Star Schema 1. Compare and contrast the star schema and snowflake schema. What are the components of a star schema? Describe the relationship between dimension and fact tables. OLAP vs OLTP 2. What are some differences between operational systems and data warehouses? How is the data different?
1. The star schema and snowflake schema are two commonly used design patterns in the field of data warehousing. Both schemas are used to structure and organize data in a way that supports efficient querying and analysis. While they serve the same purpose, there are key differences between the two.
The star schema is a simpler and more denormalized design compared to the snowflake schema. In a star schema, the center of the schema is a large fact table that contains the primary measures or metrics of interest, such as sales revenue or customer orders. The fact table is surrounded by a set of dimension tables, which provide additional descriptive information about the measures in the fact table. Each dimension table contains a primary key, which is used to establish relationships with the fact table. These relationships create the “star” shape, with the fact table at the center and the dimension tables radiating out from it.
In contrast, the snowflake schema is a more normalized design that allows for greater data granularity and flexibility. In a snowflake schema, the dimension tables are structured in a hierarchical manner, with multiple levels of relationships between the tables. This provides a more complex and detailed representation of the data but can result in more joins and potentially slower query performance compared to the star schema.
The relationship between the dimension and fact tables is crucial in both schemas. The dimension tables provide context and descriptive attributes to the measures in the fact table. They can include information such as time, location, product, or customer details. By establishing the relationships between the dimension and fact tables, queries can be performed that combine and aggregate data from the fact table while utilizing the descriptive attributes from the dimension tables. This allows for multidimensional analysis and slicing and dicing of data across different dimensions.
2. Operational systems and data warehouses serve different purposes and have distinct characteristics. Operational systems are designed to support day-to-day business operations and transactions. They are focused on capturing and processing real-time data, such as customer orders, inventory updates, or financial transactions. The primary goal of operational systems is to facilitate the functioning and efficiency of business processes.
Data warehouses, on the other hand, serve as repositories for historical and aggregated data. They are designed to support analytical and reporting needs by providing a consolidated and unified view of the organization’s data. Data in a warehouse is typically structured differently than in operational systems, with a focus on denormalization and optimized query performance. Data warehouses often incorporate data from multiple operational systems, transforming and integrating it to provide a comprehensive view of the organization’s operations.
The data in operational systems is frequently subject to updates, inserts, and deletions as new transactions occur. In contrast, the data in a data warehouse is typically non-volatile and focuses on read-only operations. Once data is loaded into the warehouse, it is usually not modified, but rather transformed or aggregated to provide insights and support analytical queries.
Another difference is the level of detail and granularity of the data. Operational systems often store granular data, capturing every transaction or event in detail. Data warehouses, however, tend to store aggregated and summarized data at various levels of granularity. This allows for efficient querying and analysis by eliminating unnecessary detail and providing pre-calculated aggregations for quick retrieval.
In summary, operational systems are designed for transactional processing in real-time, while data warehouses focus on analytical processing and historical data consolidation. The data in operational systems and data warehouses varies in structure, volatility, and level of detail, reflecting their unique purposes and requirements.
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