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What are the common challenges and pitfalls of snowflake schema implementation?

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Snowflake schema is a type of star schema that normalizes the dimension tables to reduce redundancy and improve data quality. It is often used in online analytical processing (OLAP) systems that perform complex queries and aggregations on large volumes of data. However, snowflake schema implementation also comes with some challenges and pitfalls that you should be aware of before deciding to use it. In this article, you will learn about some of the common issues and drawbacks of snowflake schema, such as:

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Olivier Refalo

Architecture, Technology and Leadership

1 Query complexity

One of the main disadvantages of snowflake schema is that it increases the query complexity and the number of joins required to retrieve data from multiple dimension tables. This can affect the performance and readability of your queries, especially if you have many levels of normalization and hierarchies in your dimensions. You may need to write longer and more complicated SQL statements or use views and stored procedures to simplify your queries.

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2 Data loading

Another challenge of snowflake schema is that it requires more time and effort to load data into the normalized dimension tables. You have to extract, transform, and load (ETL) data from multiple sources and ensure that the foreign keys and references are consistent and valid across the tables. You may also need to update or delete data from multiple tables if there are any changes or errors in the source data. This can increase the data loading complexity and latency, as well as the risk of data inconsistency and integrity issues.

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3 Schema maintenance

A third pitfall of snowflake schema is that it makes the schema maintenance more difficult and costly. You have to design and implement the snowflake schema carefully and ensure that it meets the business and analytical requirements. You also have to monitor and optimize the schema performance and quality regularly and handle any changes or modifications in the data sources or the business logic. You may need to restructure or denormalize the schema if the query performance or the data analysis needs change over time.

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Olivier Refalo

Architecture, Technology and Leadership
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I don't get this point, how is this different from a regular DWH? Schema changes is an issue no matter the vendor and solution.

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4 User accessibility

A fourth drawback of snowflake schema is that it reduces the user accessibility and usability of the data. The snowflake schema is more complex and less intuitive than the star schema, which can make it harder for the end users or the business analysts to understand and explore the data. You may need to provide more training and documentation for the users to use the snowflake schema effectively and efficiently. You may also need to use tools or applications that can simplify or abstract the snowflake schema for the users.

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5 Data redundancy

A fifth issue of snowflake schema is that it does not eliminate all the data redundancy in the dimension tables. Although the snowflake schema normalizes the dimension tables, it still retains some level of denormalization for the attributes that are frequently used or queried. This can result in some data duplication and storage overhead, as well as potential data quality problems if the data is not synchronized or updated properly.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the common challenges and pitfalls of snowflake schema implementation?

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1 Query complexity

2 Data loading

3 Schema maintenance

4 User accessibility

5 Data redundancy

6 Here’s what else to consider

OLAP

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