When building a data warehouse, SQL is the core tool for structural design, modeling, and ETL processes. 1. Commonly used models include star models (suitable for quick queries) and snowflake models (save storage but increase complexity), selected based on performance or storage requirements. 2. In the slow-changing dimension processing method, Type 2 is most commonly used to preserve history, and it is recommended to use this type for key attributes. 3. The ETL process should be processed in stages, avoid full table scanning, prioritize incremental updates, and use temporary tables or CTEs to improve readability. 4. Aggregation tables and materialized views can be used to accelerate query. The former is flexible in manual control, while the latter relies on automatic database maintenance. It is recommended to aggregate high-frequency dimension combinations in advance and refresh them regularly. Mastering these core concepts and best practices can help improve the design and maintenance efficiency of data warehouses.

SQL is one of the most commonly used tools when building a data warehouse. It is not only used for querying and analysis, but also the basis of the entire data structure, modeling and ETL process. If you are a developer or analyst who is new to data warehouses, understanding some core concepts and best practices can help you avoid many detours.

1. The basic structure of data warehouse: star model and snowflake model

The two most common models in data warehouses are the Star Schema and the Snowflake Schema .
The star model consists of a Fact Table and multiple dimension tables. It has a simple structure and is suitable for fast querying.
The snowflake model further normalizes the dimension table based on star shapes, saving storage space but may increase query complexity.

• When to use star shape? Query performance is preferred, such as reporting systems.
• When to use snowflakes? Storage efficiency is more important, or requires more complex hierarchical relationship management.

For example: In the sales data warehouse, order information is placed in the fact table, and information such as customers, products, time is connected as dimension tables.

2. Slow change dimension (SCD) processing method

In a data warehouse, the dimension data will change over time, such as customer address changes and product price adjustments. How to record these changes is an issue that must be considered when designing a dimension table.

Common SCD types are:

• Type 0: Keep the original value (not commonly used)
• Type 1: Overwrite old values (simple but lost history)
• Type 2: Add record retention history (most common, support trend analysis)
• Type 3: Add fields to record part of history

suggestion:

• Use Type 2 for dimensions related to key business indicators, such as customer status, product classification, etc.
• Unimportant properties can use Type 1 to simplify the model.

3. SQL best practices in ETL process

ETL (extraction, transformation, loading) is the core process of data warehouses, and SQL is the main language to implement this process. In order to ensure efficiency and maintainability, the following points need to be paid attention to:

• Phase processing : first do data cleaning, then do aggregation calculation, and finally load it into the target table.
• Avoid full table scanning : Use indexes reasonably, especially when large table associations.
• Incremental updates are better than full replacement : especially when only a part of the data is updated every day.
• Organize logic using temporary tables or CTEs : Improve code readability and debugging efficiency.

To give a small example: If you want to count the total sales of a certain day, don’t write it into a string of nested subqueries from the beginning, but first extract the orders of the day and then summarize them. It is clear and easy to troubleshoot errors.

4. Tips for using aggregate tables and materialized views

As the data volume grows, it may become slow to directly query the original fact table. At this time, it is necessary to introduce an aggregate table or a materialized view to speed up the query.

The difference is:

• Aggregation tables : manually created and refreshed regularly, with flexible control.
• Materialized view : Automatic database maintenance and rely on platform support (such as Oracle, PostgreSQL).

suggestion:

• Aggregate the dimension combination of high-frequency queries (such as "Sales by Region Monthly").
• Set the timing task to refresh the aggregated data regularly.
• Pay attention to data freshness requirements. Some scenarios cannot accept data with too long delays.

Basically that's all. SQL plays a very basic and critical role in data warehouses. Mastering these concepts and practices can help you better design and maintain data structures.

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