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Data WareHousing Interview Questions Part 9 Q.Explain performance tuning in Informatica? A.The goal of performance tuning is optimize session performance so sessions run during the available load window for the Informatica Server.Increase the session performance by following. 1.The performance of the Informatica Server is related to network connections. Data generally moves across a network at less than 1 MB per second, whereas a local disk moves data five to twenty times faster. Thus network connections ofteny affect on s ession performance.So aviod netwrok connections. 2.Flat files: If your flat files stored on a machine other than the informatca server, move those files to the machine that consists of informatica server. 3.Relational datasources: Minimize the connections to sources ,targets and informatica server to improve session performance.Moving target database into server system may improve session performance. 4.Staging areas: If u use staging areas u force informatica server to perform multiple datapasses.Removing of staging areas may improve session performance. 5.You can run the multiple informatica servers againist the same repository.Distibuting the session load to multiple informatica servers may improve session performance. 6.Run the informatica server in ASCII d atamovement mode improves the session performance.Because ASCII datamovement mode stores a character value in one byte.Unicode mode takes 2 bytes to store a character. 7.If a session joins multiple source tables in one Source Qualifier, optimizing the query may improve performance. Also, single table select statements with an ORDER BY or GROUP BY clause may benefit from optimization such as adding indexes. 8.We can improve the session performance by configuring the network packet size,which allows data to cross the network at one time.To do this go to server manger ,choose server configure database connections. 9.If we are target consists key constraints and indexes we slow the loading of data.To improve the session performance in this case drop constraints and indexes before we run the session and rebuild them after completion of session. 10.Running a parallel sessions by using concurrent batches will also reduce the time of loading the data.So concurent batches may also increase the session performance. 11.Partittionig the session improves the session performance by creating multiple connections to sources and targets and loads data in paralel pipe lines . 12.In some cases if a session contains a aggregator transformation ,You can use incremental aggregation to improve session performance. 13.Aviod transformation errors to improve the session performance. 14.If the sessioin containd lookup transformation You can improve the session performance by enabling the look up cache. 15.Aggreagator,Rank and joiner transformation may oftenly decrease the session performance .Because they must group data before processing it.To improve session performance in this case use sorted ports option. Q.What is the procedure to write the query to list the highest salary of three employees? The following is the query to find out the top three salaries in ORACLE:--(take emp table) select * from emp e where 3>(select count (*) from emp where e.sal>emp.sal) order by sal desc. in SQL Server:-(take emp table) select top 10 sal from emp Q.Can we run the mapping with out starting the informatica server? A.The difference between cache and uncacheed lookup iswhen you configure the lookup transformation cache lookup it stores all the lookup table data in the cache when the first input record enter into the lookup transformation, in cache lookup the select statement executes only once and compares the values of the input record with the values in the cachebut in uncache lookup the the s elect statement executes for each input record entering into th e lookup transformation and it has to connect to database each time entering the new record.
Q.What are the types of maping wizards that are to be provided in Informatica? A.The Designer provides two mapping wizards to help you create mappings quickly and easily. Both wizards are designed to create mappings for loading and maintaining star schemas, a series of dimensions related to a central fact table.Getting Started Wizard. Creates mappings to load static fact and dimension tables, as well as slow ly growing dimension tables.Slowly Changing Dimensions Wizard. Creates mappings to load slowly changing dimension tables based on the amount of historical dimension data you want to keep and the method you choose to handle historical dimension data. Q.What is the Rankindex in Ranktransformation? A.The Designer automatically creates a RANKINDEX port for each Rank transformation. The Informatica Server uses the Rank Index port to store the ranking position for each record in a group. For example, if you create a Rank transformation that ranks the top 5 salespersons for each quarter, the rank index numbers the salespeople from 1to 5. In a filter expression we want to compare one date field with a db2 system field CURRENT DATE.Our Syntax: datefield = CURRENT DATE (we didn't define it by ports, its a system field ), but this is not valid (PMParser: Missing Operator).The db2 date formate is "yyyymmdd" where as sysdate in oracle will give "dd-mm-yy" so conversion of db2 date formate to local database date formate is compulsary. other wise we will get that type of error Q.What are two modes of data movement in Informatica Server? A.The data movement mode depends on whether Informatica Server should process single byte or multi byte character data. This mode selection can affect the enforcement of code page relationships and code page validation in the Informatica Client and Server. 1.Unicode - IS allows 2 bytes for each character and uses additional byte for each non-ascii character (such as Japanese characters) 2.ASCII - IS holds all data in a single byte. The IS data movement mode can be changed in the Informatica Server configuration parameters. This comes into effect once you restart the Informatica Server. Identifying bottlenecks in various components of Informatica a nd resolving them.The best way to find out bottlenecks is writing to flat file and see where the bottle neck is. Q.What r the basic needs to join two sources in a so urce qualifier? A.The both the table should have a common feild with same datatype.Its not neccessary both should follow primary and foreign relationship. If any relation ship exists that will h elp u in performance point of view.Identifying bottlenecks in various components of Informatica and resolving them.The best way to find out bottlenecks is writing to flat file and see where the bottle neck is What is aggregate cache in aggregator transforamtion? The aggregator stores data in the aggregate cache until it completes aggregate calculations.When you run a session that uses an aggregator transformation,the informatica server creates index and d ata caches in memory to process the transformation.If the informatica server requires more space,it stores overflow values in cache files. Q.Can u tell me how to go for SCD's and its types.Where do we use them mostly A.The "Slowly Changing Dimension" problem is a common one particular to data warehousing. In a nutshell, this applies to cases where the attribute for a record varies over time. Q.What are Target Options on the Servers? A.Target Options for File Target type are FTP File, Loader and MQ.There are no target options for ERP target type. Target Options for Relational are Insert, Update (as Update), Update (as Insert), Update (else Insert), Delete, and Truncate Table. Q.What is the difference between summary filter and detail filter? A.Summary filter can be applieid on a group of rows that contain a common value.where as detail filters can be applied on each and every rec of the data base. 1.Summary Filter: we can apply records group by that contain common values. 2.Detail Filter: we can apply to each and every record in a d atabase. Q.What is the diff b/w STOP & ABORT in INFORMATICA sess level ? A. 1.Stop:We can Restart the session 2.Abort:WE cant restart the session.We should truncate all the pipeline after that start the session
Q.Difference between static cache and dynamic cache? A. 1.Static cache: You can not insert or update the cache. The informatic server returns a valu e from the lookup table or cache when the condition is true.When the condition is not true, informatica server returns the default value for connected transformations and null for unconnected transformations. 2.Dynamic cache: You can insert rows into the cache as you pass to the target. The informatic server inserts rows into cache when the condition is false.This indicates that the the row is not in the cache or target table. U can pass these rows to the target table Q.What is power center repository? A.Standalone repository. A repository that functions individually, unrelated and unconnected to other repositories. Global repository. (PowerCenter only.) The centralized repository in a domain, a group of connected repositories. Each domain can contain one global repository. The global repository can contain common objects to be shared throughout the domain through global shortcuts. Local repository. (PowerCenter only.) A repository within a domain that is not the global repository. Each local repository in the domain can connect to the global repository and use objects in its shared folders. Q.How to define Informatica server? A.Informatica server is the main server component in informatica product family..Which is resonsible for reads the data from various source system and tranforms the data according to business rule and loads the data into the target table Q.Explain the informatica Architecture in detail A.informatica server connects source data and target data using native odbc drivers.Again it connect to the repository for running sessions and retriveing metadata information. source------>informatica server--------->target
Top 50 Data Warehousing Interview Questions with Answers This article attempts to explain the fundamental rudimentary concepts of data warehousing in the form of questions and their respective answers. After reading this article, you should gain good enough knowledge on various concepts of data warehousing. A data warehouse is a electronical storage of an Organization's historical data for the purpose of analysis and reporting. According to Ki mpball, a datawarehouse should be subject-oriented, nonvolatile, integrated and time-variant.
What is the benefits of data warehouse? Historical data stored in data warehouse helps to analyze different aspects of business including, performance analysis, trend analysis, trend prediction etc. which ultimately increases efficiency of business processes.
Why Data Warehouse is used? Data warehouse facilitates reporting on different key business processes known as KPI. Dat a warehouse can be further used for data mining which helps trend prediction, forecasts, pattern recognition etc.
What is the difference between OLTP and OLAP?
OLTP is the transaction system that collects business data. Whereas OLAP is the r eporting and analysis system on that data. OLTP systems are optimized for INSERT, UPDATE operations and therefore highly normalized. On the other hand, OLAP systems are deliberately denormalized for fast data retrieval through SELECT operations.
Explanatory Note: In a departmental shop, when we pay the pri ces at the check-out counter, the sales person at the counter keys-in all the data into a "Point-Of-Sales" machine. That data is transaction data a nd the related system is a OLTP system. On the oth er hand, the manager of the store might want to view a report on out-of-stock materials, so that he c an place purchase order for them. Such report will come out from OLAP system
What is data mart? Data marts are generally designed for a single subject area. An organization may have data pertaining to different departments like Finance, HR, Marketting etc. stored i n data warehouse and each department may have separate data marts. These data marts c an be built on top of the data warehouse.
What is ER model? ER model is entity-relationship model which is designed with a goal of normalizing the data.
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