Introduction to Data Warehouse
What Is a Data Warehouse?
A data warehouse is a database designed to enable business intelligence activities: it exists to help users understand and enhance their organization's performance. It is designed for query and analysis rather than for transaction processing, and usually contains historical data derived from transaction data, but can include data from other sources. Data warehouses separate analysis workload from transaction workload and enable an organization to consolidate data from several sources. This helps in:
-§ Maintaining historical records
-§ Analyzing the data to gain a better understanding of the business and to improve the business
In addition to a relational database, a data warehouse environment can include an extraction, transportation, transformation, and loading (ETL) solution, statistical analysis, reporting, data mining capabilities, client analysis tools, and other applications that manage the process of gathering data, transforming it into useful, actionable information, and delivering it to business users.
To achieve the goal of enhanced business intelligence, the data warehouse works with data collected from multiple sources. The source data may come from internally developed systems, purchased applications, third-party data syndicators and other sources. It may involve transactions, production, marketing, human resources and more. In today's world of big data, the data may be many billions of individual clicks on web sites or the massive data streams from sensors built into complex machinery.
Data warehouses are distinct from online transaction processing (OLTP) systems. With a data warehouse you separate analysis workload from transaction workload. Thus data warehouses are very much read-oriented systems. They have a far higher amount of data reading versus writing and updating. This enables far better analytical performance and avoids impacting your transaction systems. A data warehouse system can be optimized to consolidate data from many sources to achieve a key goal: it becomes your organization's "single source of truth". There is great value in having a consistent source of data that all users can look to; it prevents many disputes and enhances decision-making efficiency.
Types of Schema's in Data Warehouse
There are four types of schemas are available in data warehouse.
1. Star Schema
2. Snow Flake Schema
3. Galaxy Schema
4. Fact Constellation Schema
Out of which the star schema is mostly used in the data warehouse designs. The second mostly used data warehouse schema is snow flake schema
When we consider an example of an organization selling products throughtout the world, the main four major dimensions are product, location, time and organization. Dimension tables have been explained in detail under the section Dimensions. With this example, we will try to provide detailed explanation about STAR SCHEMA.
What is a Star Schema?
Star Schema is a relational database schema for representing multimensional data. It is the simplest form of data warehouse schema that contains one or more dimensions and fact tables. It is called a star schema because the entity-relationship diagram between dimensions and fact tables resembles a star where one fact table is connected to multiple dimensions. The center of the star schema consists of a large fact table and it points towards the dimension tables. The advantage of star schema are slicing down, performance increase and easy understanding of data.
Steps in designing Star Schema
-§ Identify a business process for analysis(like sales).
-§ Identify measures or facts (sales dollar).
-§ Identify dimensions for facts(product dimension, location dimension, time dimension, organization dimension).
-§ List the columns that describe each dimension.(region name, branch name, region name).
-§ Determine the lowest level of summary in a fact table(sales dollar).
Important aspects of Star Schema & Snow Flake Schema
-§ In a star schema every dimension will have a primary key.
-§ In a star schema, a dimension table will not have any parent table.
-§ Whereas in a snow flake schema, a dimension table will have one or more parent tables.
-§ Hierarchies for the dimensions are stored in the dimensional table itself in star schema.
-§ Whereas hierachies are broken into separate tables in snow flake schema. These hierachies helps to drill down the data from topmost hierachies to the lowermost hierarchies.
Glossary:
Hierarchy
A logical structure that uses ordered levels as a means of organizing data. A hierarchy can be used to define data aggregation; for example, in a time dimension, a hierarchy might be used to aggregate data from the Month level to the Quarter level, from the Quarter level to the Year level. A hierarchy can also be used to define a navigational drill path, regardless of whether the levels in the hierarchy represent aggregated totals or not.
Level
A position in a hierarchy. For example, a time dimension might have a hierarchy that represents data at the Month, Quarter, and Year levels.
Fact Table
A table in a star schema that contains facts and connected to dimensions. A fact table typically has two types of columns: those that contain facts and those that are foreign keys to dimension tables. The primary key of a fact table is usually a composite key that is made up of all of its foreign keys.
A fact table might contain either detail level facts or facts that have been aggregated (fact tables that contain aggregated facts are often instead called summary tables). A fact table usually contains facts with the same level of aggregation.
What is a Snowflake Schema
A snowflake schema is a term that describes a star schema structure normalized through the use of outrigger tables. i.e dimension table hierarchies are broken into simpler tables. In star schema example we had 4 dimensions like location, product, time, organization and a fact table(sales).
In Snowflake schema, the example diagram shown below has 4 dimension tables, 4 lookup tables and 1 fact table. The reason is that hierarchies(category, branch, state, and month) are being broken out of the dimension tables(PRODUCT, ORGANIZATION, LOCATION, and TIME) respectively and shown separately. In OLAP, this Snowflake schema approach increases the number of joins and poor performance in retrieval of data. In few organizations, they try to normalize the dimension tables to save space. Since dimension tables hold less space, Snowflake schema approach may be avoided.
Fact Table
The centralized table in a star schema is called as FACT table. A fact table typically has two types of columns: those that contain facts and those that are foreign keys to dimension tables. The primary key of a fact table is usually a composite key that is made up of all of its foreign keys.
In the example fig 1.6 "Sales Dollar" is a fact(measure) and it can be added across several dimensions. Fact tables store different types of measures like additive, non additive and semi additive measures.
Measure Types
-§ Additive - Measures that can be added across all dimensions.
-§ Non Additive - Measures that cannot be added across all dimensions.
-§ Semi Additive - Measures that can be added across few dimensions and not with others.
A fact table might contain either detail level facts or facts that have been aggregated (fact tables that contain aggregated facts are often instead called summary tables).
In the real world, it is possible to have a fact table that contains no measures or facts. These tables are called as Factless Fact tables.
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