Data Warehouse
by [Name of Student]
Course
Professor
[Name of Institution]
April 24th,2020
Data warehouse is a big collection of data that is stored from various different sources, and this process is known as data warehousing. Data warehouses are used to collect data for the purpose of analyzing and criticality scrutinizing data in order to generate results and reports. Multiple queries are run on data warehouses in order to generate reports.
The technique of data warehousing has become a very big essential in the management of all academic and extracurricular data available inside a university or any other educational institutes. As universities acquire an extremely large collection of data related to students, staff, courses, and other such activities, there is a very dire need for every university to have their own data warehouse. There are multiple, very efficient data warehouses available right now in the market. Almost every big technology company is investing in data warehousing products. The two most efficient, in my opinion, for universities are Amazon Redshift and Google BigQuery.
In 2006, Amazon presented its biggest project till that date Amazon Web Services (AWS), in which all the services are run on the cloud. Redshift is also an AWS product. It is an entirely cloud-based data warehouse product which is built on PostgreSQL fork. It is a very efficient system that even has the ability to roll back on transactions. In a manner similar to Amazon, Google also has a cloud-based data warehousing product known as Google BigQuery, which supports both the standard SQL and Dremel language.
For Kennesaw state university, the best data warehouse product would be Amazon Redshift. As redshift caters more to the small or medium level organization, it has an affordable price and easy operability. In contrast to Redshift, Google’s BigQuery is designed by keeping large scale organizations and data miners more in mind, so it is a bit more complex. Amazon Redshift is available with unlimited processing at $306 TB per month, whereas BigQuery is only $20 TB per month, but it only covers storage, and processing charges are paid additionally (Tobin, 2019).