Weekly summary 3
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Apr 3, 2024
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Running Head: Weekly Summary 3
UNIVERSITY OF THE POTOMAC
DACS640:7: Online Data Integration, Warehousing, Provenance, and Analysis
Dr. Daryl R. Brydie
Megha Makol
Weekly Summary 3
Data Cube:
A data cube is a multidimensional model that represents data in an easy-to-understand format.
It is an essential component of business intelligence and data analysis systems, allowing users to
view and analyze data from various angles.
Time, Metric, and Geography are three dimensions mentioned in the question that are
commonly used in sales analysis.
Time: This dimension enables users to examine sales data over a specific time frame. For
example, a company may want to compare product line sales from one quarter to the next or
year over year.
Metric: This dimension refers to the specific measure or value under consideration. This could
be the number of units sold, total sales revenue, or profit margin in the context of sales.
Users can analyze sales data by geography using this dimension. For example, a company may
want to compare product line sales in different regions or countries.
Businesses can gain valuable insights into making informed decisions by analyzing sales data
across these three dimensions. They may, for example, identify seasonal trends, assess the
performance of various product lines, or identify regions where sales are underperforming. (1)
Snowflake Schema vs. Star Schema
The snowflake and star schemas are two ways to organize data marts or entire data warehouses
using relational databases. Dimension tables are used to describe data aggregated in a fact
table. Everyone sells something, whether it's information, a product, or a service. This
information must also be stored, either in an operational system or in a reporting system. As a
Weekly Summary 3
result, we can expect to find some kind of sales model in nearly every company's data
warehouse. Let’s revisit the sales model in both the star and snowflake schemas.
The star schema's most noticeable feature is that dimension tables are not normalized. The fact
sales table in the preceding model stores aggregated data generated by our operational
database(s). The dim_product, dim_customer, dim_store, and dim_date tables are measurement
tables. We chose these four dimensions because we need to use them as parameters in reports.
Our reporting requirements also influence the granulation within each dimension. We can easily
see why this schema is known as the star schema' from this model: It resembles a star, with the
dimension tables encircling the central fact table.
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Weekly Summary 3
Snowflake Schema:
This snowflake schema stores the same information as the star schema. The dimensions of the
fact table are the same as in the star schema example. The dimension tables in the snowflake
Weekly Summary 3
schema are normalized, which is the most significant difference. The process of normalizing
dimension tables is known as snowflaking. Once again, the snowflake schema visually resembles
its namesake, with several layers of dimension tables
forming an irregular snowflake shape. (2)
Reference:
1.
Ozdemir, Sinan. Principles of Data Science
. Packt Publishing Ltd, 2016.
2.
Star Schema Vs Snowflake Schema and the 7 Critical Differences
.
www.keboola.com/blog/starschemavssnowflakeschema#:~:text=Star%20schema
%20stores%20redundant%20data,order%20from%20the%20same%20country.