Saturday, May 30, 2015
A data warehouse is a warehouse that contains data. Sounds funny doesn't it? Imagine a warehouse as a storage structure that may be physical or logical. In the DW it's both...logical as in the data model representation and physical as to the hard disks and other computer equipment that support the data warehouse.
The first question should be "How does the data get there?" Here are three key areas to keep in mind....
Availability - will someone create a report with transactions completed 20 minutes ago or will it be with yesterday's data?
Wednesday, May 27, 2015
IIS Datastage connectivity options give us a wide scope to connect with different source or targets. It's support RDBMS, ERP, z/OS DB, OLAP system and many more.
Below listed Data Sources are available in IIS v11.3
Monday, May 25, 2015
You can access Part1 Here - Execution Steps in Transformer Stage
Certain constructs are inefficient if they are included in output column derivations, because they are evaluated once for every output column that uses them. The following examples describe these constructs:
- The same part of an expression is used in multiple column derivations.
- For example, if you want to use
the same substring of an input column in multiple columns in output
links, you might use the following test in a number of output columns
In this case, the evaluation of the substring of DSLINK1.col1[1,3] is repeated for each column that uses it. The evaluation can be made more efficient by moving the substring calculation into a stage variable. The substring is then evaluated once for every input row. This example has thus stage variable definition for StageVar1:
IF (DSLINK1.col1[1,3] = "001") THEN ...
Thursday, May 21, 2015
I've been asked this questions so many times in interviews and by different practitioner also that What are the data processing steps when datastage is processing transformer, So here I tried to compiled. Have a look -
To write efficient Transformer stage derivations, it helps to understand what items get evaluated and when.
Wednesday, May 20, 2015
Partitioner insertion and sort insertion each make writing a flow easier by alleviating the need for a user to think about either partitioning or sorting data. By examining the requirements of operators in the flow, the parallel engine can insert partitioners, collectors and sorts as necessary within a data flow.
However, there are some situations where these features can be a avoided or not needed.
If data is pre-partitioned and pre-sorted, and the InfoSphere DataStage job is unaware of this, you could disable automatic partitioning and sorting for the whole job by setting the following environment variables while the job runs:
Tuesday, May 19, 2015
1. Delete from customers.
2. Delete depositor of branches having number of customers between 1 and 3.
3. Delete branches havinng average deposit less than 5000.
4. Delete branches having maximum loan more than 5000.
5. Delete branches having deposit from Nagpur.