Continue reading »]]> We have many different ways to poll the Oracle database such as: Select Statements, functions or store procedures. I am going to write about a specific way of polling Oracle Database: Store Procedures. We have to be very careful writing the parameters properly.

I am going to describe the step we have to follow to import the schemas and binding from the Oracle database.

First of all we have to select the binding we are going to use, and then we have to connect to the Oracle database, for that we have to introduce the username and password as show below it.

Then we have to specified the Data source name where the store procedure is, and in this case specified a polling Id, this field can be fill with anything you want, I mean you can use numbers and/or  letters.

In this step we can configure the binding properties like that:

• Polled Data available Statement: Indicates the query which tells us if there are any data to poll.
• Polling actions: Indicates the URL where the Store procedure is, its name looks something like that:

http://Microsoft.LobServices.OracleDB/2007/03/PP/PackageName/StoreProcedureName

• Polling statement: It is a XML Code which indicates the store procedure we want to use to get the data we are looking for, and the parameters we have to send to it to get the information. Here is very important that all the fields are in capital letter, if not we are going to get and error.

When you have imported the schemas, you have to build and deploy the application in visual studio. Then in Biztalk Server Administration console you have to import the binding which was generated when you import the schemas.

Continue reading »]]> In my previous blog I exposed something about dimensions such as important part of BI. Now I am going to expose another essential part of BI: Data Mart and/or Data Warehouse making emphasis in the differences that Ralph Kimball and Bill Inmon have between those two definitions.

Gartner Group is an information technology research; it has a Business Intelligence definition that I particularly like: “Set of tools that provide the capacity to business users to analyze a huge amount of data and extract valuable information, which they can use to support fast decision in a highly competitive market”. There is a part that they doesn’t mention and I would like to add something to that definition, with BI we can create knowledge through the data analysis that exist in organizations and companies, that is call Data Mining (It will be my next blog).
Business Intelligence helps to resolve some paradigms existent:

• Users don’t have the correct information in the right time.
• They don’t have a unique true of the information.
• It is difficult to find, consolidate or have a specific detail of the information.

Then, when all those paradigms are resolved the organization gain competitiveness because of the decisions can be taken faster, making decision is much better in all organizations levels and they can make coordinated decisions alignedto all company processes. In the same way the value of BI consists on integration of data from multiple sources, share analyzes with different users, create a scalable infrastructure, so on.
The previous information is a general view about BI. Now I am going to focus the rest of the blog in the design phase because of my main interest is talk about Data Mart and Data Warehouse.

In the pictures below, we can notice easily the main differences between Ralph and Inmon definition.

Ralph’s definition says:  Data Warehouse is the result of the union of all small Data Marts inside the organization. For that reason the information is always store in dimensional models.

Bill’s definition instead says: Data Warehouse is only a part of all organization Business Intelligence system. An organization has a Data Warehouse and additionally has a Data Marts that take information from Data Warehouse. In the Data Warehouse the information is in third normal form. However the information in the Data Marts is non-normalized and multidimensional.

In this point I will introduce the two implementation strategies that the authors created, its advantages and disadvantages:

• Top down by Bill Inmon
  • The first step is to build the Data Warehouse and then extract information needed to generate the Data Marts.
  • The Data marts are dimensional models
  • Advantages:
    • More flexibility
• Disadvantages:
  • More time in building up
  • The cost is higher

• Bottom up by Ralph Kimball
  • Data marts are built progressively align with the business priority, and like this you are building the Data warehouse. “Think global, act Local”
  • Advantages:
    • Solutions smaller and faster to build
    • Solutions easier to understand
  • Disadvantages
    • Complex maintenance
    • Synchronizing problems

Continue reading »]]> In many occasions we have heard about Data Mining but, what is it exactly and when do we have to use it?. Well, I am going to start with some basis definitions I have collected from different sources and authors and I have made a nice combination (from my point of view) that I will share in this post.

• What is it?

Data Mining is an extraction activity and its objective is discovering facts which are in the data base. In the same way it enables you to deduce hidden knowledge by examining or training the data. The knowledge founded is expressed in patterns and rules.

• When do we have to use it or when is it useful?
  • Systems partially unknown
  • Huge number of data
  • Powerful hardware and software

Data mining is very useful in many fields such as: Marketing, government, medicine, sales and production. Data mining algorithms are part important in this process, a key to data mining success is selecting an algorithm that is appropriate for your specific task.

In the figure below I show general information of how each algorithms work, its characteristics and the specifics cases when we use it in a particular case.

Continue reading »]]> When we are talking about Business Intelligence (BI) we immediately think about business strategy and that is because BI helps any type of company to reach their goals. A dimension is an important part of all this great Business Intelligence´s world.

When we are modeling a multidimensional Data warehouse we have to deal with different things that depend on the specific context and company´s needs. Thus, in this post I am going to expose 3 types of dimension we have when we work with data marts or data warehouse. In the headline of this post I have wrote Data warehouse or data mart that is because of the differences that Ralph Kimball and Bill Inmon have when they defined the word: Data mart (I will write about it later on) , but at the end, we have dimension to work with.

The following part describes the differences between the three types of dimensions:

Type 1: You use it, when you don’t need to have all the history changes of the data. It means that in the future you can’t see the changes that a specific row has had in a period of time. In this case there is not way to find out the old value(s) because it has only the current values of a specific field. Normally, you use UPDATE to manage it. In other words you replace the old value with the new value.

Example: The Row 2 holds specific values in each field. We will suppose the Field 3 changed its value and now it will be:

Table 1 shows us its values before any change is applied.

Table 1 Dimension before the change

Table 2 shows us the results after we have made the change (Update).

Table 2 Dimension after the change (Type 1)

Type 2: When is important to have all changes in the dimension this one is the type you should use. In this case you manage it adding a new record if any value has changed. Here you use INSERT instead of update because you need to keep all the information change during a period of time. The old values don’t be replaced because the new row contains the new values and if your intention is compare that information you can easily retrieve them. This kind of dimension grows ’vertically’

Example: The Row 2 holds specific values in each field. We will suppose the Field 3 changed its value and now it will be:

Table 1 shows us its values before any change is applied.

Table 1 Dimension before the change

Table 2 shows us the results after we have made the change (INSERT). In this case we have added a new record: (Row 3) in the table with the same values in the fields that have not changed (1,2,4,5) and the new value in the field that has changed (3).

Table 2 Dimension after the change (Type 2)

Type 3: In this case the dimension grows ‘horizontally’, it means that you keep the new values adding a new field in the table and so on.

Example: The Row 2 holds specific values in each field. We will suppose the Field 3 changed its value and now it will be:

Table 1 shows us its values before any change is applied.

Table 1 Dimension before the change

Table 2 shows us the results after we have made the change. In this case we have added a new field: (Field 6) in the table and the new value in the field that has changed.

Table 2 Dimension after the change (Type 3)