Guide to data grain

Data grain is the combination of columns at which records in a table are unique. Ideally, this is captured in a single column, a unique primary key, but even then, there is descriptive grain behind that unique id. Let’s look at some examples to better understand this concept.

In the above table, each user_id is unique. This table is at the user grain.

In the above table, user_id is no longer unique. The combination of user_id and address creates a unique combination, thus this table is at the user address grain. We generally describe the grain conceptually based on the names of the columns that make it unique. A more realistic combination you might see in the wild would be a table that capture the state of all users at midnight every day. The combination of the captured updated_date and user_id would be unique, meaning our table is at user per day grain.

Creating surrogate keys

In both examples listed in the previous paragraph, we’d want to create a surrogate key of some sort from the combination of columns that comprise the grain. This gives our table a primary key, which is crucial for testing and optimization, and always a best practice. Typically, we’ll name this primary key based on the verbal description of the grain. For the latter example, we’d have user_per_day_id. This will be more solid and efficient than testing than repeatedly relying on the combination of those two columns.

The importance of data grain in data modeling

Thinking deeply about grain is a crucial part of data modeling. As we design models, we need to consider the entities we’re describing, and what dimensions (time, attributes, etc.) might fan those entities out so they’re no longer unique, as well as how we want to deal with those. Do we need to apply transformations to deduplicate and collapse the grain? Or do we intentionally want to expand the grain out, like in our user per day example?

There’s no right answer here, we have the power to do either as it meets our needs. The key is just to make sure we have a clear sense of our grain for every model we create, that we’ve captured it in a primary key, and that we’re applying tests to ensure that our primary key column is unique and not null.

Choosing the right type of data grain for your use case

Selecting appropriate grain is critical for effective data modeling success. Here are some considerations to keep in mind:

• Analysis requirements: The questions you need to answer determine your grain selection. Deeper questions often require finer grain structure.
• Data volume: Larger data volumes challenge storage and processing capabilities. Higher grain levels reduce storage needs but sacrifice analytical detail.
• Query performance: Finer grain means more records to process during queries. Performance tuning might require pre-aggregation at higher grain levels.
• Update frequency: More frequent data updates favor transactional grain. Less frequent updates work well with snapshot approaches.
• Process visibility: Tracking multi-stage processes requires accumulating snapshots. through workflows.
• Reporting cadence: Regular reporting schedules influence grain selection. Daily reporting needs different grain than quarterly analysis.
• Historical analysis: Long-term trend analysis has unique grain requirements. Consider how historical comparisons will work with your chosen grain.

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Related reading:

• A complete guide to dimensional modeling with dbt
• Successful data transformation: Six steps | dbt Labs
• Five real data transformation examples | dbt Labs

FAQs for data grain