Schema design is a crucial aspect of data warehousing that affects how efficiently you can query and analyze your data. Different schemas have different trade-offs between storage space, data quality, and query performance. In this article, you will learn how to choose and implement two common schema designs for data warehousing: star and snowflake schemas.
Star and snowflake schemas are two types of dimensional modeling techniques that organize your data into fact and dimension tables. Fact tables store the quantitative data that you want to measure, such as sales, orders, or transactions. Dimension tables store the descriptive data that you want to filter, group, or join by, such as customer, product, or date. The difference between star and snowflake schemas is how they handle dimension tables.
While considering star and snowflake schemas in dimensional modeling, it's important to recognize a third technique: columnar storage tables. This approach allows you to create denormalized tables that perform efficiently, particularly useful for handling large datasets. Columnar storage optimizes query performance by storing table data vertically, which can be more efficient for read-heavy operations. This technique can complement or be an alternative to star and snowflake schemas, offering flexibility in how you structure and access your data warehouse, especially when dealing with complex queries and large volumes of data.
A star schema has a simple and flat structure, where each dimension table is directly connected to the fact table by a single primary-foreign key relationship. This means that each dimension table contains all the attributes that describe that dimension, even if they are redundant or hierarchical. For example, a date dimension table would have columns for year, month, day, week, quarter, and so on. A star schema is easy to understand and query, but it can also consume more storage space and require more maintenance.
Importante que, antes de aprofundar-se nos estudos de um modelo estrela, estude modelagem de dados (relacional e dimensional), bem como as técnicas existentes para modelar dados. Show? Para falarmos de um modelo estrela, você precisará entender: 1 - Modelagem de dados 2 - Atributos e entidades 3 - Dimens?o de negócio e fato 4 - Relacionamento de dados 5 - Chaves primária, estrangeira e substituta 6 - Cardinalidade 7 - Técnicas para modelagem de dados, como fato agregada, particionamento etc. Perceba que há uma gama de pontos que recomendo que você estude. O modelo estrela ainda é a melhor modelagem para um DW. Dúvidas? Chamaí!
A snowflake schema has a more normalized and complex structure, where some dimension tables are further split into sub-dimension tables by removing redundant or hierarchical attributes. This means that each dimension table contains only the most granular attributes that describe that dimension, and the rest are stored in separate tables that are linked by multiple primary-foreign key relationships. For example, a date dimension table would only have a column for day, and the other columns would be in sub-dimension tables for month, quarter, and year. A snowflake schema is more efficient and consistent, but it can also make queries more complicated and slower.
The choice between star and snowflake schemas depends on several factors, such as the size and complexity of your data, the frequency and type of your queries, and the performance and scalability of your data warehouse platform. Generally speaking, a star schema is more suitable for smaller and simpler data sets, where query speed and simplicity are more important than storage space and data quality. A snowflake schema is more suitable for larger and more complex data sets, where storage space and data quality are more important than query speed and simplicity.
To implement star and snowflake schemas, you need to follow some basic steps. First, you need to identify the facts and dimensions that are relevant to your business goals and analytical needs. Second, you need to design the structure and relationships of your fact and dimension tables, following the rules of star or snowflake schemas. Third, you need to populate your tables with data from your source systems, using ETL (extract, transform, load) processes or tools. Fourth, you need to test and optimize your tables and queries, using indexes, partitions, statistics, or other techniques.
To measure query performance, you need to use some metrics and tools that can help you evaluate and compare the efficiency and effectiveness of your queries. Some common metrics are query execution time, query throughput, query cost, and query accuracy. Some common tools are query analyzers, query planners, query monitors, and query logs. By measuring query performance, you can identify and resolve any issues or bottlenecks that affect your data warehousing schema design and query optimization.
A poorly designed schema can significantly hurt database performance and query execution speed. Inefficiently organized data consume lots of system resources. They are also very confusing and hard to maintain by database administrators. This is why designing a good schema is important. Some of the benefits of well-designed schemas include: Making your data easy to understand, analyze and interpret. Reducing and eliminating data redundancy. Preventing data inconsistency and inaccuracy Improving data access and query response time. Ensuring data security Ensuring data integrity and correctness.
Modelar dados envolve compreens?o das necessidades de negócio, sendo assim recomendo que você estudo Engenharia de requisitos, antes mesmo de estudar modelagem de dados para DW. Esse campo de estudo científico, vai te apresentar técnicas e processos detalhados para você mapear requisitos funcionais e n?o funcionais (desempenho, por exemplo), de modo que você possa projetar sua arquitetura de solu??o/dados e, consequente, modelo de dados. Dúvidas? Chamaí!