Comprehensive Report: Star Schema, Fact Table, Dimension Tables

This report provides a comprehensive overview of the star schema, fact tables, and dimension tables, crucial components in data warehousing. It begins with an introduction to the star schema, highlighting its role as a simple and effective method for organizing data marts and data warehouses. The report then delves into dimension tables, explaining their function in describing dimensions, including attributes, values, and keys. Following this, the report defines fact tables as central tables containing quantitative data for analysis. The report also covers the requirements for using the star schema, emphasizing its efficiency in data storage and management. The document concludes by summarizing the importance of these elements in data warehousing, emphasizing the ease of handling queries and the advantages of star schema in data management. References to relevant literature are also included.

Running head: STAR SCHEMA, FACT TABLE AND DIMENSION TABLES
STAR SCHEMA, FACT TABLE AND DIMENSION TABLES
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STAR SCHEMA, FACT TABLE AND DIMENSION TABLES
Table of Contents
Introduction....................................................................................................................2
Discussion......................................................................................................................2
Star schema................................................................................................................2
Dimension tables........................................................................................................2
Fact tables...................................................................................................................3
Requirement of star schema.......................................................................................3
Conclusion......................................................................................................................3
References......................................................................................................................4

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STAR SCHEMA, FACT TABLE AND DIMENSION TABLES
Introduction
This report aims to discuss the star schema, dimension tables and fact table. The
requirement for using the star schema is provided in this report. A detailed discussion of star
schema, fact table and dimension table is delivered in this report. Finally, an appropriate
conclusion is provided in this report.
These tools are essential in data warehousing for proper maintaining of data along
with proper implementation of data.
Discussion
Star schema
In the aspect of computing, the use of star schema is among the easiest method of
schema of the data mart and it is the method that is commonly exploited for developing the
dimensional data marts and the data warehouses. One or more than one fact tables that are
taking reference from numerous dimension tables are included in star schema. This method is
among the simplest technique for the handling of simpler queries (Dehdouh, Boussaid and
Bentayeb 2014). The name of the star schema is obtained from the resemblance of the
physical models to any shape of star along with the fact table at the centre and all the
dimension tables adjacent it that represents the points of the star.
Dimension tables
Any dimension table is the table involved in some star schema in any data warehouse.
The data warehouses are created by utilising the models of dimensional tables that consists of
dimensional and fact tables. The dimension tables are utilised for the describing of the
dimensions, these contain the values, attributes, and dimension keys (Karun and Chitharanjan

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STAR SCHEMA, FACT TABLE AND DIMENSION TABLES
2013). The dimension tables are commonly small in size that ranges from almost few to
several rows. Sometimes, the length of the dimension tables could be very large.
Fact tables
Any fact tables is a central table included in any star schema of any the data
warehouse. The fact tables stocks huge information that is quantitative for analysing is often
renormalized (Kulyukin et al. 2013). Any fact table functions with the dimension tables and it
consists of the data that has to be analysed and the data is stored in the dimension table in the
sequence the analysis of the data has to be performed.
Requirement of star schema
The storing of all the data of any organisation could be done in any single table where
all the attributes are repeated on every row. Moreover, it would utilise the increased space
and it makes the managing of the dimension much more difficult (Kämpgen and Harth 2013).
The master data management refers to the discipline of assuring the accuracy of the
dimensions. The mapping into visualisation tools could be done easily by the star schema like
the tableau.
Conclusion
Therefore conclusion can be drawn is that the fact tables, star schema, and dimension
tables are essential for the data warehousing. In the aspect of computing, the use of star
schema is among the easiest method of schema of data mart and it is the approach that is most
commonly used for developing the data warehouses and the dimensional data marts. Any
dimension table is the table involved in some star schema in any data warehouse. Any fact
tables is a central table included in any star schema of any the data warehouse. The fact tables
stocks huge information that is quantitative for analysing is often renormalized.

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STAR SCHEMA, FACT TABLE AND DIMENSION TABLES
References
Kulyukin, V., Kutiyanawala, A., Zaman, T. and Clyde, S., 2013, January. Vision-based
localization and text chunking of nutrition fact tables on android smartphones. In Proc.
International Conference on Image Processing, Computer Vision, and Pattern Recognition
(IPCV 2013) (pp. 314-320).
Karun, A.K. and Chitharanjan, K., 2013, April. A review on hadoop—HDFS infrastructure
extensions. In Information & Communication Technologies (ICT), 2013 IEEE Conference
on (pp. 132-137). IEEE.
Kämpgen, B. and Harth, A., 2013, May. No size fits all–running the star schema benchmark
with SPARQL and RDF aggregate views. In Extended Semantic Web Conference (pp. 290-
304). Springer, Berlin, Heidelberg.
Dehdouh, K., Boussaid, O. and Bentayeb, F., 2014, September. Columnar nosql star schema
benchmark. In International Conference on Model and Data Engineering (pp. 281-288).
Springer, Cham.