Last updated on 2024年8月15日

How do you document and communicate your snowflake schema design to stakeholders?

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Snowflake schema is a type of data modeling technique that organizes data into multiple tables with many-to-one relationships. It is often used for analytical purposes, as it can reduce data redundancy and improve query performance. However, designing a snowflake schema can be complex and challenging, especially when you need to communicate your design to stakeholders who may not be familiar with the technical details. In this article, you will learn how to document and communicate your snowflake schema design to stakeholders effectively and efficiently.

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1 Define the scope and objectives

Before you start designing your snowflake schema, you need to define the scope and objectives of your project. What are the business questions that you want to answer with your data? What are the data sources and quality requirements? Who are the stakeholders and what are their expectations and needs? By clarifying these aspects, you can align your design with the project goals and avoid unnecessary complications or changes later on.

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Keith Belanger

SqlDBM Product Evangelist ??Datafluencer ?? ??Data Modeling Aficionado ?? Snowflake Data Superhero????Certified Data Vault 2.0 Practitioner ??Sr Data Leader
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In my experience it's essential to use a comprehensive data modeling solution, such as SqlDBM. Regardless of the design type, including Star, Snowflake, Data Vault, and 3NF. These tools capture of all facets of your design, ranging from logical business perspectives to physical technical details. With the inclusion of definitions, descriptions, and relevant metadata. This approach ensures that all aspects of the model are clearly represented, making ideal platform for effective communication with all stakeholders. These solutions make ensuring everyone involved has a clear understanding of the design.

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2 Choose the dimensions and facts

The next step is to choose the dimensions and facts that will form the basis of your snowflake schema. Dimensions are the descriptive attributes of your data, such as customer, product, or location. Facts are the numerical measures of your data, such as sales, revenue, or profit. You need to identify the key dimensions and facts that are relevant to your analysis and business questions. You also need to decide how to handle the hierarchies and levels of detail within each dimension, as these will affect the structure and complexity of your snowflake schema.

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Carlos Fernando Chicata

Algunas insignias de community Top Voice | Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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In my opinion, the documentation must indicate these 3 points about chosen decision on fact and dimension table: > How dimension can related with another business processes: use the enterprise data warehouse bus architecture. Maybe this model can interact with another business processes. > What type of fact are designed; transactional, Factless, Periodic Snapshot or Accumulating Snapshot; and which reason support it & what cases can use. > Why normalized tables was chosen: how it affect the model over its base dimension. These points will guide to evaluate the design of the data model.

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3 Draw the snowflake schema

Once you have chosen the dimensions and facts, you can draw the snowflake schema using a graphical tool or a notation system. A snowflake schema consists of a central fact table that connects to multiple dimension tables, which may in turn connect to other dimension tables. The schema resembles a snowflake, as the dimension tables branch out from the fact table. You need to show the primary and foreign keys, the cardinalities, and the data types of each table and column. You also need to label the tables and columns clearly and consistently, using descriptive and meaningful names.

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Nicholas Whiting

Senior Fellow | CXO Advisor | Principal Consultant | Architect / Engineer | Flight Instructor - IT Governance, Digital Transformation, Data and ML Programs, Cybersecurity, Application Modernization, and Cloud Adoption
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When drawing your snowflake schema, consider incorporating annotations or additional metadata directly into your diagram. These can highlight important considerations like data refresh rates, data lineage, or the roles of specific tables in the broader business context. This can be particularly useful when the schema is being reviewed by stakeholders who may not be as familiar with data modeling concepts, as it adds an extra layer of clarity and context.

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4 Write the documentation

In addition to creating the schema, it is necessary to write the documentation that explains the logic and rationale behind your design. This documentation should include a summary of the scope and objectives of the project, a description of the data sources and quality issues, a definition of each dimension and fact, as well as their hierarchies and levels of detail. Additionally, you should provide justification for design choices and trade-offs such as normalization, denormalization, or aggregation. Furthermore, you should include a list of assumptions and limitations of the design, as well as a glossary of terms and abbreviations used in the schema and documentation. The documentation should be clear, concise, accurate, and use a consistent format and style. It is important to update the documentation whenever any changes are made to the schema.

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Avinash (Avi) Deshpande
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In my experience, data does not get designed itself. Even before you start with Snowflake, please use a modeling tool like @sqldbm or some other tool. Design the dimensional model, logical and physical, resolve the joins, add data catalog details, business glossary. Get business validation on the design and forward engineer to the database. Please push the joins to the database even if snowflake does not need them. Publish the models to confluence for stakeholders to review. Keep the model updated in the tool and should be the only place to make changes. Key aspects - breathing data model - data catalog - business glossary - impact analysis

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Heather R.

Product Advocate? Leverages Data Governance & Data Modeling Solutions | ? Database Standards | ? Data Model Management
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The documentation is the unsung hero of this. I'm sure many find it boring or out-of-date, but it's critical—especially in a data warehouse. Why are we building this design? What are the data sources? Did we calculate this field? If so, what is the logic? This is where you capture your definitions and quality logic. It's how you ensure everyone is on the same page, and one, two, five years down the road, the team working on this understands the goals, vision, and design. Also, please don't forget it's a living, breathing document. Treat it as such! If you change the design, the sources, the metrics, or the definition, update your document. Make future you happy!

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5 Share the schema and documentation

The final step is to share the schema and documentation with your stakeholders and solicit their feedback. You should choose an appropriate format and channel for sharing, depending on the audience and purpose. For example, you can use a slide deck, a report, a dashboard, or a prototype to present your schema and documentation. You should also use visual aids, such as charts, diagrams, or examples, to illustrate your points and make them easier to understand. You should also be prepared to answer any questions or concerns that your stakeholders may have, and explain how your design meets their needs and expectations.

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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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How do you document and communicate your snowflake schema design to stakeholders?

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1 Define the scope and objectives

2 Choose the dimensions and facts

3 Draw the snowflake schema

4 Write the documentation

5 Share the schema and documentation

6 Here’s what else to consider

Data Modeling

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