Data Storage Solutions for Data Analytics- myassignmenthelp.com

Question: Discuss about theData Storage Solutions for Data Analytics. Answer: Business Drivers Select a subject for analysis using data warehousing techniques from the operational data captured in the Microsoft Northwind database. (The operational ER model is included in this document) Answer: Subject for analysis are the main subject areas. In the case of operational data captured in the Microsoft Northwind data the subject of analysis will be customer. Customer is the intersection of every line answers about Northwind data. Customer as a subject of analysis can be easily be seen and it relationship traced. In addition, customer as a subject of analysis can be agreed upon and defined in the subject areas identified in the Northwind business model (Cox, 2018). The customer as the subject of analysis leads to categorizing into subject areas namely; CustomerCustomerDemo and customerDemographics. This will be achieved by developing some questions about the Northwind information. Data Modeling Develop and present a suitable schema for the data warehouse (data mart). Discuss your reasons for the design. The suitable schema for the Northwind data will be the star schema. The star will schema will separate the business process information into facts and which hold the amount, dimensions and which are descriptive characteristic related to the information, and qualitative data about business. Astar schemaincomputing, is the easiest type ofdata martschema to makeand is the method extensively employed to develop dimensional data marts and data warehouses. A star schema with many dimensions is referred to a centipede schema. While it is simpler to maintain, it has dimensions with a few attributes which result to enquiries with various table joins and makes the star easy simple to use (Wickham, 2016). Measurements or metric are recorded by facts table. These table normally consist of foreign keys and numeric values to dimensional data where descriptive data is stored. The design of facts table are low level uniform details known as grain or granulity meaning facts probably record events at atomic stage. Over time this can lead to accumulation of large records. In a fact table. Facts table are defined as follows Specific events are recorded by transaction fact tables Facts are recorded at any given time by snapshot facts tables Aggregating snapshot tables records accumulative facts at a particular point in time. Compared to facts table dimension tables have a moderately lesser number of records, however each record might have a huge number of characteristics to define the fact information. Dimension tables, as a rule, have a moderately modest records compared with fact tables, yet each record might have a wide range of attributes to define data. Dimensions might characterize a wide assortment of qualities, however, the absolute most regular traits characterized by dimension table comprise: Time dimension table portray period at the most minimal level of period granularity of occasions to be noted in the star schema Geography table depict area information, for example, state, nation, or city Time dimension table portrays duration at which most minimal level of granularity for which occasions are noted in the star schema Product dimension tables define items Range dimension table describe scopes of period, dollar esteems or other quantifiable amounts to rearranging detailing. Employee dimension tables depict representatives, for example, sales representatives Reference Cox, D.R., 2018.Analysis of survival data. Routledge. Wickham, H., 2016.ggplot2: elegant graphics for data analysis. Springer.