Oracle Warehouse Builder 11g R2: Getting Started 2011 — Save 50%
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This article explains designing the data warehouse target. It covers some options for defining a data warehouse target structure using relational objects (star schemas and snowflake schemas) and dimensional objects (cubes and dimensions). Some of the pros and cons of the usage of these objects are also covered.
In this article by Bob Griesemer, author of Oracle Warehouse Builder 11g R2: Getting Started 2011, specifically we will cover:
- Dimensional design
- Cube and dimensions
- Dimensional Model Implementation
- Relational (star schema)
- Multidimensional (OLAP)
- Designing the ACME data warehouse
- Identifying dimensions
- Designing the cube
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When it comes to the design of a data warehouse, there is basically one option that makes the most sense for how we will structure our database and that is the dimensional model. This is a way of looking at the data from a business perspective that makes the data simple, understandable, and easy to query for the business end user. It doesn't require a database administrator to be able to retrieve data from it.
A normalized model removes redundancies in data by storing information in discrete tables, and then referencing those tables when needed. This has an advantage for a transactional system because information needs to be entered at only one place in the database, without duplicating any information already entered. For example, in the ACME Toys and Gizmos transactional database, each time a transaction is recorded for the sale of an item at a register, a record needs to be added only to the transactions table. In the table, all details regarding the information to identify the register, the item information, and the employee who processed the transaction do not need to be entered because that information is already stored in separate tables. The main transaction record just needs to be entered with references to all that other information.
This works extremely well for a transactional type of system concerned with daily operational processing where the focus is on getting data into the system. However, it does not work well for a data warehouse whose focus is on getting data out of the system. Users do not want to navigate through the spider web of tables that compose a normalized database model to extract the information they need. Therefore, dimensional models were introduced to provide the end user with a flattened structure of easily queried tables that he or she can understand from a business perspective.
A dimensional model takes the business rules of our organization and represents them in the database in a more understandable way. A business manager looking at sales data is naturally going to think more along the lines of "How many gizmos did I sell last month in all stores in the south and how does that compare to how many I sold in the same month last year?" Managers just want to know what the result is, and don't want to worry about how many tables need to be joined in a complex query to get that result. A dimensional model removes the complexity and represents the data in a way that end users can relate to it more easily from a business perspective.
Users can intuitively think of the data for the above question as a cube, and the edges (or dimensions) of the cube labeled as stores, products, and time frame. So let's take a look at this concept of a cube with dimensions, and how we can use that to represent our data.
Cube and dimensions
The dimensions become the business characteristics about the sales, for example:
- A time dimension—users can look back in time and perform time series analysis, such as how a quarter compares to the same quarter last year
- A store dimension—information can be retrieved by store and location
- A product dimension—various products for sale can be broken out
Think of the dimensions as the edges of a cube, and the intersection of the dimensions as the measure we are interested in for that particular combination of time, store, and product. A picture is worth a thousand words, so let's look at what we're talking about in the following image:
Notice what this cube looks like. How about a Rubik's Cube? We're doing a data warehouse for a toy store company, so we ought to know what a Rubik's cube is! If you have one, maybe you should go get it now because that will exactly model what we're talking about. Think of the width of the cube, or a row going across, as the product dimension. Every piece of information or measure in the same row refers to the same product, so there are as many rows in the cube as there are products. Think of the height of the cube, or a column going up and down, as the store dimension. Every piece of information in a column represents one single store, so there are as many columns as there are stores. Finally, think of the depth of the cube as the time dimension, so any piece of information in the rows and columns at the same depth represent the same point in time. The intersection of each of these three dimensions locates a single individual cube in the big cube, and that represents the measure amount we're interested in. In this case, it's dollar sales for a single product in a single store at a single point in time.
But one might wonder if we are restricted to just three dimensions with this model. After all, a cube has only three dimensions—length, width, and depth. Well, the answer is no. We can have many more dimensions than just three. In our ACME example, we might want to know the sales each employee has accomplished for the day. This would mean we would need a fourth dimension for employees. But what about our visualization above using a cube? How is this fourth dimension going to be modeled? And no, the answer is not that we're entering the Twilight Zone here with that "dimension not only of sight and sound but of mind..." We can think of additional dimensions as being cubes within a cube. If we think of an individual intersection of the three dimensions of the cube as being another cube, we can see that we've just opened up another three dimensions to use—the three for that inner cube. The Rubik's Cube example used above is good because it is literally a cube of cubes and illustrates exactly what we're talking about.
We do not need to model additional cubes. The concept of cubes within cubes was just to provide a way to visualize further dimensions. We just model our main cube, add as many dimensions as we need to describe the measures, and leave it for the implementation to handle.
This is a very intuitive way for users to look at the design of the data warehouse. When it's implemented in a database, it becomes easy for users to query the information from it.
Implementation of a dimensional model in a database
We have seen how a dimensional model is preferred over a normalized model for designing a data warehouse. Now before we finalize our model for the ACME Toys and Gizmos data warehouse, let's look at the implementation of the model to see how it gets physically represented in the database. There are two options: a relational implementation and a multidimensional implementation. The relational implementation, which is the most common for a data warehouse structure, is implemented in the database with tables and foreign keys. The multidimensional implementation requires a special feature in a database that allows defining cubes directly as objects in the database. Let's discuss a few more details of these two implementations.
Relational implementation (star schema)
The term relational is used because the tables in it relate to each other in some way. We can't have a POS transaction without the corresponding register it was processed on, so those two relate to each other when represented in the database as tables.
For a relational data warehouse design, the relational characteristics are retained between tables. But a design principle is followed to keep the number of levels of foreign key relationships to a minimum. It's much faster and easier to understand if we don't have to include multiple levels of referenced tables. For this reason, a data warehouse dimensional design that is represented relationally in the database will have one main table to hold the primary facts, or measures we want to store, such as count of items sold or dollar amount of sales. It will also hold descriptive information about those measures that places them in context, contained in tables that are accessed by the main table using foreign keys. The important principle here is that these tables that are referenced by the main table contain all the information they need and do not need to go down any more levels to further reference any other tables.
The ER diagram of such an implementation would be shaped somewhat like a star, and thus the term star schema is used to refer to this kind of an implementation. The main table in the middle is referred to as the fact table because it holds the facts, or measures that we are interested in about our organization. This represents the cube that we discussed earlier. The tables surrounding the fact table are known as dimension tables. These are the dimensions of our cube. These tables contain descriptive information, which places the facts in a context that makes them understandable. We can't have a dollar amount of sales that means much to us unless we know what item it was for, or what store made the sale, or any of a number of other pieces of descriptive information that we might want to know about it.
It is the job of data warehouse design to determine what pieces of information need to be included. We'll then design dimension tables to hold the information. Using the dimensions we referred to above in our cube discussion as our dimension tables, we have the following diagram that illustrates a star schema:
Of course our star only has three points, but with a much larger data warehouse of many more dimensions, it would be even more star-like. Keep in mind the principle that we want to follow here of not using any more than one level of foreign key referencing. As a result, we are going to end up with a de-normalized database structure. For a data warehouse, the query time and simplicity is of paramount importance over the duplication of data. As for the data accuracy, it's a read-only database so we can take care of that up front when we load the data. For these reasons, we will want to include all the information we need right in the dimension tables, rather than create further levels of foreign key references. This is the opposite of normalization, and thus the term de-normalized is used.
Let's look at an example of this for ACME Toys and Gizmos to get a better idea of what we're talking about with this concept of de-normalization. Every product in our stores is associated with a department. If we have a dimension for product information, one of the pieces of information about the product would be the department it is in. In a normalized database, we would consider creating a department table to store department descriptions with one row for each department, and would use a short key code to refer to the department record in the product table.
However, in our data warehouse, we would include that department information, description and all, right in the product dimension. This will result in the same information being duplicated for each product in the department. What that buys us is a simpler structure that is easier to query and more efficient for retrieving information from, which is key to data warehouse usability. The extra space we consume in repeating the information is more than paid for in the improvement in speed and ease of querying the information. That will result in a greater acceptance of the data warehouse by the user community who now find it more intuitive and easier to retrieve their data.
In general, we will want to de-normalize our data warehouse implementation in all cases, but there is the possibility that we might want to include another level—basically a dimension table referenced by another dimension table. In most cases, we will not need nor want to do this and instances should be kept to an absolute minimum; but there are some cases where it might make sense.
This is a variation of the star schema referred to as a snowflake schema because with this type of implementation, dimension tables are partially normalized to pull common data out into secondary dimension tables. The resulting schema diagram looks somewhat like a snowflake. The secondary dimension tables are the tips of the snowflake hanging off the main dimension tables in a star schema.
In reality, we'd want at the most only one or two of the secondary dimension tables; but it serves to illustrate the point. A snowflake dimension table is really not recommended in most cases because of ease-of-use and performance considerations, but can be used in very limited circumstances.
Let's now talk a little bit about the multidimensional implementation of a dimensional model in the database, and then we'll design our cube and dimensions specifically for the ACME Toys and Gizmos Company data warehouse.
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Multidimensional implementation (OLAP)
A multidimensional implementation or OLAP (online analytic or analytical processing) requires a database with special features that allow it to store cubes as actual objects in the database, and not just tables that are used to represent a cube and dimensions. It also provides advanced calculation and analytic content built into the database to facilitate advanced analytic querying. Oracle's Essbase product is one such database and was originally developed by Hyperion. Oracle recently acquired Hyperion, and is now promoting Essbase as a tool for custom analytics and enterprise performance management applications. The Oracle Database Enterprise Edition has an additional feature that can be licensed called OLAP that embeds a full-featured OLAP server directly in an Oracle database. This is an option organizations can leverage to make use of their existing database.
These kinds of analytic databases are well suited to providing the end user with increased capability to perform highly optimized analytical queries of information. Therefore, they are quite frequently utilized to build a highly specialized data mart, or a subset of the data warehouse, for a particular user community. The data mart then draws its data to load from the main data warehouse, which would be a relational dimensional star schema. A data warehouse implementation may contain any number of these smaller subset data marts.
We'll be designing dimensionally and implementing relationally, so let's now design our actual dimensions that we'll need for our ACME Toys and Gizmos data warehouse, and talk about some issues with the fact data (or cube) that we'll need.
We have seen the word dimension used in describing both a relational implementation and a multidimensional implementation. It is even in the name of the second implementation method we discussed, so why does the relational method use it also? In the relational case, the word is used more as an adjective to describe the type of table taken from the name of the model being implemented; whereas in the multidimensional model it's more a noun, referring to the dimension itself that actually gets created in the database. In both cases, the type of information conveyed is the same—descriptive information about the facts or measures—so its use in both cases is really not contradictory. There is a strong correlation between the fact table of the relational model and the cube of the dimensional model, and between the dimension tables of the relational model and the dimensions of the dimensional model.
Designing the ACME data warehouse
We have chosen to use a dimensional model for our data warehouse, so we'll define a cube with dimensions to represent our information. Let's lay out a basic structure of information we want each to contain. We'll begin with the dimensions, since they are going to provide the context for the measure(s) we will want to store in our cube.
Identifying the dimensions
To know what dimensions to design for, we need to know what business process we're going to be supporting with our data warehouse. Is management concerned with daily inventory? How about daily sales volume? This information will guide us in selecting the correct parts of the business to model with our dimensions.
We are going to support the sales managers in managing the daily sales of the ACME Toys and Gizmos Company, and they have already given us an example of the kind of question they want answered from their data warehouse. Since management is concerned with daily sales, we need some kind of date/time dimension that will provide us the context for the sales data indicating what day the sale transaction took place.
We can pretty much be guaranteed that we will need a time/date type dimension for any data warehouse we design, since one of the main features of data warehouses is to provide time-series type analytical query capabilities.
Are we going to need both the time and the date in this dimension, or will just the date be sufficient? We can get an answer to this question by also looking back at our business process, which showed that management is concerned with daily sales volume. Also, the implementation of the time dimension in OWB does not include the time of day since it would have to include 24 hours of time values for each day represented in the dimension due to the way it implements the dimension. In the future if time is needed, there are options for creating a separate dimension just for modeling time of day values. For our initial design, we'll call our time related dimension a Date dimension just for added clarity.
Another dimension we have included is to model the product information. Each sale transaction is for a particular product, and management has indicated they are concerned about seeing how well each product is selling. So we will include a dimension that we shall call Product. At a minimum we need the product name, a description of the product, and the cost of the product as attributes of our product dimension—so we'll include those in our logical model.
So far we have a Date dimension to represent our time series and a Product dimension to represent the items that are sold. We could stop there. Management would then be able to query for sales data for each day for each product sold by ACME Toys and Gizmos, but they wouldn't be able to tell where the sale took place. Another key piece of information the management would like to be able to retrieve is how well the stores are doing compared to each other for daily sales. Unless we include some kind of a location dimension, they will not be able to tell that. That is why we have included a third dimension called Store. It is used to maintain the information about the store that processed the sales transaction. For attributes of the store dimension, we can include the store name and address at a minimum to identify each store.
These dimensions should be enough to satisfy the management's need for querying information for this particular business process—the daily sales. We could certainly include a large number of other dimensions, but we'll stop here to keep this simple for our first data warehouse. We can now consider designing the cube and what information to include in it.
Designing the cube
In the case of the ACME Toys and Gizmos Company, we have seen that the main measure the management is concerned about is daily sales. There are other numbers we could consider such as inventory numbers: How much of each item is on hand? However, the inventory is not directly related to daily sales and wouldn't make sense here. We can model an inventory system in a data warehouse that would be separate from the sales portion. But for our purposes, we're going to model the sales. Therefore, our main measure is going to be the dollar amount of sales for each item.
A very important topic to consider at this point is what will be the grain of the measure—the sales data—that we're going to store in our cube? The grain (or granularity) is the level that the sales number refers to. Since we're using sales as the measure, we'll store a sales number; and from our dimensions, we can see that it will be for a given date in a given store and for a given product. Will that number be the total of all the sales for that product for that day? Yes, so it satisfies our design criteria of providing daily sales volume for each product. That is the smallest and lowest level of sales data we want to store. This is what we mean by the grain or granularity of the data.
A dimensional model is naturally able to handle this concept of the different levels of data by being able to model a hierarchy within a dimension. The time/date dimension is an easy example of using of various levels. Add up the daily totals to get the totals for the month, and add up 12 monthly totals to get the yearly sales. The time/date dimension just needs to store a value to indicate the day, month, and year to be able to provide a view of the data at each of those levels. Combining various levels together then defines a hierarchy. By storing data at the lowest level, we make available the data for summing at higher levels. Likewise, from a higher level, the data is then available to drill down to view at a lower level. If we were to arbitrarily decide to store the data at a higher level, we would lose that flexibility.
In this case, we have a source system—the POS Transactional system—that maintains the dollar amount of sales for each line item in each sales transaction that takes place. This can provide us the level of detail we will want to capture and maintain in our cube, since we can definitely capture sales for each product at each store for each day. We have found out that the POS Transactional system also maintains the count of the number of a particular item sold in the transaction. This is an additional measure we will consider storing in our cube also, since we can see that it is at the same grain as the total sales. The count of items would still pertain to that single transaction just like the sales amount, and can be captured for each product, store, and even date.
The only other pieces of information our cube is going to contain are pointers to the dimensions. In the relational model, the fact table would contain columns for the dollar amount, the quantity, the unit cost, and then foreign keys for each of the dimension tables.
There are times when it's valid in dimensional design to include more descriptive information right in the cube, rather than create a dimension for it. There may be some particularly descriptive piece of information that stands all by itself, which is not associated with anything else or whose additional descriptive information has already been included in other dimensions. In that case, it wouldn't make sense to create a whole dimension just for it; so it is included directly in the fact table or cube. This is referred to as a degenerate dimension.
Our design is drawn out in a star schema configuration showing the cube, which is surrounded by the dimensions with the individual items of information (attributes) we'll want to store for each. It looks like the following:
OK, we now have a design for our data warehouse. It's time to see how OWB can support us in entering that design and generating its physical implementation in the database.
We have now gone through the process of designing the target structure for our data warehouse. We began with a very high-level overview of data warehouse design topics, then talked about dimensional design and the relational versus multidimensional implementation, and then we discussed the differences between them.
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About the Author :
Bob Griesemer has over 27 years of software and database engineering/DBA experience in both government and industry, solving database problems, designing and loading data warehouses, developing code, leading teams of developers, and satisfying customers. He has been working in various roles involving database development and administration with the Oracle Database with every release since Version 6 of the database from 1993 to the present. He has also been performing various tasks, including data warehouse design and implementation, administration, backup and recovery, development of Perl code for web-based database access, writing Java code utilizing JDBC, migrating legacy databases to Oracle, and developing Developer/2000 Oracle Forms applications. He is currently an Oracle Database Administrator Certified Associate, and is employed by the Northrop Grumman Corporation, where he is currently a Senior Database Analyst on a large data warehouse project.