6. Describe the following types of facts (measures) and, for each, describe a scenario that illustrates the use of each fact type. a. Additive. - The additive measurement can be summed across any of the dimensions that relate to the fact table. This type of fact is good for calculating the figures across the dimension. For example, sales figures of year 2014 can be summed across all quarters of years 2014. b. Semi-Additive -The semi- additive measurement can be summed across some of the dimensions, but not all dimensions. This type of fact is good for data that cannot be summed over time. For instance, total number of students on Monday is 60, the total number of students on Tuesday is 30 and the total number of students on Wednesday is …show more content…
It allows the users to take a slice of data from anywhere in the database. This dimensional modeling provides faster data retrieval and better understandability. Data is stored in two types of tables, fact and dimension tables. The fact table contains the measurements of business. The dimension table contains the context of measurements. The key differences between dimensional data warehouse design and operational database design are: First, the purpose of operational database design is for data storing while the purpose of dimensional data warehouse design is for data analysis and reporting. Second, in the operational database design, the tables and joins tend to be complex since they are normalized for RDMS while in the data warehouse design, the tables and joins tend to be simple since they are deformalized for quickly retrieving data. 8. Consider the operational data and the data warehouse data shown below. Do you see any issue(s) with the fact table rows in the data warehouse Sales fact table If so, how would you correct it/them? Show the fact table as it should be including any corrected data. ? (Do not consider corrections to the operational data or
The subsequent sections provide detailed data information and example scenarios for each of the three types.
Smith, W., & Jewett, D. (2009). Tableau software and teradata database the visual approach to the active data warehouse. In Retrieved from http://www.tableausoftware.com/learn/whitepapers
the track specifications. All of these are just some of the many examples of how math is
The company can make use of SAP BW/4HANA warehouse in order to perform any analytical operations on real time data. Using this data warehouse the company can generate reports which will be helpful: • For the business managers to know more about their product manufacturing and distribution costs. These reports will provide them with necessary information so that they can build new ways to reduce overall
Facts are part of the truth, but not its whole. Fact is always limited; it’s a piece of information about something. Fact is a small division of truth as interpreted by an individual.
Now we can say that an enterprise data warehouse could be used to manage the big data and the extreme workloads but we would find that often it is more efficient to preprocess the data before storing it in the warehouse. Let’s consider an example even data from hardware sensors have a lar...
8.) Balance – I chose this, as it is very precise (it measure to 2
The main reason the metric system is known for its simplicity is because there is only unit of measurement or a base unit for each type of measured quantity measured; length mass, weight, etc… There are a few base units in the metric system but the most common ones which are used are the meter, gram and liter. As an example if ...
INTRODUCTION Some Types of measurements include length, volume, mass and temperature. Length is the measurement or extent of something from end to end. Volume is the amount of space that a substance or object occupies, or that is enclosed within a container. Mass, is the quantity of matter that a body contains, as measured by its acceleration under a given force or by the force exerted on it by a gravitational force.
There are three types of measure in order to support the average of normal distribution.
Numeric variables have values that describe a measurable quantity as a number, like 'how many' or 'how much'. Therefore numeric variables are quantitative variables. Categorical variables have values that describe a 'quality' or 'characteristic' of a data unit, like 'what type' or 'which category'. Therefore, categorical variables are qualitative variables and tend to be represented by a non-numeric value. A continuous variable is a numeric variable. Observations can take any value between a certain set of real numbers. The value given to an observation for a continuous variable can include values as small as the instrument of measurement allows. Examples of continuous variables include height, time, age, and temperature. A discrete variable is a numeric variable.
[7] Elmasri & Navathe. Fundamentals of database systems, 4th edition. Addison-Wesley, Redwood City, CA. 2004.
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
The use of information systems for warehouse management is studied extensively in literature. For example, ...
__C__ 8. Which of the following scales would be used when the information is qualitative rather than quantitative?