From: Subject: DBMS - August 1997 - A Dimensional Modeling Manifesto Date: Mon, 9 Mar 2009 21:05:32 +0100 MIME-Version: 1.0 Content-Type: multipart/related; type="text/html"; boundary="----=_NextPart_000_0002_01C9A0FA.C86266D0" X-MimeOLE: Produced By Microsoft MimeOLE V6.00.2900.5579 This is a multi-part message in MIME format. ------=_NextPart_000_0002_01C9A0FA.C86266D0 Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Content-Location: http://www.dbmsmag.com/9708d15.html DBMS - August 1997 - A = Dimensional Modeling Manifesto

 

 


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Dimensional modeling (DM) is the name of a logical design technique = often=20 used for data warehouses. It is different from, and contrasts with,=20 entity-relation modeling (ER). This article points out the many = differences=20 between the two techniques and draws a line in the sand. DM is the only = viable=20 technique for databases that are designed to support end-user queries in = a data=20 warehouse. ER is very useful for the transaction capture and the data=20 administration phases of constructing a data warehouse, but it should be = avoided=20 for end-user delivery.=20

What is ER?

ER is a logical design technique that seeks to = remove the=20 redundancy in data. Imagine that we have a business that takes orders = and sells=20 products to customers. In the early days of computing (long before = relational=20 databases) when we first transferred this data to a computer, we = probably=20 captured the original paper order as a single fat record with many = fields. Such=20 a record could easily have been 1,000 bytes distributed across 50 = fields. The=20 line items of the order were probably represented as a repeating group = of fields=20 embedded in the master record. Having this data on the computer was very = useful,=20 but we quickly learned some basic lessons about storing and manipulating = data.=20 One of the lessons we learned was that data in this form was difficult = to keep=20 consistent because each record stood on its own. The customer's name and = address=20 appeared many times, because this data was repeated whenever a new order = was=20 taken. Inconsistencies in the data were rampant, because all of the = instances of=20 the customer address were independent, and updating the customer's = address was a=20 messy transaction.=20

Even in the early days, we learned to separate out the redundant data = into=20 distinct tables, such as a customer master and a product master -- but = we paid a=20 price. Our software systems for retrieving and manipulating the data = became=20 complex and inefficient because they required careful attention to the=20 processing algorithms for linking these sets of tables together. We = needed a=20 database system that was very good at linking tables. This paved the way = for the=20 relational database revolution, where the database was devoted to just = this=20 task.=20

The relational database revolution bloomed in the mid 1980s. Most of = us=20 learned what a relational database was by reading Chris Date's seminal = book on=20 the subject, An Introduction to Relational Databases = (Addison-Wesley),=20 first published in the early 1980s. As we paged through Chris's book, we = worked=20 through all of his Parts, Suppliers, and Cities database examples. It = didn't=20 occur to most of us to ask whether the data was completely "normalized" = or=20 whether any of the tables could be "snowflaked," and Chris didn't = develop these=20 topics. In my opinion, Chris was trying to explain the more fundamental = concepts=20 of how to think about tables that were relationally joined. ER modeling = and=20 normalization were developed in later years as the industry shifted its=20 attention to transaction processing.=20

The ER modeling technique is a discipline used to illuminate the = microscopic=20 relationships among data elements. The highest art form of ER modeling = is to=20 remove all redundancy in the data. This is immensely beneficial to = transaction=20 processing because transactions are made very simple and deterministic. = The=20 transaction of updating a customer's address may devolve to a single = record=20 lookup in a customer address master table. This lookup is controlled by = a=20 customer address key, which defines uniqueness of the customer address = record=20 and allows an indexed lookup that is extremely fast. It is safe to say = that the=20 success of transaction processing in relational databases is mostly due = to the=20 discipline of ER modeling.=20

However, in our zeal to make transaction processing efficient, we = have lost=20 sight of our original, most important goal. We have created databases = that=20 cannot be queried! Even our simple order-taking example creates a = database of=20 dozens of tables that are linked together by a bewildering spider web of = joins.=20 (See Figure = 1, page=20 60.) All of us are familiar with the big chart on the wall of the IS = database=20 designer's cubicle. The ER model for the enterprise has hundreds of = logical=20 entities! High-end systems such as SAP have thousands of entities. Each = of these=20 entities usually turns into a physical table when the database is = implemented.=20 This situation is not just an annoyance, it is a showstopper:=20

Ever since the beginning of the relational database = revolution, IS=20 shops have noticed this problem. Many of them that have tried to deliver = data to=20 end users have recognized the impossibility of presenting these = immensely=20 complex schemas to end users, and many of these IS shops have stepped = back to=20 attempt "simpler designs." I find it striking that these "simpler" = designs all=20 look very similar! Almost all of these simpler designs can be thought of = as=20 "dimensional." In a natural, almost unconscious way, hundreds of IS = designers=20 have returned to the roots of the original relational model because they = know=20 the database cannot be used unless it is packaged simply. It is probably = accurate to say that this natural dimensional approach was not invented = by any=20 single person. It is an irresistible force in the design of databases = that will=20 always appear when the designer places understandability and performance = as the=20 highest goals. We are now ready to define the DM approach.=20

What is DM?

DM is a logical design technique that seeks to = present the=20 data in a standard, intuitive framework that allows for high-performance = access.=20 It is inherently dimensional, and it adheres to a discipline that uses = the=20 relational model with some important restrictions. Every dimensional = model is=20 composed of one table with a multipart key, called the fact table, and a = set of=20 smaller tables called dimension tables. Each dimension table has a = single-part=20 primary key that corresponds exactly to one of the components of the = multipart=20 key in the fact table. (See Figure 2.) = This=20 characteristic "star-like" structure is often called a star join. The = term star=20 join dates back to the earliest days of relational databases.=20

A fact table, because it has a multipart primary key made up of two = or more=20 foreign keys, always expresses a many-to-many relationship. The most = useful fact=20 tables also contain one or more numerical measures, or "facts," that = occur for=20 the combination of keys that define each record. In Figure 2, the facts = are=20 Dollars Sold, Units Sold, and Dollars Cost. The most useful facts in a = fact=20 table are numeric and additive. Additivity is crucial because data = warehouse=20 applications almost never retrieve a single fact table record; rather, = they=20 fetch back hundreds, thousands, or even millions of these records at a = time, and=20 the only useful thing to do with so many records is to add them up.=20

Dimension tables, by contrast, most often contain descriptive textual = information. Dimension attributes are used as the source of most of the=20 interesting constraints in data warehouse queries, and they are = virtually always=20 the source of the row headers in the SQL answer set. In Figure 2, we = constrain=20 on the Lemon flavored products via the Flavor attribute in the Product = table,=20 and on Radio promotions via the AdType attribute in the Promotion table. = It=20 should be obvious that the power of the database in Figure 2 is = proportional to=20 the quality and depth of the dimension tables.=20

The charm of the database design in Figure 2 is that it is highly=20 recognizable to the end users in the particular business. I have = observed=20 literally hundreds of instances where end users agree immediately that = this is=20 "their business."=20

DM vs. ER

Obviously, Figure 1 and Figure 2 look quite different. = Many=20 designers react to this by saying, "There must be less information in = the star=20 join," or "The star join is only used for high-level summaries." Both of = these=20 statements are false.=20

The key to understanding the relationship between DM and ER is that a = single=20 ER diagram breaks down into multiple DM diagrams. Think of a large ER = diagram as=20 representing every possible business process in the enterprise. The = master ER=20 diagram may have Sales Calls, Order Entries, Shipment Invoices, Customer = Payments, and Product Returns, all on the same diagram. In a way, the ER = diagram=20 does itself a disservice by representing on one diagram multiple = processes that=20 never coexist in a single data set at a single consistent point in time. = It's no=20 wonder the ER diagram is overly complex. Thus the first step in = converting an ER=20 diagram to a set of DM diagrams is to separate the ER diagram into its = discrete=20 business processes and to model each one separately.=20

The second step is to select those many-to-many relationships in the = ER model=20 containing numeric and additive nonkey facts and to designate them as = fact=20 tables. The third step is to denormalize all of the remaining tables = into flat=20 tables with single-part keys that connect directly to the fact tables. = These=20 tables become the dimension tables. In cases where a dimension table = connects to=20 more than one fact table, we represent this same dimension table in both = schemas, and we refer to the dimension tables as "conformed" between the = two=20 dimensional models.=20

The resulting master DM model of a data warehouse for a large = enterprise will=20 consist of somewhere between 10 and 25 very similar-looking star join = schemas.=20 Each star join will have four to 12 dimension tables. If the design has = been=20 done correctly, many of these dimension tables will be shared from fact = table to=20 fact table. Applications that drill down will simply be adding more = dimension=20 attributes to the SQL answer set from within a single star join. = Applications=20 that drill across will simply be linking separate fact tables together = through=20 the conformed (shared) dimensions. Even though the overall suite of star = join=20 schemas in the enterprise dimensional model is complex, the query = processing is=20 very predictable because at the lowest level, I recommend that each fact = table=20 should be queried independently.=20

The Strengths of DM

The dimensional model has a number of = important data=20 warehouse advantages that the ER model lacks. First, the dimensional = model is a=20 predictable, standard framework. Report writers, query tools, and user=20 interfaces can all make strong assumptions about the dimensional model = to make=20 the user interfaces more understandable and to make processing more = efficient.=20 For instance, because nearly all of the constraints set up by the end = user come=20 from the dimension tables, an end-user tool can provide high-performance = "browsing" across the attributes within a dimension via the use of bit = vector=20 indexes. Metadata can use the known cardinality of values in a dimension = to=20 guide the user-interface behavior. The predictable framework offers = immense=20 advantages in processing. Rather than using a cost-based optimizer, a = database=20 engine can make very strong assumptions about first constraining the = dimension=20 tables and then "attacking" the fact table all at once with the = Cartesian=20 product of those dimension table keys satisfying the user's constraints. = Amazingly, by using this approach it is possible to evaluate arbitrary = n-way=20 joins to a fact table in a single pass through the fact table's index. = We are so=20 used to thinking of n-way joins as "hard" that a whole generation of = DBAs=20 doesn't realize that the n-way join problem is formally equivalent to a = single=20 sort-merge. Really.=20

A second strength of the dimensional model is that the predictable = framework=20 of the star join schema withstands unexpected changes in user behavior. = Every=20 dimension is equivalent. All dimensions can be thought of as = symmetrically equal=20 entry points into the fact table. The logical design can be done = independent of=20 expected query patterns. The user interfaces are symmetrical, the query=20 strategies are symmetrical, and the SQL generated against the = dimensional model=20 is symmetrical.=20

A third strength of the dimensional model is that it is gracefully = extensible=20 to accommodate unexpected new data elements and new design decisions. = When we=20 say gracefully extensible, we mean several things. First, all existing = tables=20 (both fact and dimension) can be changed in place by simply adding new = data rows=20 in the table, or the table can be changed in place with a SQL alter = table=20 command. Data should not have to be reloaded. Graceful extensibility = also means=20 that that no query tool or reporting tool needs to be reprogrammed to=20 accommodate the change. And finally, graceful extensibility means that = all old=20 applications continue to run without yielding different results. In Figure 2, I = labeled the=20 schema with the numbers 1 through 4 indicating where you can, = respectively, make=20 the following graceful changes to the design after the data warehouse is = up and=20 running by:=20

  1. Adding new unanticipated facts (that is, new additive numeric = fields in=20 the fact table), as long as they are consistent with the fundamental = grain of=20 the existing fact table=20
  2. Adding completely new dimensions, as long as there is a single = value of=20 that dimension defined for each existing fact record=20
  3. Adding new, unanticipated dimensional attributes=20
  4. Breaking existing dimension records down to a lower level of = granularity=20 from a certain point in time forward.
A fourth strength of = the=20 dimensional model is that there is a body of standard approaches for = handling=20 common modeling situations in the business world. Each of these = situations has a=20 well-understood set of alternatives that can be specifically programmed = in=20 report writers, query tools, and other user interfaces. These modeling=20 situations include:=20
  • Slowly changing dimensions, where a "constant" dimension such as = Product=20 or Customer actually evolves slowly and asynchronously. Dimensional = modeling=20 provides specific techniques for handling slowly changing dimensions,=20 depending on the business environment. See my DBMS article of April = 1996 on=20 slowly changing dimensions.=20
  • Heterogeneous products, where a business such as a bank needs to = track a=20 number of different lines of business together within a single common = set of=20 attributes and facts, but at the same time it needs to describe and = measure=20 the individual lines of business in highly idiosyncratic ways using=20 incompatible measures.=20
  • Pay-in-advance databases, where the transactions of a business are = not=20 little pieces of revenue, but the business needs to look at the = individual=20 transactions as well as report on revenue on a regular basis. For this = and the=20 previous bullet, see my DBMS article of December 1995, the insurance = company=20 case study.=20
  • Event-handling databases, where the fact table usually turns out = to be=20 "factless." See my DBMS article of September 1996 on factless fact = tables.=20
A final strength of the dimensional model is the growing = body of=20 administrative utilities and software processes that manage and use = aggregates.=20 Recall that aggregates are summary records that are logically redundant = with=20 base data already in the data warehouse, but they are used to enhance = query=20 performance. A comprehensive aggregate strategy is required in every = medium- and=20 large-sized data warehouse implementation. To put it another way, if you = don't=20 have aggregates, then you are potentially wasting millions of dollars on = hardware upgrades to solve performance problems that could be otherwise=20 addressed by aggregates.=20

All of the aggregate management software packages and aggregate = navigation=20 utilities depend on a very specific single structure of fact and = dimension=20 tables that is absolutely dependent on the dimensional model. If you = don't=20 adhere to the dimensional approach, you cannot benefit from these tools. = Please=20 see my DBMS articles on aggregate navigation and the various products = serving=20 aggregate navigation in the September 1995 and August 1996 issues.=20

Myths About DM

A few myths floating around about dimensional = modeling=20 deserve to be addressed. Myth number one is "Implementing a dimensional = data=20 model will lead to stovepipe decision-support systems." This myth = sometimes goes=20 on to blame denormalization for supporting only specific applications = that=20 therefore cannot be changed. This myth is a short-sighted interpretation = of=20 dimensional modeling that has managed to get the message exactly = backwards!=20 First, we have argued that every ER model has an equivalent set of DM = models=20 that contain the same information. Second, we have shown that even in = the=20 presence of organizational change and end-user adaptation, the = dimensional model=20 extends gracefully without altering its form. It is in fact the ER model = that=20 whipsaws the application designers and the end users!=20

A source of this myth, in my opinion, is the designer who is = struggling with=20 fact tables that have been prematurely aggregated. For instance, the = design in=20 Figure 2 is expressed at the individual sales-ticket line-item level. = This is=20 the correct starting point for this retail database because this is the = lowest=20 possible grain of data. There just isn't any further breakdown of the = sales=20 transaction. If the designer had started with a fact table that had been = aggregated up to weekly sales totals by store, then there would be all = sorts of=20 problems in adding new dimensions, new attributes, and new facts. = However, this=20 isn't a problem with the design technique, this is a problem with the = database=20 being prematurely aggregated.=20

Myth number two is "No one understands dimensional modeling." This = myth is=20 absurd. I have seen hundreds of excellent dimensional designs created by = people=20 I have never met or had in my classes. A whole generation of designers = from the=20 packaged-goods retail and manufacturing industries has been using and = designing=20 dimensional databases for the last 15 years. I personally learned about=20 dimensional models from existing A.C. Nielsen and IRI applications that = were=20 installed and working in such places as Procter & Gamble and The = Clorox=20 Company as early as 1982.=20

Incidentally, although this article has been couched in terms of = relational=20 databases, nearly all of the arguments in favor of the power of = dimensional=20 modeling hold perfectly well for proprietary multidimensional databases = such as=20 Oracle Express and Arbor Essbase.=20

Myth number three is "Dimensional models only work with retail = databases."=20 This myth is rooted in the historical origins of dimensional modeling = but not in=20 its current-day reality. Dimensional modeling has been applied to many = different=20 business areas including retail banking, commercial banking, property = and=20 casualty insurance, health insurance, life insurance, brokerage customer = analysis, telephone company operations, newspaper advertising, oil = company fuel=20 sales, government agency spending, and manufacturing shipments.=20

Myth number four is "Snowflaking is an alternative to dimensional = modeling."=20 Snowflaking is the removal of low-cardinality textual attributes from = dimension=20 tables and the placement of these attributes in "secondary" dimension = tables.=20 For instance, a product category can be treated this way and physically = removed=20 from the low-level product dimension table. I believe that this method=20 compromises cross-attribute browsing performance and may interfere with = the=20 legibility of the database, but I know that some designers are convinced = that=20 this is a good approach. Snowflaking is certainly not at odds with = dimensional=20 modeling. I regard snowflaking as an embellishment to the cleanliness of = the=20 basic dimensional model. I think that a designer can snowflake with a = clear=20 conscience if this technique improves user understandability and = improves=20 overall performance. The argument that snowflaking helps the = maintainability of=20 the dimension table is specious. Maintenance issues are indeed leveraged = by=20 ER-like disciplines, but all of this happens in the operational data = store,=20 before the data is loaded into the dimensional schema.=20

The final myth is "Dimensional modeling only works for certain kinds = of=20 single-subject data marts." This myth is an attempt to marginalize = dimensional=20 modeling by individuals who do not understand its fundamental power and=20 applicability. Dimensional modeling is the appropriate technique for the = overall=20 design of a complete enterprise-level data warehouse. Such a dimensional = design=20 consists of families of dimensional models, where each family describes = a=20 business process. The families are linked together in an effective way = by=20 insisting on the use of conformed dimensions.=20

In Defense of DM

Now it's time to take off the gloves. I firmly = believe=20 that dimensional modeling is the only viable technique for designing = end-user=20 delivery databases. ER modeling defeats end-user delivery and should not = be used=20 for this purpose.=20

ER modeling does not really model a business; rather, it models the = micro=20 relationships among data elements. ER modeling does not have "business = rules,"=20 it has "data rules." Few if any global design requirements in the ER = modeling=20 methodology speak to the completeness of the overall design. For = instance, does=20 your ER CASE tool try to tell you if all of the possible join paths are=20 represented and how many there are? Are you even concerned with such = issues in=20 an ER design? What does ER have to say about standard business modeling=20 situations such as slowly changing dimensions?=20

ER models are wildly variable in structure. Tell me in advance how to = optimize the querying of hundreds of interrelated tables in a big ER = model. By=20 contrast, even a big suite of dimensional models has an overall = deterministic=20 strategy for evaluating every possible query, even those crossing many = fact=20 tables. (Hint: You control performance by querying each fact table = separately.=20 If you actually believe that you can join many fact tables together in a = single=20 query and trust a cost-based optimizer to decide on the execution plan, = then you=20 haven't implemented a data warehouse for real end users.)=20

The wild variability of the structure of ER models means that each = data=20 warehouse needs custom, handwritten and tuned SQL. It also means that = each=20 schema, once it is tuned, is very vulnerable to changes in the user's = querying=20 habits, because such schemas are asymmetrical. By contrast, in a = dimensional=20 model all dimensions serve as equal entry points to the fact table. = Changes in=20 users' querying habits don't change the structure of the SQL or the = standard=20 ways of measuring and controlling performance.=20

ER models do have their place in the data warehouse. First, the ER = model=20 should be used in all legacy OLTP applications based on relational = technology.=20 This is the best way to achieve the highest transaction performance and = the=20 highest ongoing data integrity. Second, the ER model can be used very=20 successfully in the back-room data cleaning and combining steps of the = data=20 warehouse. This is the ODS, or operational data store.=20

However, before data is packaged into its final queryable format, it = must be=20 loaded into a dimensional model. The dimensional model is the only = viable=20 technique for achieving both user understandability and high query = performance=20 in the face of ever-changing user questions.=20


Ralph Kimball was coinventor of the Xerox Star workstation, the first = commercial=20 product to use mice, icons, and windows. He was vice president of = applications=20 at Metaphor Computer Systems and is the founder and former CEO of Red = Brick=20 Systems. He now works as an independent consultant designing large data=20 warehouses. His book The Data Warehouse Toolkit: How to Design = Dimensional Data=20 Warehouses (John Wiley, 1996) is now available. You can reach Ralph = through his=20 Web page at www.rkimball.com.=20


Figure 1.


--A=20 high-level overview of an ER model. Each box on the diagram is actually = many=20 entities. Each business process shown is probably a separate legacy = application.=20 The equivalent DM design would isolate each business process and = surround it=20 with just its relevant dimensions.

Figure 2.


--A=20 detailed dimensional model for retail point of sales. Numbers 1 through = 4 show=20 places where the design may be gracefully extended.


Dimensional modeling resources:

  • The Data Warehouse Toolkit: Practical Techniques for Building = Dimensional=20 Data Warehouses, Ralph Kimball, John Wiley, 1996.=20
  • OLAP Solutions: Building Multidimensional Information = Systems, Eric=20 Thomsen, John Wiley, 1997.=20
  • Planning and Designing the Data Warehouse, Ramon Barquin and = Herb=20 Edelstein, chapter 10, Prentice Hall PTR, 1996.=20
  • Building a Data Warehouse for Decision Support, Vidette Poe, = Prentice=20 Hall, 1995.=20

    What did you think of this article? Send a=20 letter to the editor.=20


    Subscribe to=20 DBMS and Internet Systems -- It's = free=20 for qualified readers in the United States
    August 1997 Table of = Contents |=20 Other Contents | Article Index | Search | Site Index | Home

    DBMS and Internet Systems (http://www.dbmsmag.com/)
    Copyrigh= t =A9 1997=20 Miller Freeman, Inc. ALL RIGHTS RESERVED
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    Please send questions or comments to dbms@mfi.com
    Updated Thursday, July = 10, 1997 3D""=203D""=203D""=20
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