objectives requires that the differences between databases and data warehouses be defined, in addition to a clear, concise definition of just what data warehouse technologies are. Finally, the relationship between data warehouses and business intelligence (BI) including analytics needs analysis and validation. Each of these three areas are discussed in this analysis.

The implementation of the data warehouse was based on Kimball’s (Kimball and Ross, 2013) dimensional modelling techniques which involved business requirements analysis & and determination of data realities and the four step dimensional modelling design process. These was followed by the design and

In the early '90s, data warehousing applications were either strategic or tactical in nature. Trending and detecting patterns was the typical focus of many solutions. Now, companies are implementing data warehouses or operational data stores which meet both strategic and operational needs. The business need for these solutions usually comes from the desire to make near

·         Extracting data from source systems, transforming it, and then loading it into a data warehouse

Within an enterprise there are various different applications and data sources which have to be integrated together to enable Data Warehouse to provide strategic information to support decision-making. On-line transaction processing (OLTP) and data warehouses cannot coexist efficiently in the same database environment since the OLTP databases maintain current data in great detail whereas data warehouses deal with lightly aggregated and historical data. Extraction, Transformation, and Loading (ETL) processes are responsible for data integration from heterogeneous sources into multidimensional schemata which are optimized for data access that comes natural to human analyst. In an ETL process, first, the data are extracted from

Data warehouse are multiple databases that work together. In other words, data warehouse integrates data from other databases. This will provide a better understanding to the data. Its primary goal is not to just store data, but to enhance the business, in this case, higher education institute, a means to make decisions that can influence their success. This is accomplished, by the data warehouse providing architecture and tools which organizes and understands the

As your business evolves, the data warehouse may not meet the requirements of your organization. Organizations have information needs that are not completely served by a data warehouse. The needs are driven as much by the maturity of the data use in business as they are by new technology.

According to their uses, databases are also classified as transactional databases and data warehouses. A transactional database is optimised for capturing and storing data that are transacted into the system on a regular basis (Choudhuri, 2014). A relational database supports these transactions; therefore, transactional databases usually have data organised in a relational format to optimise quick data entry and storage. On the other hand, data warehouses support historic and integrated data storage that is optimised for reporting and analysis purposes. In order to make the data retrieval easy and

Mainly, in the data warehouse analyzing the large data helps the decision-making process. Indeed, in the data warehouse, the integration of the data from the

Before discussing the current data warehouse architecture in place at ICICI Bank, issues associated with it, especially due to immense data growth and different modalities of data sources, it would be appropriate to have a quick look at the data warehouse history and architectural framework and how ICICI Bank’s data warehouse has evolved over the years. Back in 2008 ICICI Bank used Teradata and was dependent on Teradata for its data warehouse. Back in those days the size of the data warehouse was 3TB. Because of the dramatic growth in the amount of data, user population and the source stations coupled with cost of scaling and maintenance as well as system availability,posed a problem for the bank in using their legacy data warehouse solution. The bank felt that its legacy data warehouse solution posed scalability issues and one of the major issues that bank faced was with their current