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A data warehouse is the electronic storage of an Organization’s historical data for the purpose of Data Analytics, such as reporting, analysis, and other knowledge discovery activities.
Other than Data Analytics, a data warehouse can also be used for the purpose of data integration, master data management, etc.
According to Bill Inmon, a data warehouse should be subject-oriented, non-volatile, integrated, and time-variant.
Non-volatile means that the data once loaded in the warehouse will not get deleted later. Time-variant means the data will change with respect to time. The above definition of data warehousing is typically considered as a “classical” definition.
Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. A data warehouse is often built to enable Data Analytics
A data warehouse helps to integrate data and store them historically so that we can analyze different aspects of the business including, performance analysis, trend, prediction, etc. over a given time frame, and use the result of our analysis to improve the efficiency of business processes.
For a long time in the past and also even today, Data warehouses are built to facilitate reporting on different key business processes of an organization, known as KPI. Today we often call this whole process of reporting data from data warehouses “Data Analytics”.
Data warehouses also help to integrate data from different sources and show single-point-of-truth values about the business measures (e.g. enabling Master Data Management).
The data warehouse can be further used for data mining which helps trend prediction, forecasts, pattern recognition, etc.
OLTP is a transaction system that collects business data. Whereas OLAP is the reporting and analysis system on that data. OLTP systems are optimized for INSERT, UPDATE operations and therefore highly normalized.
On the other hand, OLAP systems are deliberately denormalized for fast data retrieval through SELECT operations.
In a departmental shop, when we pay the prices at the check-out counter, the salesperson at the counter keys in all the data into a “Point-Of-Sales” machine. That data is transaction data and the related system is an OLTP system.
On the other hand, the manager of the store might want to view a report on out-of-stock materials, so that he can place a purchase order for them. Such a report will come out from the OLAP system.
[ Related Article OLTP vs OLAP ]
Data marts are generally designed for a single subject area. An organization may have data pertaining to different departments like Finance, HR, Marketing, etc. stored in a data warehouse, and each department may have separate data marts. These data marts can be built on top of the data warehouse.
ER model or entity-relationship model is a particular methodology of data modeling wherein the goal of modeling is to normalize the data by reducing redundancy. This is different than dimensional modeling where the main goal is to improve the data retrieval mechanism.
The Dimensional model consists of dimension and fact tables. Fact tables store different transactional measurements and the foreign keys from dimension tables that qualify the data.
The goal of the Dimensional model is not to achieve a high degree of normalization but to facilitate easy and faster data retrieval. Ralph Kimball is one of the strongest proponents of this very popular data modeling technique which is often used in many enterprise-level data warehouses.
A dimension is something that qualifies as a quantity (measure). For example, consider this: If I just say… “20kg”, it does not mean anything. But if I say, “20kg of Rice (Product) is sold to Ramesh (customer) on 5th April (date)”, then that gives a meaningful sense.
These products, customers, and dates are some dimensions that qualified the measure – 20kg.
Dimensions are mutually independent. Technically speaking, a dimension is a data element that categorizes each item in a data set into non-overlapping regions.
A fact is something that is quantifiable (Or measurable). Facts are typically (but not always) numerical values that can be aggregated.
Non-additive measures are those which can not be used inside any numeric aggregation function (e.g. SUM(), AVG(), etc.). One example of a non-additive fact is any kind of ratio or percentage. For example, a 5% profit margin, revenue to asset ratio, etc.
A non-numerical data can also be a non-additive measure when that data is stored in fact tables, e.g. some kind of varchar flags in the fact table.
Semi Additive Measures
semi-additive measures are those where only a subset of aggregation function can be applied. Let’s say account balance. A sum() function on balance does not give a useful result but max() or min() balance might be useful. Consider the price rate or currency rate.
The sum is meaningless on rate; however, the average function might be useful.
Additive measures can be used with any aggregation function like Sum(), Avg(), etc. An example is Sales Quantity etc.
This schema is used in data warehouse models where one centralized fact table references a number of dimension tables so as the keys (primary key) from all the dimension tables flow into the fact table (as a foreign key) where measures are stored. This entity-relationship diagram looks like a star, hence the name.
Consider a fact table that stores sales quantity for each product and customer at a certain time. Sales quantity will be the measure here and keys from the customer, product, and time dimension tables will flow into the fact table.
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Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
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