When do you need an accumulating snapshot?

A reader wonders how to decide between two options:  designing an accumulating snapshot vs. tracking status changes within a dimension.

I have new project to track the status of order transition. I'm unable to reach a conclusion as to implement as an accumulating snapshot or a type 2 slowly changing dimension. ETL  integrates a number of individual systems as the order transits each stage. What is the best way to design it?

Kumar
Milton Keynes, UK

Many businesses have one or more central dimensions that undergo a state transition as they touch multiple processes. Is it enough to track the changes to the dimension?  Or is an accumulating snapshot needed?

I'll walk you through some decision criteria.  But first a little refresher on the two design options the reader is considering.

Type 2 changes and timestamps


Type 2 changes track the history of something represented by a dimension.  Each time there is a change to this item, a new row is inserted in to the dimension table.

This allows any row in a fact table to be associated with a historically accurate version of the dimension as of the relevant point in time.

In the reader's case, status of the order might be an attribute of an Order dimension.  Modeled as a type 2 change, the dimension holds a status history for each order.  Adding effective and expiration dates to each row, you know exactly when each state transition occrred.

Accumulating snapshots

An accumulating snapshot is a type of fact table that records a single row for something the enterprise tracks closely, such as a trouble ticket or mortgage application--or, in the reader's case, an order.

This fact table contains multiple references to the date dimension -- one for each of the major milestones that the item in question can reach.  In the case of the order, this might be the date of order, the date of credit approval, the date of picking, the date of shipment and the date of delivery.

Unlike other kinds of fact tables, the accumulating snapshot is intended to be updated.  These dates are adjusted each time one of the milestones is reached.

There may also be facts that track the number of days (or minutes) spent between each milestone.  These "lags" are a convenience -- they can be computed from the dates.  (Building them into the fact table makes analysis much easier, but does require that the ETL process revisit rows on a regular basis, rather than when status changes.)

Avoiding correlated subqueries

If a type 2 slowly changing dimension with timestamps tracks the history, why consider an accumulating snapshot?

The analytic value of the accumulating snapshot is that it allows us to study the time spent at various stages. In the reader's case, it can make it simple to study the average time an order spends in the "picking" stage, for example.

We can do this with a type 2 slowly changing dimension as well, but it will be more difficult to study the average time between stages. For the order in question, days spent in the picking stage requires knowing the date of credit approval and the date picked.  These will be in two different rows of the dimension.  Now imagine doing this for all orders placed in January 2012.  This will require a correlated subquery.

The accumulating snapshot pre-correlates these events and places them in a single row.  This makes the queries much easier to write, and they are likely to run faster as well.  The cost, of course, is the increased data integration burden of building the additional fact table.

Avoiding drilling across

When each of the discrete milestones is captured by a different fact table, lag may be computed without correlated subqueries.  In this case, it will involve drilling across.

For example, separate fact tables track orders, credit approvals, picking and shipping.  Each references the order dimension.  Days spent in the picking stage can be studied by drilling across credit approvals and picking, with results linked by the common order dimension. ¹

Here, the pressure for an accumulating snapshot is reduced.  It may still be warranted, depending on your reporting tools, developer skills an user base.

Summary and final advice

In the end, your decision should boil down to the following:

1. An accumulating snapshot should only be considered if you are studying the time spent between major milestones
2. If it helps avoid correlated subqueries, it may be a strong option
3. If it avoids drill-across queries, it may be a useful option

Making the choice will impact several groups -- ETL developers, report developers, and potentially users.  Make sure this is a shared decision.

Also keep in mind the following:

• If you build an accumulating snapshot, you will probably also want to track status in the dimension as a type 2 change.
• Accumulating snapshots work best where the milestones are generally linear and predicable.  If they are not, the design and maintenance will be significantly more complex.

Last but not least:

• The accumulating snapshot should be derived from one or more base fact tables that capture the individual activities.  

When in doubt, build the base transaction-grained fact tables first.  You can always add an accumulating snapshot later. 

Learn more

This is a popular topic for this blog.  Here are some places where you can read more:

• Deeper into the Accumulating Snapshot (December 13, 2010) Discusses the accumulating snapshot and issues surrounding non-linear processes.

• Responding to star schema Detractors with Timestamps (March 12, 2008) Explains how a type 2 techniques and timestamps can track the change history of a dimension.

• Accumulating snapshots: are they necessary? (January 14, 2010)  Looks at why the accumulating snapshot and type 2 technique may be used together.

• Q&A: Accumulating snapshots (October 1, 2010) Explores the cardinality relationship between accumulating snapshot and dimension table

• Three ways to drill across (December 23, 2011) Covers the drill-across process

And of course, these concepts are covered extensively in my books.  In the latest one, Star Schema: The Complete Reference, the following chapters may be of interest:

• Chapter 8, "More Slow Change Techniques" discusses time stamped tracking of slow changes

• Chapter 11, "Transactions, Snapshots and Accumulating Snapshots" explores the accumulating snapshot in detail.

• Chapter 14, "Derived Schemas", discusses derivation of an accumulating snapshot from transaction-grained stars.

• Chapter 4, "A Fact Table for Each Process", includes a detailed discussion of dril-across analysis.

Image Credit:  Creativity103 via Creative Commons

¹ Note that this scenario applies to the reader, but does not always apply.  Trouble tickets, for example, may be tracked in a single fact table that receives a new row for each status change.  In this case, there is no drill-across option.

Three ways to drill across

Previous posts have explored the importance of conformed dimensions and drilling across.  This post looks at the process of drilling across in more detail. 

There are three primary methods for drilling across.  These methods can be leveraged manually, automated with BI software, or performed during the data integration process.

Querying multiple stars

Conformed dimensions ensure compatibility of information in various stars and data marts; drilling across is the process of bringing it together.  (For a refresher, see this post.)
 
For example, suppose we wish to compute "Return Rate" by product.  Return rate is the ratio of shipments to returns.

Information about shipments and returns is captured in two granular star schemas:

• shipment_facts captures shipment metrics by Salesperson, Customer, Product, Proposal, Contract, Shipment, Shipper, and Shipment Date
• return_facts captures return metrics by Salesperson, Customer, Product, Contract, Reason and Return Date

Reporting on return rate will require comparing facts from each of these stars. 

Drilling across

Recall that fetching facts from more than one fact table requires careful construction of queries. It is not appropriate to join two fact tables together, nor to link them via shared dimensions. Doing so will double-count facts, triple-count them, or worse.

Instead, the process must be completed in two phases.

• Phase 1:  Fetch facts from each fact table separately, aggregating them to a common level of detail
• Phase 2:  Merge these intermediate result sets together based on their common dimensions

In practice, there are several ways that this task can be performed.

Method 1: Issue two queries, then merge the results

The first method for drilling across completes phase 1 on the database, and phase 2 in the application (or on the application server).

1. Construct two separate queries: the sum of quantity shipped by product, and the sum of quantity returned by product.
2. Take the two result sets as returned by the DBMS, and merge them based on the common products. Compute the ratio at this point.

While it may seem odd that phase 2 not be performed on the DBMS, note that if the data sets are already sorted, this step is trivial.

Method 2:  Build temp tables, then join them

The second method performs both phases on the DBMS, making use of temporary tables.

1. Construct two SQL statements that create temporary tables: the sum of quantity shipped by product, and the sum of quantity returned by product
2. When these are completed, issue a query that performs a full outer join of these tables on the product names and computes the ratio.

Be sure that the temporary tables are cleaned up.

Method 3: Join subqueries

Like the previous method, this method performs all the work on the DBMS.  In this case, however, a single query does all the work.

Queries for each fact table are written, then joined together in the FROM clause of a master query.  For example:


SELECT
  COALESCE (shp.product, rtn.product) as Product,
  quantity_returned / quantity_shipped as ReturnRate
FROM
  ( SELECT product, sum(quantity_shipped)as quantity_shipped
    FROM shipment_facts, product
    WHERE .....
  ) shp
FULL OUTER JOIN
  ( SELECT product, sum(quantity_returned) as quantity_returned
    FROM return_facts, product
    WHERE....
  ) rtn
 ON
    shp.product = rtn.product
   
The two subqueries in the FROM clause represent phase 1.  Phase 2 is represented by the main SELECT query that joins them and computes the ratio.

Applying these techniques

These techniques may applied in a variety of ways:

1. Report developers may write their own queries using one of more of these methods
2. You may have BI software that can automate drilling across using one or more of these methods
3. Drilling across may be performed at ETL time using one of these methods (or an incremental variant)

In the latter case, the ETL process builds a new star (or cube) that contains the result of drilling across. This is called a derived schema, or second line data mart.

Learn More

For more information, see the following resources:

• Multiple Stars and Conformed Dimensions (August 15, 2011)

• Conformed Dimensions (November 15, 2011)

Many pages are devoted to this topic in my books. In the latest one, Star Schema: The Complete Reference, the following chapters may be of interest:

• Chapter 5, "Conformed Dimensions" discusses these techniques in greater detail.  

• Chapter 14, "Derived Schemas" looks at special considerations when creating derived stars that pre-compute drill-across comparisons.

• Chapter 16, "Design and Business Intelligence", discusses how to work with SQL-generating BI software.

More to come on this topic in the future.  If you have questions, send them in.

-Chris

Image by Patrick Hosely via Creative Commons 2.0

Conformed Dimensions

This second post on conformed dimensions explores different ways in which dimensions can conform. 

There are several flavors of conformed dimensions. Dimensions may be identical, or may share a subset of attributes that conform.

Conformance basics

Conformed dimensions are central to the discipline of dimensional modeling.  The basics of conformance were introduced in a post from earlier this year.  In a nutshell:

• Measurements of discrete business processes are captured in individual star schemas (e.g. proposals and orders)

• Some powerful business metrics combine information from multiple processes.  (e.g. fill rate: the ratio of orders to proposals)

• We construct these metrics through a process called drilling across

• Drilling across requires dimensions with the same structure and content (e.g. proposals and orders have a common customer dimension)

For a refresher on these concepts, see "Multiple stars and conformed dimensions" (8/15/2011). 

Physically shared dimensions not required 

When two fact tables share the same dimension table, their conformance is a given. Since the shared dimensions are the same table, we know they will support drilling across.

For example, stars for proposals and orders may share the customer dimension table.  This makes it possible to query orders by customer and products by customer, and then merge the results together.

But this process of drilling across does not require shared dimension tables. It works equally well if proposals and orders are in separate data marts in separate databases.

As long as the stars each include dimensions that share the same structure (e.g. a column called customer_name) and content (i.e. the customer values are the same), it will be possible to merge information from the stars. 

Levels of conformance 

It is easy to take this a step further.  We can also observe that there is compatibility between dimensions that are not identical.

If a subset of attributes from two dimension share the same structure and content, they form a sort of “lowest common denominator” across which we can compare data from the stars.

For example, suppose we establish budgets at the monthly level, and track spending at the daily level.  Clearly, days roll up to months.  If designed correctly, it should be possible to compare data from budget and spending stars by month.

The picture below illustrates the conformance of a MONTH and DAY table graphically.  The ring highlights the shared attributes; any of these can be used as the basis for comparing facts in associated fact tables.

In this case, the two conformed dimensions participate in a natural hierarchy.  Months summarize days. The month table is referred to as a “conformed roll-up” of day.

To successfully drill across, the content of the shared attributes must also be the same.  Instances of month names, for example, must be identical in each table -- "January" and "January" conform; "January" and "JAN." do not.

To guarantee conformance of content, the source of the rollup should be the base dimension table. This also simplifies the ETL process, since it need not reach back to the source a second time. 

Other kinds of conformance 

Identical tables and conformed roll-ups are the most common kinds of conformed dimensions.  Other kinds are less common. 

Degenerate dimensions (dimensions that appear in a fact table) may also conform. This is particularly useful with transaction identifiers. 

Overlapping dimensions may share a subset of attributes, but not participate in a hierarchy. This is most common with geographical data. 

More Info 

For more information, see the following posts: 

• Multiple Stars and Conformed Dimensions (August 15, 2011) 

• Q and A: Degenerate Dimensions, ETL and BI (October 15, 2010)
• Creating transaction identifiers for fact tables (October 17, 2011)  This post addresses degenerate dimensions and transaction identifiers

I also write about conformed dimensions extensively in Star Schema, The Complete Reference.  

If you enjoy this blog, you can help support it by picking up a copy!

Photo by Agnes Periapse, licensed under Creative Commons 2.0

Are bridge tables really fact tables?

A reader observes that bridge tables seem problematic, and wonders if they should just replaced by factless fact tables.

Q:  I am wondering if all bridge tables are in fact replacements for factless fact tables. The problem with the bridge table as you mention it is that...[you] need to do an expensive join and issues with Cartesian joins/ double counting etc. So the question is whether a bridge table is practical option as compared to a separate fact.

Ashish
Bangalore, India

A:  The bridge table looks a lot like a fact table, but it is used very differently.

While we avoid joining a fact table to another fact table, we seek to join bridge tables to fact tables. This can have implications for BI tools that generate SQL.

Similarities

A bridge table appears similar to a fact table because it contains multiple foreign keys. This is most evident when you look at an attribute bridge table, which links a dimension to an outrigger. It consists solely of foreign key references to the dimension and the outrigger.

In this respect, the bridge table is very similar to a factless fact table.  Indeed, one might make the argument that a bridge relates a set of dimensions in much the same way that a factless fact table describes conditions.

But there is a very important difference: we never join a fact table to another fact table. Bridges, on the other hand, are intended to be joined with fact tables.

We do not join fact tables to fact tables

You should never join two or more fact tables--either directly or indirectly via shared dimensions. Fact values will repeat if multiple rows in either fact table share the same dimensionality. We receive a Cartesian product of all related facts.  The result is double-counting, or worse. 

Instead of joining fact tables, we use a technique called drilling across. Facts are collected from each table and aggregated to common level of detail, then merged into a single result set. I wrote about this process earlier this year.¹

Many BI tools that generate SQL are able to identify fact tables and automatically invoke drill across logic when required.

We do join bridge tables to fact tables

Bridge tables represent groups. We create them so that a single fact can be associated with a group of values (such as multiple salespeople) rather than a single value.

When we use a bridge table, we link it to other fact tables.²  By doing this, a single fact in the fact table associates with multiple rows in the bridge table.   

With a bridge, we are exploiting the very Cartesian product that we normally seek to avoid.

We are intentionally repeating a single fact for multiple group members.  To avoid incorrect results, it behooves us to group results by member, or to constrain for a single group member.

Even if you were to replace a bridge with a factless fact table, this is the behavior you would desire. Rather than drill across, you would link it to other fact tables, in order to associate the bridged values with various facts. Cartesian products and the danger of double counting would remain.

Bridge tables and BI software

A bridge table is not really a fact table. It is not the locus of process measurement.  It describes neither activities nor conditions. It is merely a construct that allows us to deal with repeating values. It is meant to be linked with fact tables, and used with care.

Because a bridge is composed of foreign keys, however, some BI tools may identify it as a fact table. If your tool does this, you will need to prevent it from invoking drill-across logic for queries that involve a bridge.

Your tool may have a facility for this. If it does not, you can hide the bridge by joining it to dimensions within a view.

More info

Thanks to Ashish for the comments. If you have a question about bridge tables, send it to the address on the sidebar.

You can learn more in my book, Star Schema: The Complete Reference.  Two full chapters are dedicated to bridge tables, including 30 diagrams.

See also:

• Resolve repeating attributes with a bridge table (January 28, 2011)
• Bridge to multi-valued dimensions (February 9. 2011)
• Bridge Tables and Many-to-many Relationships (April 25, 2011)
• Multiple Stars and Conformed Dimensions (August 15, 2011)
• Factless fact tables (September 15, 2011)

¹Factless fact tables which describe conditions are not joined to other fact tables either. When they are compared to other fact tables, we typically use set operators or subqueries.

²This may happen directly, in the case of a dimension bridge, or indirectly, in the case of an attribute bridge.

Image by ahisgett licensed under Creative Commons 2.0

Multiple Stars and Conformed Dimensions

The concept of conformed dimensions is central to the discipline of dimensional modeling. This post introduces conformed dimensions; future posts will explore them in more detail. 

In your data warehouse, each star corresponds to a business process. Combining facts from different processes can produce powerful compound metrics. The key to making this work is a set of conformed dimensions.

Conformed dimensions are closely associated with Kimball's "Bus Architecture," but are crucial in any scenario that involves dimensional data.

Multiple Stars

In a dimensional design, each star captures collects measurements that describe a discrete business process.

If we have two measurements that describe different processes, we place them into separate fact tables.

For example, a sales data mart may contain multiple stars:

• Proposal information by Salesperson, Prospect, Product, Proposal and Proposal Date
• Order information by Salesperson, Customer, Product, Proposal, Contract and Order Date
• Shipping information by Salesperson, Customer, Product, Proposal, Contract, Shipment, Shipper, and Shipment Date
• Return information by Salesperson, Customer, Product, Contract, Reason and Return Date

In each of these stars, the fact table will record measurements that describe the processes of issuing proposals, taking orders, shipping product and handling returns.

By recording measurements of each process in a different star, we are able to capture information at the most detailed level possible.  We can study each of these processes, complete with attendant details, by accessing the appropriate star.

Cross-process Metrics

Some of the most powerful measurements actually combine information from multiple processes. These metrics require combining facts from different stars.

In the sales data mart, the ratio of proposals to orders is the "close rate," a powerful indicator that sales managers and executives look at on a regular basis.

Similarly, the ratio of shipments to returns is the "return rate," an essential quality control metric.

Drilling Across

When we compare facts from different stars, we don't simply join the fact tables.  To do so might cause double-counting of some facts.

Instead we follow a process that Kimball calls drilling across.  The drill-across process can be broken down into two phases.  In the first phase, each star is queried, and results are aggregated to a common level of detail.  In the second phase, these result sets are merged based on their common dimensions.

For example, to compute the return rate by product for August of 2011, we do the following:

1. a. Figure out quantity shipped by product for August 2011
   b. Figure out quantity returned by product for August 2011
   
2. Merge these amounts based on the common product names and compute the ratio

We may perform this drill across operation at query time (many BI tools can do this automatically), or we may do it at ETL time, storing the results in a separate star or cube (sometimes called a second-line data mart.) 

Conformed Dimensions

The key to making all this work is the organization of the dimensions.  As you saw in the example above, we used the dimension values to link our results together: product names were used to merge together shipment and return quantities and compute their ratio.  This would not have worked if the product dimensions for Shipments and Returns had been different.

This is the basic idea behind conformed dimensions.  We say that two dimensions conform if they have the same structure and content.  Both our stars, had a product dimension table with a product name attribute, and the product names were specified the same way in each.  Sharing a single physical table is one way to ensure conformance, but it is not required.

Two dimensions can also conform if one has a subset of the other's attributes.  As long as the common attributes have the same structure and content, they are said to conform.

Planning Conformance

By planning a set of conformed dimensions, we ensure that fact tables can be used to compare processes.  This is important within a single data mart, such as the one above, and it is also important when looking across multiple data marts.

Conformed dimensions are the organizing principle in Kimball's architecture.  Conformed dimensions are planned up-front, as a part of a project that establishes a data architecture based on dimensional design. Implementation proceeds once this conformance bus has been planned.

The concept is also important in other architectures. For example, the sales data mart discussed above might be part of Corporate Information Factory architecture.  Within this data mart, conformance guarantees we can compare shipments to returns, proposals to orders, and so forth.

More to come

In the coming weeks, I will post more about conformed dimensions.  We will look at "levels" of conformance, how to document conformed dimensions, and how different tools work with conformed dimensions. If you have my book, I also encourage you to read Chapters 4 and 5, which look at these concepts in detail.

Photo by Agnes Periapse, licensed under Creative Commons 2.0

Ten Things You Won't Learn from that Demo Schema

Many people learn about dimensional modeling by studying a sample star schema database that comes with a software product. These sample databases are useful learning tools—to a point. Here are 10 things you won't learn by studying that demo.

If you've learned everything you know about star schema by working with a sample database, you probably have a good intuitive grasp of star schema design principles. In a previous post, I provided a list of 10 terms and principles that most sample databases illustrate well.

But there are many important things about a dimensional data warehouse that are not revealed by the typical "Orders" or "Sales" demo. Here are the top 10 things you will not learn from that sample database.

1. Multiple Fact Tables Most sample databases contain a single star—one fact table and its associated dimension tables. But it is rare to find a business process that can be modeled with a single fact table; it is impossible to find an enterprise that can. Most real-world designs will involve multiple fact tables, sharing a set of common dimensions.
   
   When facts become available at different times, or with different dimensionality, they almost always belong in separate fact tables. Modeling them in a single fact table can have negative consequences. Mixed grain issues may result, complicating the load and making reporting very difficult. For example, building reports focused on only one of the facts can result in a confusing preponderance of extra rows containing the value zero.


2. Conformance With multiple fact tables, it is also important that each star be designed so that it works with others. A design principle called conformance helps ensure that as we build each new star, it works well with those that came before it. This avoids the dreaded stove-pipe. This principle allows a set of star schemas to be planned around a set of common dimensions and implemented incrementally.


3. Drilling Across It's also important to understand how to properly build a report that accesses data from multiple stars. A single SQL select statement won't do the job. Double counting, or worse, can result. Instead, we follow a process called drilling across, where each star is queried individually. The two result sets are then combined based on their common attributes. These drill across reports are some of the most powerful in the data warehouse.


4. Snapshot Fact Tables The fact table found in most demo stars is usually called a transaction fact table. But there are real world situations where other types of fact table designs are called for.
   
   A snapshot design is useful for capturing the result of a series of transactions at a point-in-time; for example, the balance of each account in the general ledger at the end of each day. This type of design introduces the concept of semi-additivity, which can be a problem for many ad hoc query tools. It makes no sense to add together yesterday's balance and today's balance. It is not uncommon to compute averages based on the data in a snapshot star. But one must be careful here; the SQL Average() function may not always be what you need.


5. Factless Fact Tables Another type of fact table often contains no facts at all. Factless fact tables are useful in situations where there appears to be nothing to measure aside from the occurrence of an event, such as a customer contact. They also come in handy when we want to capture information about which there may be no event at all, such as eligibility.
   
   In addition to transaction, snapshot and factless designs, there are other types of fact table as well. It is not uncommon to need more than one, even when modeling a single activity.


6. Roles and Aliasing Many business processes involve a dimension in multiple roles. For example, in accounting a transaction may include the employee who makes a purchase, as well as the employee who approves it. There is no need for separate"Purchaser" and "Approver" dimensions. A single "Employee" dimension will do the job. The fact table will have two foreign key references to the Employee dimension--one that represents the purchaser, and one that represents the approver. We use SQL "aliasing" when querying this schema in order to capture the two employee roles.


7. Advanced Slow Change Techniques If you are lucky, you were able to learn about Type 1 and Type 2 Slowly Changing Dimension techniques from the demo schema. I described these in a previous post. Often, analytic requirements require more.
   
   A Type 3 change allows you to "have it both ways," analyzing all past and future transactions as if the change had occurred (retroactively) or not all.
   
   There are also hybrid approaches, one of which tracks the "transaction-time" version of the changed data element as well as the "current-value" of the data element.
   
   And then there's the time-stamped dimension technique, also called a transaction dimension. In this version, each row receives an effective date/time and an expiration date time. This provides Type 2 functionality, but also allows point-in-time analysis of the dimensional data.


8. Bridge Tables Perhaps the most confusing technique for the novice dimensional designer is the use of bridge tables. These tables are used when the standard one-to-many relationship between dimension and fact does not apply. There are three situations where bridge tables come in handy:
   
   An attribute bridge resolves situations where a dimension attribute may repeat multiple times. For example, a dimension table called "Company" may include an attribute called "Industry." Some companies have more than one industry. Rather than flattening into "Industry 1," "Industry 2," and so on, an attribute bridge captures as many industries as needed.
   
   A dimension bridge resolves situations where an entire dimension may repeat with respect to facts. For example, there may be multiple salespeople involved in a sale. Instead of loading the fact table with multiple salesperson keys, a dimension bridge gracefully manages the group of salespeople.
   
   A hierarchy bridge resolves situations where a recursive hierarchy exists within a dimension. For example, companies own other companies. At times, users may want to roll transactions up that occur beneath a specific company, or vice versa. Instead of flattening the hierarchy, which imposes limitations and complicates analysis, a hierarchy bridge can be joined to the transaction data in various ways, allowing multiple forms of analysis.
   
   All bridge implementations have implications for usage, or report building. Improper use of a bridge can result in double counting or incorrect results. Bridges also make deployment of business intelligence tools more difficult.


9. Derived Schemas Useful stars can also be derived from existing stars. Often called "second-line" solutions, these derived schemas can accelerate specific types of analysis with powerful results. The merged fact table combines stars to avoid drilling across. The sliced fact table partitions data based on a dimension value, useful in distributed collection and analysis. The pivoted fact table restructures row-wise data for columnar analysis and vice-versa. And set operation fact tables provide precomputed results for union, intersect and minus operations on existing stars.


10. Aggregate Schemas One of the reasons the star schema has become so popular is that it provides strong query performance. Still, there are times when we want results to come back faster. Aggregate schemas partially summarize the data in a base schema, allowing the database to compute query results faster. Of course, designers need to identify aggregates that will provide the most help, and queries and reports will receive no benefit unless they actually use the aggregate. Aggregates are usually designed as separate tables, instead of providing multiple levels of summarization in a single star. This avoids double counting errors, and can allow the exploitation of an automated query rewrite mechanism so that applications do not need to be aware of the aggregates.

I limited this list to 10 things. That's enough to make the point: a demo schema will only take you so far. When I teach Advanced Star Schema Design courses, I find that even people with many years of experience have questions, and always learn something new.

If you want to learn more, read the books recommended in the sidebar of this blog. Take a class on Advanced Star Schema Design. Interact with your peers at The Data Warehousing Institute conferences. And keep reading this blog. There will always be more to learn.

Related Posts:

Top 10 Thinks You Should Know About that Demo Schema

© 2007 Chris Adamson