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 theRole Playing Dimension in dataware house with some example , why they are important.Hope you will enjoy this small data warehouse tutorial.
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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 theDegenerate Dimension in dataware house with some example , why they are important.Hope you will enjoy this small data warehouse tutorial. Degenerate Dimension in Data warehouseA 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. 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 warehouseA 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. 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 warehouseIn 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. 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. [caption id="" align="alignnone" width="458"] Data warehouse example[/caption] 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:
After so manyInformatica 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 ArchitectureDomain: 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 onInformatica Architecture here 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.
In below article , we will go through the some the Informatica Interview question based on the lookup transformation in Informatica. In last tutorial , we have gone through the detailed explanation of lookup Transformation. You can find a good details of Lookup transformation here. We also checked some of the example of connected and unconnected lookup transformation also. Lets go through some of the Informatica Interview question on Lookup transformation. Check here : Difference connected and unconnected Lookup Transformation here . Que: What is a pipeline lookup transformation?? Ans :A pipeline lookup transformation is used to perform lookup on application sources such as JMS, MSMQ or SAP. A pipeline lookup transformation has a source qualifier as the lookups source. Que: What are the different type of Sources for Lookup ? Ans :
Ques: What are the tasks of a lookup transformation? Ans : The lookup transformation is used to perform the following tasks?
Ques: . What is "Output Old Value on Update"? Ans : This option is used when dynamic cache is enabled. When this option is enabled, the integration service outputs old values out of the lookup/output ports. When the Integration Service updates a row in the cache, it outputs the value that existed in the lookup cache before it updated the row based on the input data. When the Integration Service inserts a new row in the cache, it outputs null values. When you disable this property, the Integration Service outputs the same values out of the lookup/output and input/output ports. Ques: . How do you configure a lookup transformation? Ans : Configure the lookup transformation to perform the following types of lookups:
SCD Type-2 In Informatica Slowly Changing Dimension Type-2 ,(also known as SCD -2) tracks historical changes by keeping multiple records for a given natural key in the dimensional tables .For example , we may need to track the current location of a supplier along with its previous location just to track his sales in different region . Example of SCD Type -2 For Example : If we want to keep track of DEPT of an employee , we can add two extra fields , Say Start-date , End_date to keep track of those records: EMP_SCD2
SQL Joins are to combine data from different tables to show relation between them . Example you can join EMP table with DEPT table to get department along with Employee records. See Also : Learn about Oracle Architecture here There are basically below type of Joins : 1. INNER Join :SELECT emp.empno, emp.ename ,emp.job, dept.deptno,dept.dname FROM emp INNER JOIN dept ON emp.deptno= dept.deptno;2. Left Outer Join :SELECT emp.empno, emp.ename ,emp.job, dept.deptno,dept.dname FROM emp LEFT OUTER JOIN dept ON emp.deptno= dept.deptno;3. Right Outer JoinSELECT emp.empno, emp.ename ,emp.job, dept.deptno,dept.dname FROM emp RIGHT OUTER JOIN dept ON emp.deptno= dept.deptno;4. Full Outer JoinSELECT emp.empno, emp.ename ,emp.job, dept.deptno,dept.dname FROM emp FULL OUTER JOIN dept ON emp.deptno= dept.deptno; I Found a good article on "Type of Oracle join with Example" here. See Also : Learn about Oracle Architecture here Hope you enjoyed this small and useful article on SQL joins in Oracle :) |