Rule #1: Load detailed atomic data into dimensional structures.
Dimensional models should be populated with bedrock atomic details to support the unpredictable filtering and grouping required by business user queries. Users typically don't need to see a single record at a time, but you can't predict the somewhat arbitrary ways they'll want to screen and roll up the details. If only summarized data is available, then you've already made assumptions about data usage patterns that will cause users to run into a brick wall when they want to dig deeper into the details. Of course, atomic details can be complemented by summary dimensional models that provide performance advantages for common queries of aggregated data, but business users cannot live on summary data alone; they need the gory details to answer their ever-changing questions.
Rule #2: Structure dimensional models around business processes.
Business processes are the activities performed by your organization; they represent measurement events, like taking an order or billing a customer. Business processes typically capture or generate unique performance metrics associated with each event. These metrics translate into facts, with each business process represented by a single atomic fact table. In addition to single process fact tables, consolidated fact tables are sometimes created that combine metrics from multiple processes into one fact table at a common level of detail. Again, consolidated fact tables are a complement to the detailed single-process fact tables, not a substitute for them.
Rule #3: Ensure that every fact table has an associated date dimension table.
The measurement events described in Rule #2 always have a date stamp of some variety associated with them, whether it's a monthly balance snapshot or a monetary transfer captured to the hundredth of a second. Every fact table should have at least one foreign key to an associated date dimension table, whose grain is a single day, with calendar attributes and nonstandard characteristics about the measurement event date, such as the fiscal month and corporate holiday indicator. Sometimes multiple date foreign keys are represented in a fact table.
Rule #4: Ensure that all facts in a single fact table are at the same grain or level of detail.
There are three fundamental grains to categorize all fact tables: transactional, periodic snapshot, or accumulating snapshot. Regardless of its grain type, every measurement within a fact table must be at the exact same level of detail. When you mix facts representing multiple levels of granularity in the same fact table, you are setting yourself up for business user confusion and making the BI applications vulnerable to overstated or otherwise erroneous results.
Rule #5: Resove many-to-many relationships in fact tables.
Since a fact table stores the results of a business process event, there's inherently a many-to-many (M:M) relationship between its foreign keys, such as multiple products being sold in multiple stores on multiple days. These foreign key fields should never be null. Sometimes dimensions can take on multiple values for a single measurement event, such as the multiple diagnoses associated with a health care encounter or multiple customers with a bank account. In these cases, it's unreasonable to resolve the many-valued dimensions directly in the fact table, as this would violate the natural grain of the measurement event. Thus, we use a many-to-many, dual-keyed bridge table in conjunction with the fact table.
Rule #6: Resolve many-to-one relationships in dimension tables.
Hierarchical, fixed-depth many-to-one (M:1) relationships between attributes are typically denormalized or collapsed into a flattened dimension table. If you've spent most of your career designing entity-relationship models for transaction processing systems, you'll need to resist your instinctive tendency to normalize or snowflake a M:1 relationship into smaller subdimensions; dimension denormalization is the name of the game in dimensional modeling.
It is relatively common to have multiple M:1 relationships represented in a single dimension table. One-to-one relationships, like a unique product description associated with a product code, are also handled in a dimension table. Occasionally many-to-one relationships are resolved in the fact table, such as the case when the detailed dimension table has millions of rows and its roll-up attributes are frequently changing. However, using the fact table to resolve M:1 relationships should be done sparingly
Rule #7: Store report labels and filter domain values in dimension tables.
The codes and, more importantly, associated decodes and descriptors used for labeling and query filtering should be captured in dimension tables. Avoid storing cryptic code fields or bulky descriptive fields in the fact table itself; likewise, don't just store the code in the dimension table and assume that users don't need descriptive decodes or that they'll be handled in the BI application. If it's a row/column label or pull-down menu filter, then it should be handled as a dimension attribute.
Though we stated in Rule #5 that fact table foreign keys should never be null, it's also advisable to avoid nulls in the dimension tables' attribute fields by replacing the null value with "NA" (not applicable) or another default value, determined by the data steward, to reduce user confusion if possible.
Rule #8: Make certain that dimension tables use a surrogate key.
Meaningless, sequentially assigned surrogate keys (except for the date dimension, where chronologically assigned and even more meaningful keys are acceptable) deliver a number of operational benefits, including smaller keys which mean smaller fact tables, smaller indexes, and improved performance. Surrogate keys are absolutely required if you're tracking dimension attribute changes with a new dimension record for each profile change. Even if your business users don't initially visualize the value of tracking attribute changes, using surrogates will make a downstream policy change less onerous. The surrogates also allow you to map multiple operational keys to a common profile, plus buffer you from unexpected operational activities, like the recycling of an obsolete product number or acquisition of another company with its own coding schemes.
Rule #9: Create conformed dimensions to integrate data across the enterprise.
Conformed dimensions (otherwise known as common, master, standard or reference dimensions) are essential for enterprise data warehousing. Managed once in the ETL system and then reused across multiple fact tables, conformed dimensions deliver consistent descriptive attributes across dimensional models and support the ability to drill across and integrate data from multiple business processes. The Enterprise Data Warehouse Bus Matrix is the key architecture blueprint for representing the organization's core business processes and associated dimensionality. Reusing conformed dimensions ultimately shortens the time-to-market by eliminating redundant design and development efforts; however, conformed dimensions require a commitment and investment in data stewardship and governance, even if you don't need everyone to agree on every dimension attribute to leverage conformity.
Rule #10: Continuously balance requirements and realities to deliver a DW/BI solution that's accepted by business users and that supports their decision-making.
Dimensional modelers must constantly straddle business user requirements along with the underlying realities of the associated source data to deliver a design that can be implemented and that, more importantly, stands a reasonable chance of business adoption. The requirements-versus-realities balancing act is a fact of life for DW/BI practitioners, whether you're focused on the dimensional model, project strategy, technical/ETL/BI architectures or deployment/maintenance plan.
Specifically, types of facts: Accumulating, Factless, Transactions, Snapshots, Additive, SemiAdditive and Non Additive
Accumulating Fact Table
An accumulating fact table is where all of the dimensional attributes are not available at the time of creation and the dimensions that are linked to a fact table change over time. The most common implementation of this is in the recording of dates against facts.
Take a "Sales" fact, typical dates you may be intersted in when tracking an individual sale is maybe, order_date, ship_date, delivery_date and payment_date. These would not all be available when the fact is first created. Over time the fact record would accumulatemore relationships with the dimensions as the relevant date milestones were passed for the sale.
Factless Facts
A factless fact is where the fact does not store an actual numerical measure, the mere existance of a fact record indicates that an event has happened that you wish to track.
The classic example of this would be an "Attendance" fact. If you had dimensions to record date, scheduled_course, instructor and delegate then you could create a fact table that held the permutations of these dimensions. From this you could evaluate the number of courses you run, the number of delegates, the number of courses by instructor etc.
I would never simply leave a factess fact as a bare collection of foreign key columns I would always add a dummy measure column which would be set to 1 which you would then sum.
Transaction Grain
This is the most common type of fact. You would declare the grain of the fact, ie the level of detail and then this is what would be stored. For example you may have a sales_order fact, every time a new sales order a new row would be created in the fact table. alternatively you may have a "monthly_sales" fact. At the end of every month you would aggregate up all the sales that happened in that month and record the single total value.
Snapshot Facts
The snapshot fact contains a reflection of the state of an entity at a given point in time. A classic example of this would be a "daily_balance" fact in a banking system. This would, on a daily basis record the balance of each account, it would NOT list the individual transactions that happened on the account.
Additive Facts
A fully additive fact is one where the measures can be aggregated.
For example our Sales fact above would be fully additive as you can aggregate the sales amout over time, by product, by region or by salesman and still get the correct answer.
A fully additive fact is one where the measures can be aggregated.
For example our Sales fact above would be fully additive as you can aggregate the sales amout over time, by product, by region or by salesman and still get the correct answer.
Semi Additive Facts
A semi additive fact is one where the measure can either have only a subset of aggregations applied to it, it you can count the measures but not sum them, or the measures are only additive over a subset of the dimensions.
Using our "daily_balances" fact above would be a good example of a semi additive fact. The daily balances can be aggregated by customer if the customer has multiple accounts to give the customrs daily balance, however the balances could not be aggregated over time as adding last weeks balance onto this weeks balance would result in a nonsensical figure.
Non Additive Facts
A non additive fact is one where the measure is non aggregable over any dimensions. These are commonly where percentages have been calculated and stored in the fact. Another example could be a profit margin on a sale, there is this figure other than at an individual sale level.
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