Oracle Warehouse Builder 11g: Getting Started. Copyright © .. We will build a basic data warehouse using Oracle Warehouse Builder. It has thetwestperlnetself.ga, covers each of these. Oracle Warehouse Builder 11gR2: Getting Started Extract, Transform, and Load data to build a dynamic, operational data warehouse Bob Griesemer. Oracle Warehouse Builder 11gR2: Getting Started Extract, Transform, and Load data to build a dynamic, operational data warehouse. Bob Griesemer. 1.
|Language:||English, Spanish, Dutch|
|Genre:||Academic & Education|
|Distribution:||Free* [*Sign up for free]|
Extract, Transform, and Load data to build a dynamic, operational data warehouse with Oracle Warehouse Builder 11g R2 with this book and eBook Build a. P U B L I S H I N G professional expertise distilled Oracle Warehouse Builder 11gR2: Getting Started Bob Griesemer Chapter No.3 "Designing the Target. 年4月20日 There's a revised edition of the Bob Griesemer OWB book from Packt Publishing - Oracle Warehouse Builder 11gR2: Getting Started
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. 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.
But we will look at the relational implementation in greater detail as that is the one we're going to use throughout the remainder of the book for our data warehouse project. The diagrams presented showed all the tables interconnected, and we discussed the use of foreign keys in a table to refer to a row in another table. That is fundamentally a relational database. 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 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. We discussed normalization back in Chapter 2, which involved the use of foreign key references to information in other tables to lessen the duplication and improve data accuracy.
For a data warehouse, however, 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. 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 downloads 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.
The Kimball book on Dimensional Modeling was referred to at the beginning of Chapter 2. This book discusses some limited circumstances where it might be acceptable to implement a snowflake design, but it is highly discouraged for most cases. 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.
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. 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.
This will make the concepts we just discussed more concrete, and will form the basis for the work we do in the rest of the book as we implement this design. We'll then close out this chapter with a discussion on designing in the Warehouse Builder, where we'll see how it can support either of these implementations. 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.
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, as we saw earlier. We used that to illustrate the cube concept and to show a star schema representation of it, so the information shows us the dimensions we need.
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.
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.
Add up the daily totals to get the totals for the month, and add up 12 monthly totals to get the yearly sales. 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.
We'll discuss this further in the next chapter when we build our time dimension in the Warehouse Builder. 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 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. It is explained in more detail in the Kimball book on dimensional modeling we talked about earlier.
There are many other aspects to dimensional design that we don't have the space to cover here, but are covered in the Kimball book in more detail. It would be a good idea for you to read this book or a similar one to get a better understanding of the detailed dimensional modeling concepts such as this.
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.
OWB currently supports designing a target schema only in an Oracle database, and so we will find the objects all under the Oracle node in the Projects tab. Let's launch Design Center now and have a look at it. But before we can see any objects, we have to have an Oracle module defined to contain the objects.
We created this in the last chapter when we imported our metadata from that source. If that is the case, our Projects tab window will look similar to the following: Creating a target user and module We need a different module to create our target objects in.
So before going any further, let's create a new module in the Projects tab for our target to hold our data warehouse design objects. However, before we can do that, we should have a target schema defined in the database that will hold our target objects when we deploy them. So, it can be confusing to know exactly where our main data warehouse is going to be located. The target schema is going to be the main location for the data warehouse.
When we talk about our "data warehouse" after we have it all constructed and implemented, the target schema is what we will be referring to. Amid all these different components we discussed that compose the Warehouse Builder, the target is where the actual data warehouse will be built.
Our design will be implemented there, and the code will be deployed to that schema by OWB to load the target structure with data from the sources. Every target module must be mapped to a target user schema. Back in Chapter 1, when we ran the Repository Assistant to create the repository and workspace, we created the acmeowb user as the repository owner and mentioned that this user can be a deployment target for our data warehouse.
However, it does not have to be the target user. It's a good idea to create a separate user schema to become the target so that user roles in our database can be kept separate. Using the OWB repository owner schema would mean our target data warehouse would have to be on the same database server as our repository. In large installations, that will most likely not be the case. So for maximum flexibility, we're going to create a separate user schema.
In our case, that user will be created in the same database as the repository; but it can be moved to another database easily if we expand and add more servers.
Creating a target user There are a couple of ways we can go about creating our target user create the user directly in the database and then add to OWB, or use OWB to physically create the user.
If we have to create a new user, and if it's on the same database as our repository and workspaces, it's a good idea to use OWB to create the user, especially if we are not that familiar with the SQL command to create a user.
However, if our target schema were to be in another database on another server, we would have to create the user there. It's a simple matter of adding that user to OWB as a target, which we'll see in a moment. Let's begin in the Design Center under the Globals tab. We talked about that Globals tab back in our introduction to the Design Center in Chapter 2.
There we said it was for various objects that pertained to the workspace as a whole. One of those object types is a Users object that exists under the Security node as shown here: [ ] 21 Chapter 3 Right-click on the Users node and select New User We create a workspace user by selecting a database user that already exists or create a new one in the database.
In addition to previously announced graduate programs in scoring for film and multimedia, electronic composition and audio design, global entertainment and music business, symphonic band studies, and studio performance, recording, acoustic and technology systems innovation and design, multi-focus music technologies, and manipulation of computer-generated images that are musically integrated with computer-based musical composition and sound design.
The acclaimed book by Michael Hewitt is available at eBooks. Composition for Computer Musicians. Description: Many DJs, gigging musicians, and electronic music producers understand how to play their instruments or make music on the computer, but they lack the basic knowledge of music th. download Music Theory for Computer Musicians in ebook format.
It not only teaches music theory using traditional notation, but also with the more modern notation of the piano roll. My biggest hobby remains music. You'll also learn about mixing and mastering your track and distributing it to a mass audience. Analysis of musical creativity in middle school students through composition using computer-assisted instruction: A multiple case study. Experimental Electronic Music: The Heart Chamber Orchestra is an audiovisual performance where musicians control a computer composition and visualization environment with there heartbeats.
As a former trumpet player, current drummer and bass player, I spent a lot of time studying solfege, and a little bit of composition.