What is a star schema?
A star schema is a multi-dimensional data model used to organize data in a database so that it is easy to understand and analyze. Star schemas can be applied to data warehouses, databases, data marts, and other tools. The star schema design is optimized for querying large data sets.
What is the difference between normalized and Star schemas?
Normalized models allow any kind of analytical query to be executed, so long as it follows the business logic defined in the model. Star schemas tend to be more purpose-built toward a particular view of the data, thus not really allowing more complex analytics. Star schemas don't easily support many-to-many relationships between business entities.
What is a type 1 ScD in star schema design?
Star schema design theory refers to two common SCD types: Type 1 and Type 2. A dimension-type table could be Type 1 or Type 2, or support both types simultaneously for different columns. A Type 1 SCD always reflects the latest values, and when changes in source data are detected, the dimension table data is overwritten.
What is the most consistent table in a star schema?
The most consistent table you'll find in a star schema is a date dimension table. A dimension table contains a key column (or columns) that acts as a unique identifier, and descriptive columns.
Is star schema in 3NF?
A star schema model is designed with the following in mind: Redundant data storage for performance: Data is stored in significantly fewer tables than a typical transactional database, which are NOT in 3NF which means columns in a table contains data which is repeated throughout the table.
Is star schema normalized or denormalized?
Star schema's dimension tables do not contain any foreign keys. That is, the dimension tables do not reference any other tables, nor do they have any "sub-dimension tables." They are generally denormalized because some information may be duplicated in the dimension tables.
Does star schema use normalization?
Star schema dimension tables are not normalized, snowflake schemas dimension tables are normalized. Snowflake schemas will use less space to store dimension tables but are more complex.
What normal form is snowflake schema?
third normal formTables in a snowflake schema are usually normalized to the third normal form. Each dimension table represents exactly one level in a hierarchy.
What is 3NF in SQL?
Third normal form (3NF) is a database schema design approach for relational databases which uses normalizing principles to reduce the duplication of data, avoid data anomalies, ensure referential integrity, and simplify data management. It was defined in 1971 by Edgar F.
Is snowflake schema normalized or denormalized?
Normalization of dimension tables The snowflake schema is a fully normalized data structure. Dimensional hierarchies (such as city > country > region) are stored in separate dimensional tables. On the other hand, star schema dimensions are denormalized.
What level of normalization is data model?
These steps are called normalization rules. Each rule is referred to as a normal form (1NF, 2NF, 3NF). The first three forms are the most important ones. There are more than 3 normal forms but those forms are rarely used and can be ignored without resulting in a non flexible data model.
Are fact tables normalized or denormalized?
A fact table is the central table in a star schema of a data warehouse. A fact table stores quantitative information for analysis and is often denormalized. A fact table works with dimension tables.
Is dimensional model normalized or denormalized?
Dimensional models are more denormalized and optimized for data querying, while normalized models seek to eliminate data redundancies and are optimized for transaction loading and updating.
Is snowflake a 3NF?
In the snowflake schema, dimension tables are normally in the third normal form (3NF). The snowflake schema helps save storage however it increases the number of dimension tables.
What is the difference between 3NF and star schema?
Third normal form modeling is a classical relational-database modeling technique that minimizes data redundancy through normalization. When compared to a star schema, a 3NF schema typically has a larger number of tables due to this normalization process.
What is difference between snowflake and star schema?
A star schema contains both dimension tables and fact tables in it. A snowflake schema contains all three- dimension tables, fact tables, and sub-dimension tables. It is a top-down model type. It is a bottom-up model type.
What is difference between snowflake and star schema?
A star schema contains both dimension tables and fact tables in it. A snowflake schema contains all three- dimension tables, fact tables, and sub-dimension tables. It is a top-down model type. It is a bottom-up model type.
Is dimensional model normalized or denormalized?
Dimensional models are more denormalized and optimized for data querying, while normalized models seek to eliminate data redundancies and are optimized for transaction loading and updating.
Are fact tables normalized or denormalized?
A fact table is the central table in a star schema of a data warehouse. A fact table stores quantitative information for analysis and is often denormalized. A fact table works with dimension tables.
Why is snowflake schema normalized?
The main difference between star schema and snowflake schema is that the dimension table of the snowflake schema is maintained in the normalized form to reduce redundancy. The advantage here is that such tables (normalized) are easy to maintain and save storage space.
Why are star schemas not well enforced?
Another disadvantage is that data integrity is not well-enforced due to its denormalized state.
Why is the star schema important?
The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries. The star schema gets its name from the physical model's resemblance to a star shape with a fact table at its center and the dimension tables surrounding it representing the star's points.
What are the benefits of star schema?
Star schemas are denormalized, meaning the typical rules of normalization applied to transactional relational databases are relaxed during star-schema design and implementation. The benefits of star-schema denormalization are: 1 Simpler queries – star-schema join-logic is generally simpler than the join logic required to retrieve data from a highly normalized transactional schema. 2 Simplified business reporting logic – when compared to highly normalized schemas, the star schema simplifies common business reporting logic, such as period-over-period and as-of reporting. 3 Query performance gains – star schemas can provide performance enhancements for read-only reporting applications when compared to highly normalized schemas. 4 Fast aggregations – the simpler queries against a star schema can result in improved performance for aggregation operations. 5 Feeding cubes – star schemas are used by all OLAP systems to build proprietary OLAP cubes efficiently; in fact, most major OLAP systems provide a ROLAP mode of operation which can use a star schema directly as a source without building a proprietary cube structure.
What are the disadvantages of star schema?
The main disadvantage of the star schema is that it's not as flexible in terms of analytical needs as a normalized data model. Normalized models allow any kind of analytical query to be executed, so long as it follows the business logic defined in the model.
What is snapshot fact table?
Snapshot fact tables record facts at a given point in time (e.g., account details at month end)
Which is simpler, join logic or star schema?
Simpler queries – star-schema join-logic is generally simpler than the join logic required to retrieve data from a highly normalized transactional schema.
Why do fact tables have surrogate keys?
Fact tables are generally assigned a surrogate key to ensure each row can be uniquely identified. This key is a simple primary key.
Consumability: Bronze, Silver, Gold
One main goal underpins most data transformation and integration activity: to make data more easily and reliably consumable by end users. Consumability is a primary axis of variation between types of data models, and it’s a helpful way to differentiate them. We often use a bronze/silver/gold framework to characterize levels of consumability.
Data models: Nouns and Verbs
The key aspect of gold standard consumability is that everyone is clear what the data is. How it got there is of no concern to the consumer. If reports are wrong, or insights are misleading, business users will not be conciliated to hear that you used the latest and greatest technology, or that the runtimes were exceptionally fast and well tuned.
Raw data models
The raw phase is all about capturing whatever data has been supplied. It might have a well defined data model, whether documented or not. Or it might be semi-structured or even unstructured, in which case you will need to work out how to make the format more accessible.
ODS data models
An Operational Data Store (ODS) is modeled in a very similar way to a staging area. In an ODS, the data model is also a copy of the source system.
Third Normal Form (3NF) data models
A Third Normal Form area in a data warehouse is where real data integration can begin. The aim is to store data independently of the vagaries of any particular source system.
Data Vault models
Every Data Vault model is also a third normal form model. So all of the features and comments in the previous 3NF section also apply to Data Vault. There are some extra rules in Data Vault to help with long term flexibility and maintainability. This makes Data Vault an even better choice as the data model for large scale data integration.
Aggregated data models
Aggregates don’t add anything that does not already exist in the source data. Like dimensional models, they are an attempt to deliver data in the simplest, most consumable way.
What is measure in star schema?
In star schema design, a measure is a fact table column that stores values to be summarized.
What is a snowflake dimension?
Categories are assigned to subcategories, and products are in turn assigned to subcategories. In the Adventure Works relational data warehouse, the product dimension is normalized and stored in three related tables: DimProductCategory, DimProductSubcategory, and DimProduct.
Why is a table a surrogate key?
The table must also define a surrogate key because the business key (in this instance, employee ID) won't be unique. It's important to understand that when the source data doesn't store versions, you must use an intermediate system (like a data warehouse) to detect and store changes.
Is a degenerate dimension table good?
However, if the Adventure Works resellers sales table has order number and order line number columns, and they're required for filtering, a degenerate dimension table would be a good design. For more information, see One-to-one relationship guidance (Degenerate dimensions).
Is optimal model design part science?
Lastly, it's important to understand that optimal model design is part science and part art. Sometimes you can break with good guidance when it makes sense to do so. There are many additional concepts related to star schema design that can be applied to a Power BI model. These concepts include: Measures.
Does a factless fact table include measure columns?
A factless fact table doesn't include any measure columns. It contains only dimension keys.
What is star schema?
Star schema is an approach of arranging a database into fact tables and dimension tables. Typically a fact table records a series of business events such as purchase transactions. Dimension tables generally store fewer records than fact tables but may have more specific details about a particular record. A product attributes table is one example.
Why do star schemas skip normalization?
Star schemas often skip normalization for two reasons: simplicity of queries and performance.
What is the first normal form?
First normal form specifies that table values should not be divisible into smaller parts and that each cell in a table should contain a single value.
What is the goal of getting to third normal form?
The goal of getting to third normal form is to eliminate update, insertion, and deletion anomalies.
Is it possible or desirable to merge normalization and star schemas?
Is it possible or desirable to merge normalization and star schemas? Sure.
What is star schema?
Star schema is an approach of arranging a database into fact tables and dimension tables. Typically a fact table records a series of business events such as purchase transactions. Dimension tables generally store fewer records than fact tables but may have more specific details about a particular record.
Why do star schemas skip normalization?
Star schemas often skip normalization for two reasons: simplicity of queries and performance.
What is the first normal form?
First normal form specifies that table values should not be divisible into smaller parts and that each cell in a table should contain a single value. So if we had a customer table with a requirement to store multiple phone numbers, the simplest method would be like this. customer_id.
What is the goal of getting to third normal form?
The goal of getting to third normal form is to eliminate update, insertion, and deletion anomalies.
Is it possible or desirable to merge normalization and star schemas?
Is it possible or desirable to merge normalization and star schemas? Sure.
Why do we need to load data into a star schema?
If the data is very dirty or the structure of the data needs transformation before it works well for analysis, then making the extra step of loading the data into a physical star schema starts to make a lot of sense.
What is 3NF schema?
1) Normally, 3NF schema is typical for ODS layer, which is simply used to fetch data from sources, generalize, prepare, cleanse data for upcoming load to data warehouse.
Does star schema make surface data easier?
You will probably find many opinions on this question. Here is mine. If you ultimately going to surface data through cubes (SSAS), a star schema will make that process much easier. If you have a 3NF data warehouse, you will still have some work to do in order to surface this data to users/applicatoins/whatever in order to make the data easier queriable.
Can you use Star Schema on Inmon?
Now it is common to create Star Schema data marts on top of 3NF data warehouse in Inmon approach too. It will be used for SQL based reports to simplify their development and improve performance.
Can you create a star in a DSV?
You certainly could create a star (or a snowflake) design in a DSV and forego the step of creating a physical star/snowflake data mart/data warehouse. If your data is very, very clean and needs no transformations from the 3NF source to the cube, then this works reasonably OK.
Can SSAS be used on top of a star schema?
In my experience implementing an SSAS solution on top of a clean, disciplined star schema can be very easy and quick to do, while at the other end of the spectrum doing the same against a very messy 3NF OLTP data (e.g. orphaned records, poor data typing, multi-key string joins between tables, bridging across 5 outer joins to pull in all required data elements, etc.) can take 10X longer, and the cube performance can be much worse (DW surrogate key integer joins vs. multiple column string joins, for example).
What is Kimball's recommendation for star schemas?
Kimball recommended star schemas in his book [1]. He stated that ease-of-use and higher query performance delivered by the star schema outweighed the storage efficiencies provided by the snowflake schema. His other book [2] indicated fact tables were typically normalized to the third normal form (3NF) and dimension tables are in the second normal form (2NF), or possibly in third normal form (3NF).
Who advocated the star schema?
Kimball advocated the star schema and provided six reasons in his book [1];
What is normalized relation?
Informally, a relational database relation is often described as "normalized" if it meets 3NF [6]. We use data collected in an invoice [7] from Acme Industries, a fictitious company, to go through the normalization process. This is a simplified example used to explain normalization and is not enough for a real-world application.
What is database normalization?
Database normalization is the process of analyzing given relation schemas based on their functional dependencies and primary keys to minimize redundancy and update anomalies.
What is denormalization in data retrieval?
Denormalization is the process of transforming higher normal forms to lower normal forms via storing the join of higher normal form relations as a base relation. Denormalization increases the performance in data retrieval at cost of bringing update anomalies to a database.
What is relation schema?
A relation schema consists of the name of a relation followed by a list of its attributes. A database schema, which is composed of many relation schemas and connections between relations, represents the logical view of a database.
Can a dimension table be 1NF?
Dimension tables are usually in 2NF and possibly in 3NF, but they cannot be in 1NF. A table in 1NF has partial dependencies, which indicates different entities are mixed into the same dimension table, as demonstrated in Table 2. This introduces the insert anomaly. For example, we cannot add new products to the table if the products have not been sold.
Overview
In computing, the star schema is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. The star schema consists of one or more fact tables referencing any number of dimension tables. The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries.
Model
The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes related to fact data. Examples of fact data include sales price, sale quantity, and time, distance, speed and weight measurements. Related dimension attribute examples include product models, product colors, product sizes, geographic locations, and salesperson names.
Benefits
Star schemas are denormalized, meaning the typical rules of normalization applied to transactional relational databases are relaxed during star-schema design and implementation. The benefits of star-schema denormalization are:
• Simpler queries – star-schema join-logic is generally simpler than the join logic required to retrieve data from a highly normalized transactional schema.
Disadvantages
The main disadvantage of the star schema is that it's not as flexible in terms of analytical needs as a normalized data model. Normalized models allow any kind of analytical query to be executed, so long as it follows the business logic defined in the model. Star schemas tend to be more purpose-built toward a particular view of the data, thus not really allowing more complex analytics. Star schemas don't easily support many-to-many relationships between business entit…
Example
Consider a database of sales, perhaps from a store chain, classified by date, store and product. The image of the schema to the right is a star schema version of the sample schema provided in the snowflake schema article.
Fact_Sales is the fact table and there are three dimension tables Dim_Date, Dim_Store and Dim_Product.
See also
• Data warehouse
• Online analytical processing
• Reverse star schema
• Snowflake schema
• Fact constellation
External links
• Stars: A Pattern Language for Query Optimized Schema
• Fact constellation schema
Star Schema Overview
Normalization vs. Denormalization
- To understand some star schema concepts described in this article, it's important to know two terms: normalization and denormalization. Normalization is the term used to describe data that's stored in a way that reduces repetitious data. Consider a table of products that has a unique key value column, like the product key, and additional columns describing product characteristics, in…
Star Schema Relevance to Power Bi Models
- Star schema design and many related concepts introduced in this article are highly relevant to developing Power BI models that are optimized for performance and usability. Consider that each Power BI report visual generates a query that is sent to the Power BI model (which the Power BI service calls a dataset). These queries are used to filter, group, and summarize model data. A w…
Measures
- In star schema design, a measureis a fact table column that stores values to be summarized. In a Power BI model, a measure has a different—but similar—definition. It's a formula written in Data Analysis Expressions (DAX) that achieves summarization. Measure expressions often leverage DAX aggregation functions like SUM, MIN, MAX, AVERAGE, etc. to produce a scalar value result …
Surrogate Keys
- A surrogate keyis a unique identifier that you add to a table to support star schema modeling. By definition, it's not defined or stored in the source data. Commonly, surrogate keys are added to relational data warehouse dimension tables to provide a unique identifier for each dimension table row. Power BI model relationships are based on a single unique column in one table, whic…
Snowflake Dimensions
- A snowflake dimension is a set of normalized tables for a single business entity. For example, Adventure Works classifies products by category and subcategory. Categories are assigned to subcategories, and products are in turn assigned to subcategories. In the Adventure Works relational data warehouse, the product dimension is normalized and stored in three related table…
Slowly Changing Dimensions
- A slowly changing dimension (SCD) is one that appropriately manages change of dimension members over time. It applies when business entity values change over time, and in an ad hoc manner. A good example of a slowly changing dimension is a customer dimension, specifically its contact detail columns like email address and phone number. In contrast, some dimensions are …
Role-Playing Dimensions
- A role-playing dimensionis a dimension that can filter related facts differently. For example, at Adventure Works, the date dimension table has three relationships to the reseller sales facts. The same dimension table can be used to filter the facts by order date, ship date, or delivery date. In a data warehouse, the accepted design approach is to define a single date dimension table. At qu…
Junk Dimensions
- A junk dimensionis useful when there are many dimensions, especially consisting of few attributes (perhaps one), and when these attributes have few values. Good candidates include order status columns, or customer demographic columns (gender, age group, etc.). The design objective of a junk dimension is to consolidate many "small" dimensions into a single dimension …
Degenerate Dimensions
- A degenerate dimension refers to an attribute of the fact table that is required for filtering. At Adventure Works, the reseller sales order number is a good example. In this case, it doesn't make good model design sense to create an independent table consisting of just this one column, because it would increase the model storage size and result in Fieldspane clutter. In the Power …