Snowflake Schema In Data Warehouse
Table of contents
- Introduction snowflake schema in data warehouse
- What is Snowflake ?
- Difference Between Snowflake and Star Schema.
- Characteristics of Snowflake Schema.
- Features of the Snowflake Schema.
- Advantages of Snowflake Schema.
- Disadvantages of Snowflake Schema.
- What is Data Warehouse.
Components of Data Warehouse Schema.
Steps in Schema Design
Advantages of Data Warehouse Schemas
- Disadvantages of Data Warehouse Schemas
- Characteristics of Data Warehouse
- History of Data Warehouse
- Need for Data Warehouse
Benefits of Data Warehouse
- Data Warehouse Tools
The snowflake schema is a type of star schema.In this design, a central fact table connects to several dimension tables. Unlike the star schema, the dimensions in a snowflake schema are organized in a normalized format across multiple related tables. This structure emerges when the dimensions of a star schema are more detailed and organized, featuring various levels of relationships, where child tables can have multiple parent tables. The snowflake effect only impacts the dimension tables, leaving the fact tables unchanged.
A snowflake schema is a data modeling method used in data warehousing to organize data in a way that makes it easy to query large datasets. In this schema, dimension tables are normalized into several related tables, forming a hierarchical or “snowflake” shape.
In a snowflake schema, the fact table remains at the center, surrounded by dimension tables. Each dimension table is divided into multiple related tables, creating a structure that looks like a snowflake.
What is Snowflake?
The snowflake design comes from expanding and normalizing the dimension table. A dimension table is considered snowflaked when its low-cardinality attributes are split into separate normalized tables. These new tables are linked back to the original dimension table using foreign key constraints. Typically, snowflaking is not advised for dimension tables because it makes the model harder to understand and slows down performance, as more tables need to be joined for queries.
Difference Between Snowflake and Star Schema
The key difference between star schema and snowflake schema is that in the snowflake schema, the dimension tables are organized in a normalized way to cut down on redundancy. This makes the tables easier to manage and saves storage space. However, it also means that more joins are needed for queries, which can slow down system performance.
Additionally, querying a snowflake schema can be more complicated than a star schema due to the need for more table joins. This can lead to longer response times for queries and increased resource use in the database. The snowflake schema may also be harder to understand and maintain because of its complexity.
Choosing between a snowflake schema and a star schema for a data warehousing project depends on the project’s specific needs and the balance between query speed, schema complexity, and data integrity.
Characteristics of Snowflake Schema
- The snowflake schema takes up little disk space.
- Adding a dimension to the schema is simple.
- The dimension table has two or more groups of attributes that provide details at various levels.
- Different source systems fill the attribute groups in the same dimension table.
- However, having many tables can slow down performance.
Features of the Snowflake Schema
Normalization
The snowflake schema is designed to be normalized, meaning it organizes data into several related tables. This helps to minimize data duplication and enhances data consistency.
Hierarchical Structure
In the snowflake schema, there is a hierarchical setup centered around a main fact table. This fact table holds the key metrics, while the dimension tables provide additional context for those metrics.
Multiple Levels
The snowflake schema can have various levels of dimension tables, all connected to the main fact table.
Joins
Using the snowflake schema often requires more complicated SQL queries that involve joining multiple tables. This can affect performance, particularly with large data sets.
snowflake schema Advantages & Disadvantages
- It offers organized data that helps solve data integrity issues.
- It takes up little disk space since the data is well-structured.
Disadvantages:
- Snowflaking can save space in dimension tables, but the overall savings in the data warehouse are often small.
- Avoid snowflaking or normalizing dimension tables unless it's necessary.
- Do not separate hierarchies of dimension tables into different tables; they should stay within the dimension table.
- Multiple hierarchies for the same dimension should be created at the most detailed level possible.
What is Data Warehouse
- A Data Warehouse (DW) is a type of relational database made for analysis and queries, not for handling transactions. It holds historical data from various sources, including transaction data.
- A Data Warehouse offers a unified view of historical data across the organization, helping decision-makers with data analysis and modeling. It serves the entire organization, not just specific user groups.
- It is not meant for everyday operations or transaction processing; instead, it aids in decision-making.
- A Data Warehouse can be described by these features:
- It is a database tailored for research tasks, pulling data from different applications.
- It caters to a limited number of users who engage for longer periods.
- It includes both current and past data to give a broader view of information.
- It is primarily used for reading data.
- It consists of a few large tables.
- A Data Warehouse is a subject-focused, integrated, and time-sensitive collection of information that supports management decisions.
Components of Data Warehouse Schemas Fact Table
The fact table is the main part of a data warehouse schema. It holds numerical data and connects to dimension tables through foreign keys. Fact tables are essential in relational databases for keeping measures and allowing quick queries.
Primary Key: Uniquely identifies each record in the fact table.
Foreign Keys: Link to primary keys in dimension tables.
Dimension Tables
Dimension tables hold descriptive information about the measures found in the fact table. They give context to the raw data and allow for in-depth analysis.
Customer Dimension Table: Holds information about customers, including their names, addresses, and demographic details.
Date Dimension Table: Contains information about dates, including year, month, and day.
Designing Data Warehouse Schemas
Designing an effective data warehouse schema involves understanding business processes, defining dimension and fact tables, and ensuring data integrity.
Steps in Schema Design
Find Business Processes: Figure out the main processes and metrics that need to be examined.
Define Dimension Tables: Create tables for descriptive attributes.
Create Fact Table: Centralize measures and link to dimension tables.
Normalize Data: For snowflake schema, further break down dimension tables to reduce redundancy.
Ensure Data Integrity: Use primary and foreign keys to maintain relationships.
Advantages And Disadvantages of Data Warehouse Schemas
- Improved Querying: Good schemas boost query speed.
- Data Unification: Helps merge data from various sources.
- Less Redundancy: Snowflake schema reduces data duplication.
- Better Data Analysis: Allows for complex queries and in-depth analysis.
Disadvantages
- Complexity: Creating and managing intricate schemas can be tough.
- Storage Needs: Snowflake and fact constellation schemas might need extra storage.
- Query Speed: Many joins in snowflake schema can slow down performance.
Characteristics of Data Warehouse
Subject-Oriented
A data warehouse focuses on organizing and analyzing data for decision-makers. It usually offers a clear view of a specific topic, like customers, products, or sales, rather than the entire organization's daily activities. This is achieved by leaving out irrelevant data and including all necessary information for users to grasp the topic.
Integrated
A data warehouse combines different types of data sources, such as RDBMS, flat files, and online transaction records. To make sure everything is consistent, it needs data cleaning and integration, which helps standardize naming conventions and attribute types across these sources.
Time-Variant
Historical data is stored in a data warehouse. For instance, you can access records from 3 months, 6 months, 12 months, or even older data. This is different from a transaction system, which usually only keeps the latest file.
Non-Volatile
A data warehouse is a separate storage system that holds data transformed from the original operational RDBMS. Unlike the operational system, the data warehouse does not have updates like adding, changing, or removing data.
History of Data Warehouse
The concept of data warehousing emerged in the late 1980s when IBM researchers Barry Devlin and Paul Murphy created the “Business Data Warehouse.”
Essentially, this idea aimed to provide a structure for moving information from operational systems to decision-making environments. It sought to solve issues related to this flow, particularly the high costs involved.
Without a data warehousing structure, a lot of space was needed to manage different decision support environments. In large companies, it was common for these environments to function separately.
Goals of Data Warehousing
- Assist with reporting and analysis.
- Keep the organization’s past data.
- Serve as a base for making decisions.
Need for Data Warehouse
1) Business User: Business users need a data warehouse to access summarized information from the past. Since they are not technical, the data should be shown in a simple way.
2) Store historical data: A data warehouse is necessary to keep time-related data from the past. This data is used for various needs.
3) Make strategic decisions: Some strategies rely on the information in the data warehouse, helping in making important decisions.
4) For data consistency and quality: By gathering data from different sources in one place, users can ensure that the data is uniform and consistent.
5) High response time: The data warehouse must handle unexpected loads and different types of queries, requiring it to be flexible and respond quickly.
Benefits of Data Warehouse
- Recognize business trends to improve forecasting choices.
- Data warehouses are built to handle large volumes of data effectively.
- The layout of data warehouses is user-friendly, making it easier for users to explore, comprehend, and query.
- Queries that are complicated in many normalized databases can be simpler to create and manage in data warehouses.
- List ItemData warehousing is a practical way to meet the high demand for information from many users.
- Data warehousing allows for the analysis of extensive historical data.
Data Warehouse Tools
Tools that help gather data and formats accurately from external sources into a data warehouse must carry out several key functions, including:
- Combining and integrating data.
- Changing data from one format to another.
- Transforming and calculating data based on business rules that require changes.
Managing and synchronizing metadata, which involves storing or updating information about source files, transformation processes, loading formats, and events.
When setting up a data warehouse, several important criteria should be considered:
- The tool must be able to identify readable data in the source environment.
- It should support flat files, indexed files, and older database management systems.
- The ability to combine records from different data sources is often necessary.
- An interface to specify what information to extract and convert is essential.
- The tool should be able to read from repository products or data dictionaries.
- The code produced by the tool must be easy to maintain.
- Selective extraction of specific data items and records allows users to get only what they need.
- A detailed examination of data at the field level is required to turn data into useful information.
- The tool must support data type and character set translation when transferring data between different systems.
- It should be able to create aggregated, summarized, and derived fields and records.
- The stability of the vendor and the support for their products should be carefully assessed.
FAQ"s
A snowflake schema is a more detailed version of a star schema. It has dimension tables that are divided into smaller parts, following a multi-dimensional data model.
A star schema consists of dimension tables and fact tables.
It is called a snowflake schema because its diagram looks like a snowflake. The process of snowflaking helps to organize dimension tables in star schemas.
No, “snowflake” and “snowflake schema” are not the same. Snowflake refers to a type of data warehouse software for handling large structured and semi-structured data. A snowflake schema is a way to arrange data in a database.
In a snowflake schema, there are usually four types of tables:
– Fact tables
– Dimension tables
– Hierarchy tables
– Bridge tables
Yes, we can join two fact tables easily now that we have a bridge table with identifiers for both fact tables.