Role Playing dimension in Data warehouse

In last  few tutorial , we started talking about various Type of dimension in Data ware house  and what role they play in data warehouse.Last time  we talked about Conformed dimension and Junk Dimension . Data warehouse mainly consists of Dimension and Fact tables.   In below article we will go through the Role Playing Dimension in dataware house with some example , why they are important.Hope you will enjoy this small data warehouse tutorial.

Role PlayingDimension in Data warehouse

A dimension is Role playing dimension , with multiple valid relationships between itself and another table . This is most commonly seen in dimensions such as Time and Customer.

For example :In below Order Fact Table  , it has multiple relationships to the Date dimension on the keys Order_date ,shipping_date. Now to handle this situation instead of creating two separate dimension table we can create two views of original date dimensions table one is Order_date_dim and another is ship_date_dim

 

I found a good article on Role Playing dimension in data ware house here

I also found some good article on other various type of dimension as well.

• Conformed dimension 
• Junk dimension
• Degenerate Dimension

Degenerate dimension in Data warehouse

In last  few tutorial , we started talking about various Type of dimension in Data ware house  and what role they play in data warehouse.Last time  we talked about Conformed dimension and Junk Dimension . . Data warehouse mainly consists of Dimension and Fact tables.   In below article we will go through the Degenerate Dimension in dataware house with some example , why they are important.Hope you will enjoy this small data warehouse tutorial.

Degenerate Dimension in Data warehouse

A Degenerate dimension is a Dimension which has only a single attribute. This dimension is typically represented as a single field in a fact table.The data items thar are not facts and data items that do not fit into the existing dimensions are termed as Degenerate Dimensions.

 For example : In below Fact Table with customer_id, product_id, bill_no, date in key section and price, quantity in measure section. In this fact table, bill_no from key section is a single value, it has no associated dimension table. Instead of creating a  separate dimension table for that single value, we can include it in fact table to improve performance. So here the column, bill_no is a degenerate dimension or line item dimension. -

 

I found a good article on Degenerate dimension in data ware house here

I also found some good article on other various type of dimension as well.

• Conformed dimension 
• Junk dimension
• Role Playing Dimension

Junk dimension in Data warehouse

In last tutorial , we have gone through some of the basic concept of data warehouse , what exactly data warehouse store ,importance of data warehouse. We also saw various properties of  data warehouse. Data warehouse mainly consists of Dimension and Fact tables. We will go through different type of dimension mainly used in data warehouse in upcoming articles.  In below article we will go through the Junk Dimension in dataware house with some example , why they are important.Hope you will enjoy this small data warehouse tutorial.

Junk Dimension in Data warehouse

A junk dimension is grouping of low cardinality flags and indicators. This junk dimension helps in avoiding cluttered design of data warehouse. Provides an easy way to access the dimensions from a single point of entry and improves the performance of sql queries.

 For example : For example, assume that there are two dimension tables (gender and marital status). The data of these two tables are shown below:

I found a good article on junk dimension in data ware house here

I also found some good article on other various type of dimension as well.

• Conformed dimension 
• Degenerate dimension
• Role Playing Dimension

Conformed dimension in Datawarehouse with Example

In last tutorial , we have gone through some of the basic concept of data warehouse , what exactly data warehouse store ,importance of data warehouse. We also saw various properties of  data warehouse. Data warehouse mainly consists of Dimension and Fact tables. We will go through different type of dimension mainly used in data warehouse in upcoming articles.  In below article we will go through the Conformed Dimension in dataware house with some example , why they are important.Hope you will enjoy this small data warehouse tutorial.

Conformed Dimension in Data warehouse

In dataware house , conformed Dimension is the dimension which has the same meaning and content when being referred from different fact tables. A conformed dimension can refer to multiple tables in multiple data marts within the same organization. For example : Time is a common conformed dimension because its attributes (day, week, month, quarter, year, etc.) have the same meaning when joined to any fact table. Similarly Customer dimension will have the same meaning irrespective of which FACT table we are referring to.

I found a good article on conformed dimension in data ware house here

I also found some good article on other various type of dimension as well.

• Junk dimension 
• Degenerate dimension
• Role Playing Dimension

Data Warehousing Concepts with Examples

A data warehouse is the concept of data extracted from operational systems and made available as historical snapshots for ad-hoc queries and scheduled reporting. Data warehouse help in determining the effectiveness of business processes, create policy, forecast trends, analyze the market and much more . Below snapshot  gives simple view how data warehouse data is prepared and provide the reporting capabilities to Business analysts.


Data warehouse example

 See also : I found a good article on the difference Difference between Data warehouse data and Operational Data 

Key Features :

A common way of introducing data warehousing is to refer to the characteristics of a data warehouse as set forth by William Inmon:

• Subject Oriented:
• Integrated
• Nonvolatile
• Time Variant :

For more  detailed article on this data warehouse concept , do visit here.  I hope you like this small introductory article on data warehouse concept .

Dynamic Cache in Informatica Lookup Transformation

As we saw in our previous informatica tutorial , that  look transformation cache need to be enabled to boost the performance of Lookup transformation( by avoiding lookup in the lookup source again and again)

Now if Look up source is getting changed , then we  need to also refresh lookup cache. Dynamic cache in lookup Transformation solves our purpose  :).

Dynamic cache:

If you want to cache the target table and insert new rows into cache and the target,you can create a look up transformation to use dynamic cache.The informatica server dynamically inserts data to the target table. 

 

1. You cannot share the cache between a dynamic Lookup transformation and static Lookup transformation in the same target load order group.
2. You can create a dynamic lookup cache from a relational table, flat file, or source qualifier transformation.
3. The Lookup transformation must be a connected transformation.
4. Use a persistent or a non-persistent cache.
5. If the dynamic cache is not persistent, the Integration Service always rebuilds the cache from the database, even if you do not enable Re-cache from Lookup Source.
6. When you synchronize dynamic cache files with a lookup source table, the Lookup transformation inserts rows into the lookup source table and the dynamic lookup cache. If the source row is an update row, the Lookup transformation updates the dynamic lookup cache only.
7. You can only create an equality lookup condition. You cannot look up a range of data in dynamic cache.

Informatica Powercenter Architecture

After so many Informatica tutorial  on so many topics ( transformation  , tuning of transformation), question arises , what exactly is happening behind the scene. So time has come to discuss about architecture of Informatica in details. We will discuss about the various component of Informatica architure , various services and how they are interlinked to each other. Hope you will enjoy this Informatica tutorial. By the way , it is one of the most  asked interview question in Informatica.

 

Component of Informatica Architecture

Domain: Domain is the primary unit for management and administration of services in Powercenter. The components of domain are one or more nodes, service manager an application services.

Node: Node is logical representation of machine in a domain. A domain can have multiple nodes. Master gateway node is the one that hosts the domain. You can configure nodes to run application services like integration service or repository service. All requests from other nodes go through the master gateway node.

Service Manager: Service manager is for supporting the domain and the application services. The Service Manager runs on each node in the domain. The Service Manager starts and runs the application services on a machine.

Application services: Group of services which represents the informatica server based functionality. Application services include powercenter repository service, integration service, Data integration service, Metadata manage service etc.

Powercenter Repository: The metadata is store in a relational database. The tables contain the instructions to extract, transform and load data.

Powercenter Repository service: Accepts requests from the client to create and modify the metadata in the repository. It also accepts requests from the integration service for metadata to run workflows.

Powercenter Integration Service: The integration service extracts data from the source, transforms the data as per the instructions coded in the workflow and loads the data into the targets.

Informatica Administrator: Web application used to administer the domain and powercenter security.

Metadata Manager Service: Runs the metadata manager web application. You can analyze the metadata from various metadata repositories.

I also found a good article on Informatica Architecture here