Star schema vs snowflake schema differences


  • Difference between Star Flake or Snow Flake Schema?
  • star schema and snowflake schema
  • Difference Between Star and Snowflake Schema
  • What’s on our mind?
  • STAR vs SNOWFLAKE
  • Difference between Star Flake or Snow Flake Schema?

    What is Star Cluster Schema? What is a Star Schema? Star Schema in data warehouse, in which the center of the star can have one fact table and a number of associated dimension tables. It is known as star schema as its structure resembles a star. The Star Schema data model is the simplest type of Data Warehouse schema. It is also known as Star Join Schema and is optimized for querying large data sets. Example of Star Schema Diagram Characteristics of Star Schema: Every dimension in a star schema is represented with the only one-dimension table.

    The dimension table should contain the set of attributes. The dimension table is joined to the fact table using a foreign key The dimension table are not joined to each other Fact table would contain key and measure The Star schema is easy to understand and provides optimal disk usage. The dimension tables are not normalized. Snowflake Schema in data warehouse is a logical arrangement of tables in a multidimensional database such that the ER diagram resembles a snowflake shape.

    A Snowflake Schema is an extension of a Star Schema, and it adds additional dimensions. The dimension tables are normalized which splits data into additional tables. In the following Snowflake Schema example, Country is further normalized into an individual table. Example of Snowflake Schema The main benefit of the snowflake schema it uses smaller disk space. Easier to implement a dimension is added to the Schema Due to multiple tables query performance is reduced The primary challenge that you will face while using the snowflake Schema is that you need to perform more maintenance efforts because of the more lookup tables.

    Hierarchies are divided into separate tables. It contains a fact table surrounded by dimension tables. One fact table surrounded by dimension table which are in turn surrounded by dimension table In a star schema, only single join creates the relationship between the fact table and any dimension tables. A snowflake schema requires many joins to fetch the data. Simple DB Design. Denormalized Data structure and query also run faster.

    Normalized Data Structure. High level of Data redundancy Very low-level data redundancy Single Dimension table contains aggregated data. Data Split into different Dimension Tables.

    Cube processing is faster. Cube processing might be slow because of the complex join. Offers higher performing queries using Star Join Query Optimization. Tables may be connected with multiple dimensions. The Snowflake schema is represented by centralized fact table which unlikely connected with multiple dimensions.

    What is a Galaxy Schema? A Galaxy Schema contains two fact table that share dimension tables between them. It is also called Fact Constellation Schema. The schema is viewed as a collection of stars hence the name Galaxy Schema. Example of Galaxy Schema As you can see in above example, there are two facts table Revenue In Galaxy schema shares dimensions are called Conformed Dimensions. Characteristics of Galaxy Schema: The dimensions in this schema are separated into separate dimensions based on the various levels of hierarchy.

    For example, if geography has four levels of hierarchy like region, country, state, and city then Galaxy schema should have four dimensions. Moreover, it is possible to build this type of schema by splitting the one-star schema into more Star schemes. The dimensions are large in this schema which is needed to build based on the levels of hierarchy.

    This schema is helpful for aggregating fact tables for better understanding. Snowflake schema contains fully expanded hierarchies. However, this can add complexity to the Schema and requires extra joins.

    On the other hand, star schema contains fully collapsed hierarchies, which may lead to redundancy. So, the best solution may be a balance between these two schemas which is Star Cluster Schema design. Example of Star Cluster Schema Overlapping dimensions can be found as forks in hierarchies. A fork happens when an entity acts as a parent in two different dimensional hierarchies. Fork entities then identified as classification with one-to-many relationships. Summary: Multidimensional schema is especially designed to model data warehouse systems The star schema is the simplest type of Data Warehouse schema.

    Comparing Snowflake vs Star schema, a Snowflake Schema is an extension of a Star Schema, and it adds additional dimensions. It is called snowflake because its diagram resembles a Snowflake. In a star schema, only single join defines the relationship between the fact table and any dimension tables.

    Star schema contains a fact table surrounded by dimension tables. Snowflake schema is surrounded by dimension table which are in turn surrounded by dimension table A snowflake schema requires many joins to fetch the data.

    A Galaxy Schema contains two fact table that shares dimension tables. Star cluster schema contains attributes of Star and Snowflake Schema. You Might Like:.

    star schema and snowflake schema

    All rights reserved. Exsilio Solutions is proud to introduce our new blog! Now, you can take Exsilio with you on your phone, tablet, and desktop - redefine what you thought possible! In the practice of building a data warehouse, a developer needs to go through the decision of choosing between a star or snow flake schema. These schemas pertain to who the data will be stored for. The decision on what schema to choose impacts performance, readability and maintainability so it is probably the key choice needed to be made before a data warehouse project gets underway.

    Which schema is better for performance? The Star schema is in a more de-normalized form and hence tends to be better for performance. Along the same lines the Star schema uses less foreign keys so the query execution time is limited. In almost all cases the data retrieval speed of a Star schema has the Snowflake beat. Which schema is better for readability? The Star schema is easier for readability because its query structure is not as complex, on the other hand the Snowflake has a complex query structure and is tougher for readability and implementing changes.

    The changes to be implemented can be tougher to put into a Snowflake schema because of the tendency to have a lot of joins in the query structure. The Star schema on the other hand uses less joins and tends to have more data redundancy. So for readability the schema to go with would be the star schema.

    Which schema is better for maintainability? Maintainability for a data warehouse is heavily dependent on the amount of redundant data.

    The more redundancies the more places the maintenance needs to take place. Out of the two schemas the Snowflake has the least data redundancies so is hence the more maintainable choice. Snowflake vs Star Schema Now comes a major question that a developer has to face before starting to design a data warehouse. Snowflake or Star schema? If the data is relatively small and the end result is more of a DataMart than a data warehouse, then the choice tends to lean towards Star schema.

    On the other hand, if you are building a bigger solution with many to many relationships then going with the Snowflake is your best bet. Another thing that needs to be considered is the number of dimensions in your dimension table.

    For example, a star schema would use one date dimension but a Snowflake, can have Dimension date tables that extends out to dimension day of the week, quarter, month…etc. Bad habits while implementing Snowflake and Star schemas There are three key phases to building a data warehouse: planning, implementation, and documentation.

    Planning The first stage in creating a data warehouse is planning. Sometimes, especially with deadlines, not enough time is spent on this stage, this can become a routine and is a bad habit. Besides that, there are three bad habits that tend to occur where the planning stage is done: Not enough business cases are considered, the more business cases that are considered for the data warehouse the more functional and useful a data warehouse will be. With scalability not being considered, the data warehouse may have a short shelf life and end up costing more than it helps.

    The current architecture of the data used by the company not being considered. With this some questions tend to go unanswered questions like: Is the data available? Is the data able to be consolidated? Is the current infrastructure able to handle the data warehouses load onto the network? Implementation Implementing a data warehouse is the most technical part of the process and a bad habit is having a haphazard implementation. To avoid this, developers need to pay attention to detail and follow the plan they made during the planning process to a tee.

    An extra sense of attention to detail needs to be given during the implementation process. Another bad habit that can be found during this phase is the content of the created objects, clear names should be given to the fact and dimension tables.

    Also the fields within these tables need to be clearly named this will help later down the road in making reports as well as ad-hoc querying the database. Documentation The worst mistake made in the production of a data warehouse is documentation or more accurately the lack there of.

    A data warehouse is made to be accessed to write queries from, to make reports from and to organize information. If the setup of the data warehouse is not documented, then this makes the whole process harder and not user-friendly.

    BI report developers have a harder time accessing the data and using it to make the solutions that the data warehouse was made to provide in the first place. At the same time the maintenance of the data warehouse becomes harder as the organizational structure, teams and developers change over the course of time. A simple change could take months to implement because no documentation was provided. Losing the company money, time, resources and a business decision making advantage.

    The documentation is a vital key to building a successful lasting data warehouse. Also, the documentation should thoroughly cover the ETL, from an xml standpoint as well as the servers data sources , parameters, data cleansing techniques and data matching techniques.

    Lastly it should cover any special features such as slow changing dimensions that were used. This makes it easier for developers to make changes, analysts to create reports, and the organization to see where this data warehouse fits into the scheme of their organization. All of the above are bad habits that if not considered can lead to a huge waste of time and resources and in the worst case a failed data warehouse.

    Written by Sukhmani Bains.

    Difference Between Star and Snowflake Schema

    One dimension is Product wise sales and the other dimension is Age-wise sales. The Dimensions are stored in the Dimension table. So, measures are numbers and dimensions describe those numbers. Please have a look at the following diagram for a better understanding.

    What’s on our mind?

    The Fact table has measures i. As you can see in the below image, we have four dimension tables such as Country, City, Employees, and Products. And each dimension table representing the Fact table for measure values. Check out what makes Hevo amazing: Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.

    Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations. Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency. Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time.

    This ensures efficient utilization of bandwidth on both ends. Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

    Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time. Simplify your data analysis with Hevo today!

    Sign up here for a day free trial! It is a top-down model. It is a bottom-up model. Queries execute faster in the Star Schema. This is achieved using the Star Join Query Optimization technique. Tables can be connected with Multiple Dimensions. Execution of queries takes a longer time in the Snowflake Schema than in the Star Schema. The longer execution time is also caused by the many number of foreign keys. There is no normalization in the Star Schema. In the Snowflake Schema, there is both normalization and denormalization.

    It is obvious that a lot of data is duplicated not normalized with this schema. Snowflake Schema in Data Warehouse The snowflake schema is an extension of a star schema. The main difference is that in this architecture, each reference table can be linked to one or more reference tables as well.

    The aim is to normalize the data. Look at the Products table in the previous example. The Product segment field can be repeated many times for many products. But if we create one more table, Segments, we can just reference the Products table to the Segments table using ids — foreign keys.

    STAR vs SNOWFLAKE

    The same can be done for the Customer location field in the Customers table or the Department region field in the Departments table. Here is a visualization of the snowflake schema: If there are a lot of different tables, this structure resembles a snowflake.

    It has the center fact tableand many reference tables that make up the branching, similar to what snowflakes have. Having more lookup tables allows perfect data normalization because less data is duplicated. In a star schema, all information is placed in the fact table and the lookup tables that have a direct reference to the fact table.

    In a snowflake schema, it is possible that the first-level lookup tables have their own lookup tables. So, the information is dispersed over the entire system. This is the most important difference, and what all the following conclusions are based on. Star schema results in high data redundancy and duplication. Snowflake schema ensures a very low level of data redundancy because data is normalized.

    Star schema is very simple, while the snowflake schema can be really complex. In general, there are a lot more separate tables in the snowflake schema than in the star schema. Snowflake schema uses less disk space than star schema. Benefits, Disadvantages, and Use Cases of Each of the Schemas Each schema has its own advantages, disadvantages, and recommended use cases. Benefits of the Star Schema It is extremely simple to understand and build. No need for complex joins when querying data.

    Simpler to derive business insights. Disadvantages of the Star Schema Denormalized data can cause integrity issues. This means some data can turn out to be inconsistent at times.


    Star schema vs snowflake schema differences