What is Star Schema?PlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
 
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWStar SchemaAll>Star SchemaTry Databricks for freeGet StartedWhat 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.Introduced by Ralph Kimball in the 1990s, star schemas are efficient at storing data, maintaining history, and updating data by reducing the duplication of repetitive business definitions, making it fast to aggregate and filter data in the data warehouse. Fact tables and dimension tablesA star schema is used to denormalize business data into dimensions (like time and product) and facts (like transactions in amounts and quantities).A star schema has a single fact table in the center, containing business "facts" (like transaction amounts and quantities). The fact table connects to multiple other dimension tables along "dimensions" like time, or product. Star schemas enable users to slice and dice the data however they see fit, typically by joining two or more fact tables and dimension tables together.Denormalized dataStar schemas denormalize the data, which means adding redundant columns to some dimension tables to make querying and working with the data faster and easier. The purpose is to trade some redundancy (duplication of data) in the data model for increased query speed, by avoiding computationally expensive join operations.In this model, the fact table is normalized but the dimensions tables are not. That is, data from the fact table exists only on the fact table, but dimensional tables may hold redundant data.Benefits of star schemasFact/dimensional models like star schemas are simple to understand and implement, and make it easy for end users to find the data they need. They can be applied to data marts and other data resources.Great for simple queries because of their reduced dependency on joins when accessing the data, as compared to normalized models like snowflake schemas.Adapt well to fit OLAP models.Improved query performance as compared to normalized data, because star schemas attempt to avoid computationally expensive joins.How does a star schema differ from 3NF (Third Normal Form)?3NF, or Third Normal Form, is a method of reducing data-redundancy through normalization. It is a common standard for databases that are considered fully normalized. It typically has more tables than a star schema due to data normalization. On the flip-side, queries tend to be more complex due to the increased number of joins between large tables.ResourcesDatabricks Blog: Five Simple Steps for Implementing a Star Schema in Databricks with Delta LakeDatabricks SQL product pageDatabricks glossary - Snowflake schema Back to GlossaryLearn what’s driving the Lakehouse pattern.REGISTER NOWProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights