How do you update and maintain a snowflake schema over time?

1 Design your schema

The first step to update and maintain a snowflake schema is to design it properly. You should identify the business requirements, the key performance indicators, the granularity, and the dimensions of your data. You should also consider the trade-offs between normalization and denormalization, and the impact on query speed and storage space. You should use a data modeling tool, such as ERwin, PowerDesigner, or SQL Server Data Tools, to create a logical and physical model of your schema, and document the metadata, relationships, and constraints.

2 Implement your schema

The next step is to implement your schema in a data warehouse or a data mart. You should use a data integration tool, such as Informatica, SSIS, or Talend, to extract, transform, and load (ETL) data from various sources into your schema. You should also use a data quality tool, such as DataFlux, Trillium, or SSIS, to validate, cleanse, and standardize your data. You should follow the naming conventions, data types, and formats of your schema, and ensure that the data is consistent and accurate.

3 Monitor your schema

The third step is to monitor your schema regularly, and check for any errors, anomalies, or changes. You should use a data profiling tool, such as DataFlux, SSIS, or Talend, to analyze the structure, content, and distribution of your data. You should also use a data auditing tool, such as Pentaho, SSIS, or Talend, to track the history, lineage, and usage of your data. You should review the performance, security, and compliance of your schema, and identify any issues or opportunities for improvement.

4 Modify your schema

The fourth step is to modify your schema as needed, based on the feedback, requirements, or trends of your business. You should use a data modeling tool, such as ERwin, PowerDesigner, or SQL Server Data Tools, to make changes to your schema, such as adding, deleting, or modifying tables, columns, or relationships. You should also use a data integration tool, such as Informatica, SSIS, or Talend, to update the ETL processes and the data quality rules accordingly. You should test the impact of your changes on the data and the queries, and deploy them carefully.

5 Optimize your schema

The fifth step is to optimize your schema periodically, and enhance its performance and efficiency. You should use a data warehouse tuning tool, such as SQL Server Management Studio, Oracle Enterprise Manager, or Teradata Administrator, to analyze the workload, the query plan, and the statistics of your schema. You should also use a data warehouse optimization tool, such as SQL Server Analysis Services, Oracle OLAP, or Teradata Aggregate Designer, to create and manage aggregates, indexes, partitions, and views. You should apply the best practices and techniques for optimizing your schema, such as using surrogate keys, avoiding snowflaking too much, and balancing the trade-offs between query speed and storage space.

6 Document your schema

The sixth step is to document your schema thoroughly, and communicate it to your stakeholders and users. You should use a data dictionary tool, such as ERwin, PowerDesigner, or SQL Server Data Tools, to create and maintain a comprehensive and consistent documentation of your schema, including the metadata, definitions, descriptions, and business rules. You should also use a data visualization tool, such as Tableau, Power BI, or QlikView, to create and share dashboards, reports, and charts that illustrate the insights and value of your schema. You should update and review your documentation regularly, and ensure that it is accessible and understandable.

7 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?