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Different Data Warehouse Schemas

Solutions Review’s Tim King created this introductory resource to highlight the different data warehouse schemas for your reference.

Enterprise Data Warehouse (EDW) schemas define the logical structure and organization of data within a data warehouse. These schemas determine how data is stored, related, and accessed, facilitating efficient querying and reporting. Here are some of the different types of EDW schemas:

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  1. Star Schema:
    • In a star schema, the data warehouse is organized into a central fact table surrounded by dimension tables.
    • The fact table contains quantitative data (measures or metrics), and each dimension table contains descriptive attributes.
    • The dimension tables are connected to the fact table through foreign key relationships.
    • Star schemas are widely used for their simplicity and efficiency in supporting business intelligence queries.
  2. Snowflake Schema:
    • A snowflake schema is an extension of the star schema.
    • In this schema, dimension tables are normalized into sub-dimensions, reducing data redundancy.
    • Normalization helps save storage space and simplifies maintenance but may result in more complex queries.
  3. Galaxy Schema (Fact Constellation):
    • A galaxy schema, also known as a fact constellation, includes multiple fact tables that share dimension tables.
    • This schema is suitable for scenarios where data marts have their own fact tables, but they share some common dimensions.
    • It supports complex analytical queries involving multiple fact tables.
  4. Multidimensional Schema (OLAP Cube):
    • Multidimensional schemas are often used in Online Analytical Processing (OLAP) systems.
    • Data is organized into cubes, where each dimension represents an aspect of the data.
    • Cubes allow for multi-dimensional analysis, making them suitable for complex reporting and analysis.
  5. Hybrid Schema:
    • A hybrid schema combines elements of star, snowflake, and other schema types to meet specific business requirements.
    • It provides flexibility in designing the schema to optimize both storage and query performance.
  6. Data Vault Schema:
    • The Data Vault schema is a modeling approach focused on capturing and storing raw data in a highly flexible and scalable way.
    • It consists of three main types of tables: hubs (for business keys), links (for relationships), and satellites (for attributes).
    • Data Vault schemas are designed for data integration and historical tracking of changes, making them suitable for data warehouse environments with rapidly changing source data.
  7. Anchor Schema:
    • An anchor schema is used in data warehouses where multiple fact tables share dimension tables.
    • It uses anchor dimensions to simplify the relationship between fact tables and shared dimensions.
    • This schema helps reduce complexity in querying shared dimensions.
  8. No Schema (Schemaless):
    • Some data lakes and modern data warehousing systems may use a schemaless approach.
    • In a schemaless architecture, data is ingested in its raw form without predefined structures.
    • Schema-on-read is applied when querying the data, allowing for flexibility in handling unstructured or semi-structured data.

The choice of an EDW schema depends on factors such as the organization’s data modeling needs, query performance requirements, and data complexity. Star and snowflake schemas are common choices for their balance of simplicity and efficiency, but other schemas, such as galaxy schemas or Data Vault schemas, may be selected for more specialized use cases. Hybrid approaches and schemaless architectures are also gaining popularity as organizations seek flexibility in handling diverse data types and use cases within their data warehousing environments.

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