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Wednesday, 31 July 2013

Hyperion financial management Interview question



HFM1:
1.how to set the dimention in hfm?

2.how many types of account?

3.wat is mean value dimention?

4.where the translation ll be done?-&

5.wat is mean switchsignflow and switchsigntype?

6.wat is mean ICP?

7.how to set icp? if set Y means wat? N means wat? R means wat?

8.wat is mean by plugaccount?

9.how many types of rule?

10.descripe elimination rule?-&

11.wat mean by review process in hfm?-&

12.how to enable process management in hfm?-&

FDQM:

1.wat is mean by validation rule and how to set?-&

2.if I want to 10 location means how to mapping?-&

3.how to import in fdqm?

Smartview:

1.How to connect the source with smartview?

2.do u know infrastructure in HFM?

3.HFM architechture?


HFM2:
1.account type:balace and balancerecurring?

2.if we change currency in entity means wat we ll do in appl?-&

3.how to set ICP?

4.define value and period dimension?

5.how to enable data audit?-&

6.explain organisation by period?

7.when currency transaction ll happpen?

8.if entity x-currency euro and entity y-currency USD means to see in dataform which value dimention set in pov?-&

9.wat is mean by contribution value?-&

10.wat value dimention set for journal entry?

11.how to get icp matching report?-&

12.how set the process management?

13.wat new thing u doing in dataform? link,Scalc function for calculation

14.write the rule to enter value for particular account?-&

 
HFM3:
1.If we create user for journals, then user can post journal, suppose user can do unpost for journal?-&

2.how can we see the ICP without using dataform and grid?-&

3.user can give matching tolarence?-&

4.organisation by period means wat?
for example in old entity stucture i gave YTD... next year i add one new entity.. if i am consolidate jan means wat ll see the result?-&

5.they give scenario for rules?

6.if client give excel means.. how you load input into HFM?_&

7. wat issue you solved in ur project?-&

8.what is the HFM services u go to start?

9.types of mapping?

Command to find lock in oracle table



SELECT statment SELECT SID,SERIAL# FROM V$SESSION WHERE SID IN (SELECT SESSION_ID FROM DBA_DML_LOCKS WHERE NAME = Table Name)

Friday, 26 July 2013

To Drop Tablespace in oracle



SQL> drop tablespace kk including contents;
Tablespace dropped.

SQL>
SQL> select username from dba_users:
It will display all the username in the DB

SQL> select name from v$database;
NAME
ORCL
It will display the DB name


Sql> select count(*) from user_objects;
It will display all the objects in that schema.

Thursday, 25 July 2013

Full Database Export in oracle



C:\>exp system/<system pwd> file=<filename.dmp log=logfile.log full=y

C :\> exp system/system file=fulldump.dmp log=fulldump.log full=y

Export command in oracle



C:\> exp <username>/<password> file=<filename.dmp> log=<logname.log>

C:\>exp kk/kk file=kk.dmp log=kk.log

Export: Release 9.2.0.1.0 - Production on Thu Oct 20 20:50:23 2005

Copyright (c) 1982, 2002, Oracle Corporation. All rights reserved.
Connected to: Oracle9i Enterprise Edition Release 9.2.0.1.0 - Production
With the Partitioning, OLAP and Oracle Data Mining options
JServer Release 9.2.0.1.0 - Production
Export done in WE8MSWIN1252 character set and AL16UTF16 NCHAR character set
. exporting pre-schema procedural objects and actions
. exporting foreign function library names for user KK
. exporting PUBLIC type synonyms
. exporting private type synonyms
. exporting object type definitions for user KK
About to export KK's objects ...
. exporting database links
. exporting sequence numbers
. exporting cluster definitions
. about to export KK's tables via Conventional Path ...
. exporting synonyms
. exporting views
. exporting stored procedures
. exporting operators
. exporting referential integrity constraints
. exporting triggers
. exporting indextypes
. exporting bitmap, functional and extensible indexes
. exporting posttables actions
. exporting materialized views
. exporting snapshot logs
. exporting job queues
. exporting refresh groups and children
. exporting dimensions
. exporting post-schema procedural objects and actions
. exporting statistics
Export terminated successfully without warnings.

Import command in oracle




C:\>imp <username>/<password> file=<dumpname.dmp> log=<logfilename.log> full=y

C:\>imp kk/kk file=kk.dmp log=kkimp.log full=y

• C:\>imp <username>/<password> file=<dumpname.dmp> log=<logfilename.log> fromuser=<fromusername> touser=<tousername>

• Before import we need to check the tablespace free space.

SYSTEM -200M

INDX -100M

DEFAULT TABLESPACE (ORION) -

C:\>imp kk/kk file=kk.dmp log=kkimp.log fromuser=surya touser=kk
Imp……
Imp…….

Import completed successfully with warnings.

After Import Connect to the Respective DB user

C:\>sqlplus

Enter username: kk/kk

SQL>@c:\compile – it will compile all the Invalid objects run the compile script atlease 4 times.

To create Database user in Oracle



To create DB user

SQL> create user <username> identified by <password> default tablespace <Tablespace Name>
Temporary tablespace <Temporary tablespace name>;

SQL> create user kk identified by kk default tablespace kk temporary tablespace
temp;

User created.

SQL>

SQL> show user;
USER is "SYS"

SQL>

Sql> grant create session,imp_full_database to <user name>

SQL> grant create session,imp_full_database to kk;

Grant succeeded.

SQL> alter user kk quota unlimited on <username>;

SQL> alter user kk quota unlimited on kk;
User altered.

SQL>exit

Increase the Tablespace Size in oracle




Increasing the Existing Datafile

SQL> ALTER DATABASE DATAFILE 'E:\ORION_DBF\KK1.DBF' RESIZE 100M;

Database altered.

SQL>

• Appending additional DATA file to the Tablespace

SQL> alter tablespace kk add datafile 'e:\orion_dbf\kk2.dbf' size 20m;

Tablespace altered.

To Create Table Space in Oracle




To Create Table Space:

Create a folder in local drive like ORION_DBF

Goto sqlplus enter userid/pwd
SQL>


SQL> CREATE TABLESPACE KK DATAFILE 'E:\ORION_DBF\KK1.DBF' SIZE 50M;

Tablespace created.





Friday, 19 July 2013

Types of Fact Tables in Datawarehouse



There are 3 types of fact tables in data warehouse

1. Transactional 

2. Periodic Snapshot

3. Accumulating  snapshot


Transactional Fact Table: 

1. Transactional fact table will be in detailed level.

2. It will have each line of transaction. Example: each line of sale and invoice.

3. Transactional fact table has so detailed level data, so it will be have all the dimensional assisted with it



Periodic Snapshot Fact table:

1. Periodic snapshot fact table will have information by time to time.

2. Consider you want  the information of growth of sales  information of an organisation for a particular region last month compare to this month. Then you will need  periodic snapshot fact table.

3. Periodic snapshot fact table will depend upon transactional fact table as it need a detail held in transactional fact table related to region sales of an organisation.


Accumulating  snapshot Fact table:

1. Accumaulating snapshot fact table is used to show the information of the business which has cleary defined from begin to the end.

2. If you see a purschase order of an organisation, The purschase will move in specific order unstill it is fully processed.

3. Accumlating snapshot fact table will have multiple date column, each date column represent the level of purshase status of an organisation.


How you will define the fact table?

1. Identify the business process of an orginaization

2. Define the grain

3. choose the dimensional for your fact table


Thursday, 18 July 2013

OBIEE 11G ARCHITECTURE WITH EXPLANATION






Below diagram describes the standard logical architecture of Oracle business intelligence 11g system



The entire system architecture is called BI Domain, this BI Domain divided into Java components and non-Java components. Java components are weblogic server Domain components and non-java components are Oracle BI system components.






Weblogic Server Domain

This domain consists of Managed server and Admin Server. These services comprises mainly with all the java modules to trigger the java services.


Admin Server: A JEE container that runs in a dedicated Java virtual machine that contains Java components for administering the system .It typically trigger the start, stop kind of admin activity for his peer Manager server processes.

Managed Server:A JEE container that runs in a dedicated Java virtual machine that provides the run-time environment for the Java-based services and applications within the system. The services comprises of BI plug-in, Security, publisher, SOA, BI Office services etc



Node Manager: Node Manager is a separate java utility runs to trigger the auto start, stop, restart activities and it provides process management services for the Admin server and Managed Server.



Oracle Process Manager and Notification Server (OPMN):By using this OPMN services we can stop and start all system components of BI. It is monitored, managed and controlled by Fusion Middleware Controller.


Oracle Weblogic Server (Console):

It is a Java EE application server that supports the deployment of Oracle Business Intelligence Java components. Oracle WebLogic Server Administration Console access has been provided by Fusion Middleware Control. Oracle WebLogic Server Administration Console enables to monitor and manage a WebLogic Server domain. Its capabilities include the following:

· Monitoring health and performance of JEE servers· Configuring WebLogic domains· Stopping and starting JEE servers· Viewing JEE server logs



Fusion Middleware Control:


Fusion Middleware Control is a browser-based tool and the recommended method for monitoring, managing, and configuring Oracle Business Intelligence components.



Starting, stopping, and restarting all system components (BI Server, BI Presentation Server) and Managed Servers

· Configuring preferences and defaults


· Starting, stopping, and restarting all system components (BI Server,BI Presentation Server) and Managed Servers


· Managing performance and monitoring system metrics(DMS-Dynamic Monitoring System)


· Performing diagnostics and logging (ODL-Oracle Diagnostic Logging)Fusion Middleware Control also provides access to Oracle WebLogic Server Administration Console, where you monitor and manage Oracle Business Intelligence Java components.


Java components:
Deployed as one or more Java EE applications:



Administrative Components — Enterprise Management applications and JMX MBeans for managing all configuration and run-time settings for Oracle Business Intelligence.



Oracle BI Publisher — This component provides an enterprise reporting solution for authoring, managing, and delivering all types of highly formatted documents to employees, customers, and suppliers.


Oracle BI Office — This component provides the integration between Oracle Business Intelligence and Microsoft Office products.



Oracle BI Action Services — This component provides the dedicated Web services that are required by the Action Framework and that enable an administrator to manually configure which Web service directories can be browsed by users when they create actions.



Oracle Real-Time Decisions (Oracle RTD) — This component provides enterprise analytics software solutions that enable companies to make better decisions in real-time at key, high-value points in operational business processes.



Oracle BI Security Services — This component provides dedicated Web services that enable the integration of the Oracle BI Server with the Oracle Fusion Middleware security platform i.e JPS (Java Platform Security) , CSF (Credential Store Framework) and users and groups managed by BI LDAP security.


Oracle BI SOA Services — This component provides dedicated Web services for objects in the Oracle BI Presentation Catalog, to invoke analyses, agents, and conditions. They make it easy to invoke Oracle Business Intelligence functionality from Business Process Execution Language (BPEL) processes.


Oracle BI Plug-in — A JEE application that routes HTTP and SOAP requests to Oracle BI Presentation Services.


System components:
Deployed as non-JEE components, such as processes and services written in C++ and J2SE:


Oracle BI Server — This component provides the query and data access capabilities at the heart of Oracle Business Intelligence and provides services for accessing and managing the enterprise semantic model (stored in a file with a .RPD extension).



Oracle BI Presentation Services — This component provides the framework and interface for the presentation of business intelligence data to Web clients. It maintains an Oracle BI Presentation Catalog service on the file system for the customization of this presentation framework.


Oracle BI Scheduler — This component provides extensible scheduling for analyses to be delivered to users at specified times. (Oracle BI Publisher has its own scheduler)



Oracle BI JavaHost — This component provides component services that enable Oracle BI Presentation Services to support various components such as Java tasks for Oracle BI Scheduler, Oracle BI Publisher, and graph generation.



Oracle BI Cluster Controller — This components distributes requests to the BI Server, ensuring requests are evenly load-balanced across all BI Server process instances in the BI domain.

Tuesday, 16 July 2013

Protect your data warehouse


How to protect your datawarehouse 

While many used data warehouses to gain access to information and analysis, and some companies want to put restrictions on the type of information that can be accessed factor. While there are some advantages in doing so, there are also some disadvantages as well.

Overall, the company does not put emphasis on security even after it has been building a data warehouse. Before setting up a security system to store your data, it is first important to understand what is the function of the data warehouse is being designed for your.

If most of the people who use the data warehouse will not be considered in the background reports, you will need to establish a security system that can accommodate that. While you want to take some security measures, it is important to make sure you do not add too much. When you set up a security system for your data warehouse, there are three areas you want to pay attention to, and this is analytical, consolidation, and consolidated reports.

The analytical side of data warehouses is something you will hear about more than others. It is especially important during the planning stages. Despite this, more than 70 percent of data warehouses are built with standardized reporting in mind. There will be only a handful of people in most of the companies that will know how to make plans based on the information obtained in the analytical repository. While these people have a tremendous amount of skill and knowledge, and they make up only a small portion of those who work with data stores.

Trying to use advanced security measures in the analytical data warehouse generally worthless. However, CRF is a different issue. With standardized reporting, and a security system is not an option. The reason for this is because of this area of the warehouse that will have the most activity, and this is the most vulnerable to performance problems. Data warehouse, which puts the emphasis on the consolidation seeks to integrate the information that it contains. Despite this, some companies may choose to merge the data into a single source. When multiple sources are brought together information, security will become a complex issue.

As you can imagine, the financial information within the data warehouse you need to have a different level of security of information relating to stock. In addition, it has various departments within the company to have their own levels of security. In order for you to secure your data warehouse, it is important to make sure that every form of information has its own security system. If you are considering adding security to your data warehouse, you will need to decide where the security system should be added. Places that are common choose is the level of the database and the application.

Put a security system in the effective application because it can not be connected to the data that is processed by the application. In addition, the actual functions of the program can also be secured. After application, the next best place to add security is the data warehouse itself. When you add security to the data warehouse, and all computer programs are safe. And one will choose to rely on a number of factors. If you use more than one program within the database, it may be better to use a security program at the database level. You may also need to use a security system at the database level if more than 100 users will have access to the data warehouse.

Another thing that you want to become familiar with is the security table. The table will contain security features that are secured along with the identification of the user. The table will be held values that are related to the information that the user is allowed to access.

You may find that security table could become the largest table inside the warehouse. However, the security table can play an important role in making sure your information is secure. When you secure your data warehouse, it is important to make sure the levels are set right to security. Each type of information within the database will need to be secured in a different way. It can also be slowed down the speed of your data warehouse.

How to build DataWarehouse


And the structure of the data warehouse consists of elements and components that make up the database. Structure will show how the components work together, and can also show how the database will grow over a certain period of time.

While each has a data warehouse structure, and only a few of them a high degree of organization. It is important to note that the data warehouse with the structure of the organization is the most likely to succeed. A data warehouse without the presence of the structure of the organization not be as flexible as it should be. In the absence of good faith, there would be no contacts, they will become difficult to maintain database.
If the company aspires to compete successfully, it is necessary to design the structure of a data warehouse that is highly organized and efficient-A "blue print" to make sure they are properly designed. Must be the definition of all the components, and show how the system will grow as they are used. Thus, it is important to build a data warehouse with the right structure.

The first thing is important to realize that the structure of your data warehouse should be connected directly to your business. For example, if you need to design a data warehouse, which will be updated every night, you will need this to be built in the structure. To build this function in the temple, you will need to have a technical understanding of the system. Some of the things you may want to build a structure within your data warehouse is the availability of global, customer analysis, and data sources, reliability, and daily updates. It is also important to look at the components that will form the structure.

Two core components that will form the structure of your data warehouse is the art elements and data elements. You will also need to consider the business processes, devices, networks, and operating systems. When it comes to data, you may need to create a structure that provides information related to billing or shipping. Should the data within the data warehouse has the same structure. It should also be kept in the same way. A common question that is raised between the organizations is the decision of whether the data appears in dimensional or entity / relationship.

The one you choose depends on how you run your organization. Once you have made your decision, you will want to consider the next infrastructure architecture. The most important factors that are related to infrastructure architecture is the flexibility, size, and scalability. If you do your research, you should not have a hard time building this. When it comes to the network, you will need to pay attention to data sources. There must be a sufficient amount of available bandwidth to transmit information. Must be on desktop computers, which used to be strong enough to run the necessary programs. In addition, should the software you use will be easy to transfer between machines.

It should also put emphasis on the technical architecture. The process must be used which is based off the metadata catalog. It should be a data warehouse is able to pull data from many sources, and may require this data to be encrypted or compressed. You will also need to make sure the data transfer correctly, it should be cleaned, integrated, and audited. It is also important to make sure that you download to many of the goals.

Strategies for designing a data warehouse



To build an effective data warehouse, it is important for you to understand the design principles of the data warehouse. If you do not build your data warehouse correctly, you can run into a number of different problems.

Based on the correct ways to build a strong data warehouse information technology tactics. First, it is important that you and your organization understand the importance of having a data warehouse. If workers feel that the data warehouse is necessary, they may not be used, and this can cause conflicts. Everyone in your organization must understand the importance of using the system.

After you've got your colleagues behind the concept of the use of the data warehouse, you will need to focus next on the integrity of the data. You will need to avoid designing a data warehouse that will load the data is inconsistent. It is also important to avoid creating a database that will replicate data. Should be the goal of your organization be to integrate the data and the development of standards that will be used and followed. After the integrity of the data, and the next you will need to consider the efficiency of implementation. This basically means that you want to design a system that is simple to use. It does not matter how good the design of the data warehouse if your employees have a hard time using it.

If workers have difficulty using the data warehouse, and will slow down the speed and productivity of your process. When it comes to creating a data warehouse, you will need to make it as simple as possible. It should be for all of your workers to be able to use it without problems. The efficiency of the implementation of the principle, which naturally leads to the next topic will need to focus on, and this is the ease of use. This is a concept that is an important part of your business. The reason for this is because end-users do not take advantage of the program, which is very difficult to use. It is important for you to keep this in mind. The use of the design that are friendly and easy to learn.

Once you have a data warehouse design that is easy to use, and the next you will need to consider the operational efficiency. Once the data warehouse has been created, and should be able to carry out quickly. In addition to this, it should not be errors or other technical problems. When errors or technical problems do not occur, it should be simple to correct. Another thing you want to look at the cost involved with a support system. Do you want to keep these costs as low as possible.

Design principles that have been discussed in this article so far are more relevant to the business of information technology. However, there are a number of information technology design principles that you will need to follow. One of these is scalability. This is a problem that many of the data warehouse designers up to. The best way to deal with this problem is to create a data warehouse that is scalable from the beginning. Design it in a way which will allow them to support expansions or upgrades. You should be able to adapt to a number of different work situations. Better data warehouses are those that are scalable.
Data repository that you design should fit within the guidelines for Information Technology Standards. Every tool that you use to build your data warehouse work well with the standards. You will need to make sure they are designed in a way that makes it easy for your staff to use. While following the guidelines in this article does not allow you to always be successful, and will fluctuate a lot of odds in your favor. We must be wary of companies that promise perfect results if you use the means of design. Regardless of how the data warehouse design your own, and you will always run into problems. However, in accordance with the principles of the right to make problems easier to recognize and resolve.

When it comes to using the data warehouse, it is not a question of "if" will run into problems. It is the question of "how" and "when." When designing your data warehouse well, and will be better equipped to solve any problems you encounter.

How to assess your data warehouse
While many large companies now use data warehouses, and the concept has not yet become fully mature. Not developed the principles and methods used in the management of data warehouses.
One reason for this is the difficulty that often involved with data stores. There are a number of techniques that should be used in order to identify and extract the data, and continued the tools needed to change on a consistent basis. Because of this, it often requires a great deal of technical skill in order to manage data warehouses. Caused many of these complications are a number of data warehouse software to fail.
In spite of these problems, and there is a huge demand for information management systems. Many companies use data warehouses because they face strong competition, and must be able to record, monitor and analyze information in order to make strategic decisions. However, it will be difficult for companies to meet these challenges if you are not able to correctly using their own data warehouses. The first step in correctly using your data warehouse is to develop a strong business processes and methods. It is not simply enough to get to the data warehouse. Any company that has sufficient resources can not do this.
Your company's success lies in its ability to produce strong processes that can be used to achieve the best results. Data warehouses are tools, and how you can use will play a strong role in whether you succeed or fail. No matter what process you develop for your data warehouse, and there are a number of things that you will want to keep in mind. First, you will need to avoid making the same mistakes again. Secondly, you will need to review and find the warehouse operations that were successful and use it to your advantage. It is these issues that companies want to pay attention to.

This is where the assessment of your data warehouse and this is very important. You will be able to find the mistakes that can be avoided in the future, and you will also be able to find successful methods that can be used again. Terms that you will need to deal with when assessing your data warehouse is the "how", "why" and "what." The objective of matter in these conditions is to find the best processes and methods that allow you and your company to prosper. But before you can start to assess your data warehouse, you need to know when it should be evaluated. Time is money, and you do not want to waste time assessing repository if it is not necessary.

If you are about to use your data warehouse for the first time, and this is an example of a time when you want to assess it. The information you gain from evaluation allows you to make better decisions about how they should be used in the data warehouse. You should know the needs of your business, you should also know how your data warehouse can help you take care of these needs. You should also determine whether your organization is ready to use their data warehouse after its construction. However, it is not enough to evaluate the data warehouse once. As the company continues to grow, it will change your requirements, you will need a data warehouse to re-evaluate. The best time to evaluate your data warehouse when you're not sure which direction the company should go in it.

Last time to assess your data warehouse when your company is running into problems. It should also be divided because if you notice they are lagging behind in some areas. As technology continues to advance, you will need to evaluate the data warehouse on a regular basis to find out what areas need to be upgraded.
In fact, you or your company decides that the data warehouse must become the central point in your process, and you've decided to put a focus on knowledge management. Assess your warehouse is not something that can be done only once. This should be done whenever it is necessary. When you are able to properly assess your data warehouse, you will be able to make good decisions that can allow your company to achieve success.

Methods of DataWarehouse



Most organizations agree that data warehouses are a useful tool. They benefit from the ability to store and analyze the data, and this can allow them to make sound business decisions. It is also important for them to ensure proper dissemination of information, and it should be easy to access by people who are responsible for making decisions.

There are two elements that make up a data warehouse environment, and this is a supply and staging. It can also be known as a staging area acquisitions. It consists of ETL processes, and once the data has been prepared, it will be sent to the display area.

When data is placed within the display area, a number of programs analysis and consideration. While many organizations agree on the overall goal of data warehouses, may approach to build these institutions vary. Try to use data clusters alone is not a good approach, because they are oriented departments. In addition, we will try to use data clusters alone are ineffective, and it will reach to a number of problems in the long term. There are two techniques for building data warehouses that have become very popular. This is the Kimball Bus Architecture and corporate information factory.

With technology Kimball, will convert the raw data and refined within the staging area. It is important to make sure properly handled data during this step. During the staging process, and will be withdrawn raw data from the source systems. While some of the staging operations may be centralized, and will be distributed to others. The display area has a dimensional structure, and this model will have the same information as a standard model. However, it will be easier to use, and it will display information that is summarized.

Will create a three-dimensional model through business process. Departments within the organization does not play a role in this. Data will be populated once it is placed inside a warehouse dimensions, not depending on the various departments that may make up the organization. When placed inside the warehouse business processes, the system will become highly efficient. Data warehouse approach popular following that you will want to become familiar with is the corporate information factory. Another name for this technique is the approach EDW. Will be the format of the data that is extracted from the source.

In the CIF, the data warehouse is used to hold the record data warehouses, and it may also have specific data warehouses that are designed to extract the data. May be designed for the populations of specific data circuits, and it may be the summary data which is in the form of dimensional structure. The data can be obtained from the data warehouse atomic standard. While there are some similarities between these techniques, there are some notable differences as well.

One of the fundamental differences between these two technologies is the basis of data normalization. With the approach of Kimball, the data structures that must be obtained before displaying depends on the dimensions of the source data and transformation. In most cases, it was not required to store a duplicate of the data in each of the foundations and normalized dimensions. It is believed many of the people who choose to use a normalized data structure that is faster than the dimensional structure, but they often fail to take the ETL into account.

Another thing that separates the two approaches is the management of data warehouse data (IAEA). With CIF, the data will be stored within the data warehouse Atomic normalization. In contrast, the method Kimball States that the atomic data should be placed in the context of the structure dimensions. When data is placed within the structure dimensions, it can be summed up in a wide variety of different ways.
It is important to make sure of the details of the information that you have so that users will be able to ask relevant questions. While most users do not put the focus on the details of the transaction and one offspring, they may want a summary of a large number of transactions. It is important for them to have the details so that they will not be able to answer important questions. Should be the approach you choose to be one which achieves your company's needs.

Requirements For Data Warehouses



There are some requirements that companies need to meet if they wish to use data warehouses effectively. When first introduced data warehouses in the 1990s, and developed many companies to focus on identifying the data warehouse in the system, which was distinct from the standard operating system.
This view was shared by many of the companies, and also seen in the data warehouse from being a centralized data that are operational. However, over the past decade, many of the companies were to change their views on how they perceive data warehouses. In the 1990s was a decade of trial and error. While there are many successes, there have been many failures.

One of the things that will improve the data warehouse industry is to increase the processing power of the computer. The technology also has advanced to the point where OLAP engines can focus on pulling data rather than placed in a data warehouse. It should also be noted that the field of dimensional modeling has greatly improved over the past decade. To achieve success in the current market, companies need to understand the requirements that must meet if they want their data warehouses to be successful. The companies first thing you want to do is to move from a centralized development strategy to one that is decentralized. In addition, it should also be growing development.

The only thing that companies must be aware that it is inevitable that smaller departments will create their own small repositories. Because he can not stop this practice, it is important for companies to create a framework that allows these departments to share information with the rest of the company. Remember, the purpose of the data warehouse is to give a view of the company as a whole. In spite of all the departments will need their own small repositories to answer crucial questions, you must provide this information to the rest of the company. In spite of this, you should be able to design data-Mart department their own unique way.

The second requirement is that companies will want to meet is the ability to deal with emergencies when they occur variables. The only thing that remains constant is change, and the company must prepare for this. It should be based on a data warehouse in a way that allows them to evolve. It will be frustrating and boring to have to change plans every time the company needs to adapt to the new change. The company must be able to add additional information to their own data warehouse without having to modify any of its components. Once this is done, the company can add new information to their system without having to make changes to boring, and they can focus on more important issues.

The third condition is that companies will want to meet is the rapid implementation. This follows closely to build a system that is decentralized rather than centralized. In the past, companies took months and sometimes years to build a data warehouse, which was central. This is a significant increase in the costs involved with the construction of the system, and the company wasted a great deal of time. Using rapid deployment, the data warehouse can be built in pieces, and it can be done much faster with a high level of efficiency. To do so quickly, it should be to all parts of the data warehouse using the same structure.
Once this is done, it will be much easier for the company to build parts and indexed. And inquire about parts also become much easier. The fourth condition that companies will need to have is the ability to easily dig into simpler form of atomic data.

The vast majority of the company's data clusters need to use atomic data, it is important for departments to access this information without having to give their employees a great deal of training. Another requirement that the company must have is the data clusters that when combined can create the entire data warehouse. It must be shaped pools data from the underlying data (IAEA), because it is not effective to replicate data in all measurements throughout the company.

It is also important for companies to make sure they are repositories of data available 24 hours a day. In the past, the data warehouse will be down for certain periods of time, and this has led to a decrease in efficiency. The existence of data warehouses online 24 hours a day allowing the company to be highly efficient.

Issues in Data warehousing



There are some issues surrounding data warehouses that companies need to be prepared for. The failure to prepare for these issues is one of the main reasons why many of the data warehouse project is unsuccessful. One of the first issues companies need to face is that they are going to spend a considerable amount of time loading and cleaning the data.

Some experts said that the typical data warehouse project will require companies to spend 80% of their time doing so. While the percentage may or may not be as high as 80%, the only thing you must realize is that most sellers reduce the amount of time you'll have to spend to do so. While cleaning can be complicated data, and extract it can be more difficult.

No matter how well a company is preparing to manage the project, and we must face the fact that the scope of the project is likely to be longer then they estimate. While most of the projects will start with the specified requirements, and will conclude with the data. Once end users see what can be done with the data warehouse once finished, it is very likely that they will put a high demand for it. While there is nothing wrong with this, it is best to find out what the users of the data warehouse next need instead of what they want at the moment. Another issue that companies will have to face is having problems with their systems put the information in the data warehouse.

When a company enters this stage for the first time, you will find that the problems that had been hidden for years suddenly appears. Once this happens, the business managers have to make the decision as to whether or not the problem can be fixed through a transaction processing system or a data warehouse that is read only. It should also be noted that the company will be often responsible for storing data that have not been collected by the existing systems they have. This can be a headache for developers who encounter a problem, and the only way to solve it is by storing the data in the system. Many companies also find that some of their data is not validated by transaction processing programs.

In such a situation, you will need the data to be validated. When data is placed in the warehouse, and there will be a number of contradictions that will occur within the fields. Many of these areas have information that descriptive. When of the most common issues is when the controls are not put under the names of clients. This will cause headaches for the user repository would want in the data warehouse for ad hoc query to select the name of a particular client. Developer of the data warehouse may find itself forced to change transaction processing systems. In addition, there may be a need for them also bought some forms of technology.

One of the most important problems a company can face is a transaction processing system that feeds information into the data warehouse with few details. This may occur often in the data warehouse that is tailored towards the products or customers. Some developers may refer to this as a matter of granular. Regardless, it is a problem that will want to avoid at all costs. It is important to make sure that the information that is placed in the data warehouse is rich in details.

Many companies also make the mistake of not enough high-budget resources that are related to the structure of the feeding system. To deal with this, the companies want to build part of the logic to clean the platform for the feeding system.

This is important especially if it happens to be a platform mainframe. During the cleaning process, you will be expected to do a lot of sorting. The good news about this is that central facilities often be proficient in this area. Some chosoe users to build within the aggregates since the central assembly will also require a lot of sorting. It should also be noted that many of the end user will not use the training they receive for the use of the data warehouse. However, it is important to teach the basics of using it, especially if the company wants them to use the data warehouse frequently.

Rules of Datawarehouse


Once the company has successfully carried out their own data warehouse, it is important for them to establish rules and regulations to be used. While different companies will have different rules when it comes to dealing with their own data warehouses, they are some general principles that you will need to pay attention to.

These principles not only make using your own data warehouse easier, but also allow the organization to be used much more efficiently. Very first rule of thumb is to realize that data warehouses are a challenge to use. And many experts say that at least 30% of the information you give may not be consistent.

One of the most problematic things about this is that the company may not notice the error if they are dealing with an operational unit to be based on the transaction. In spite of this, we should not allow this error rate in the data warehouse. When you consider the fact that many of the stores data on a large-scale can cost millions of dollars to purchase and implement, and 30% error rate is unacceptable. To resolve this issue, it is important for companies to analyze their data carefully before making decisions that are based on it. It is unwise to simply accept the data as it is, without looking carefully for errors or other problems.

The second rule of data warehouses is to understand the data that is stored. It has been said that knowledge is power, but this is only half the truth. Knowledge that is stored and unused is power potential. The companies want to make every day analysis of the databases that are connected to the data warehouse. To understand the data, you should be able to find the relationships between systems analysts are numerous. Once found on these relationships, and must be preserved when transferring data within the data warehouse. The implementation of a data warehouse requires often the user to make some modifications to the schema of the database.

If the user does not understand the relationships between the different systems, they may be prone to generate errors that can adversely affect the accuracy and efficiency of the system. Another important rule of thumb is to learn how to find the entities that are equal to or equal to each other. One of the most common problems that can occur in the data warehouse is when they show the same piece of data in different parts of the machine with different names. For example, two of the departments within the organization may be helping one client, but the name of the Ministry shall not be placed in the system twice under different names.
One name can be provided, and can be another name abbreviation. This can create serious problems in the system if it is not corrected, the best way to solve this problem is to use a data conversion tool. Because many consist of large companies and institutions from many different departments, and can be serious problems arise when each of them decides to store the information in a different way. One of the cases in which this occurs often through mergers. To avoid this problem, companies want to create a standard database structure. This will make it much easier integration when they occur.

Perhaps one of the most important principles of data storage is the use of meta data in a manner supportive of the quality of data within the data warehouse. The metadata can be defined as "data about data." It is data that describes the data within the data warehouse.

One of the biggest challenges facing companies will try to reconcile the metadata across multi-vendor tools. To deal with this problem, the companies want to make sure they generate and use metadata interfaces or other products. Look for vendors who are able to integrate metadata from a variety of sources that are mixed.

It is also important for companies to make sure they choose the right products and data conversion. Data Conversion product is a device that extracts, cleans, and load the data into the data warehouse. It will also record the date of this process. Product data transfer is very important, and companies must carefully choose the product.

Data warehouse tools



There are a number of important tools that are related to data warehouses, and one of these is the data collection. Data warehouse can be designed to store information on the basis of a certain level of detail.
For example, you can store data on a per-transaction basis, or you can store it on the basis of a summary. These are examples of data collection. When the data is summarized, and queries are moving at a much faster rate. However, some information may be lost during the query, and this could be important information in order to solve a specific problem.

Before you decide which one you will use, it is important to weigh your options carefully. Once you have carried out an operation, you will need to rebuild the warehouse in order to undo it. The best way to deal with this situation is to make sure the data warehouse is created with a large amount of details. However, the cost of this can be huge depending on the storage options you choose. Once you have filled your data warehouse with important information, you will need to use this data to help you make smart investment decisions. And tools that can allow you to do this fall under the topic of so-called business intelligence.

Business Intelligence is a field that is very diverse. It consists of things such as executive information systems, decision support systems, and on-line analytical processing. It can go in business intelligence break down to a field that is called multi-dimensional analysis tools. These are the tools that will allow the user to view data from a wide range of angles. Will query tool that allows the user to send SQL queries in a warehouse to search for results. Data mining is also a field that falls within the framework of business intelligence, and will allow you to search for patterns and relationships within the data warehouse.

Another tool is connected to data warehouses is data visualization. The tools that are used to visualize the data to provide visual data models. This data can come in the form of complex 3D images. The objective of data visualization is to allow the user to view trends in the way that is easier to understand than complex models that are based off of statistics. One tool that is allowing this area to move forward is VRML, or Virtual Reality Modeling Language. For data warehouses to function properly, it is also important to put the focus on metadata management. The metadata can be described as "information about information."
Must be managed meta data when the data is obtained or analyzed. The metadata will be held in a warehouse, and can give you important information about many of the tools the data warehouse. Became the metadata management process properly science within itself. If this is done correctly, the company can benefit greatly. The reason for this is important is that you can not allow organizations to analyze the changes that occur within the database tables. This is a tool that plays an important role to build a data warehouse.
Data storage is a field that is a bit complex. There are many sellers who are trying to advertising tools, but did not allow the cost and complexity with their products to be used by a large number of companies. Any company that is considering using data warehouses must make sure that they have taken the time to review and understand the technology. Can be useful only if you know how to use it. Once you understand and acquisition of technology, it is possible for you to gain a strong advantage over competitors. This has made it attractive data warehouses for many companies.


One of the biggest advantages of data warehouses is that they allow you to store information that can be used to improve marketing strategies for your company. Not only can you improve marketing strategies, but will also be able to make strategic decisions based on the information that has been collected and organized. With techniques such as data mining, data visualization, and will be able to discover patterns important that you do not know exist. That can detect patterns that allow the company to earn big profits.

Datawarehouse Disadvantages



Many of the vendors spend a great deal of time talking about the advantages of data warehouses, and why companies need them if they wish to survive in the global market. While there is a degree of truth in the statements made by many vendors, it is important for companies to realize that data warehouses are not a panacea, and find a solution to all the problems you will encounter the company.

To be able to maximize the efficiency of the data warehouse requires the company to look at it from multiple perspectives, and that includes those that are both positive and negative.

The ultimate goal of the data warehouse system for storing historical information about the transactions of the company, and provide these إينفورمتين in a way that will allow the business to make important decisions. However, these data do not constitute only a small part of the information that it needs to operate, and its value, but may be limited. In some cases, the end user does not have a strong interest in the old data processing, and a lot of this data is provided in the basic reports. Many of the markets that operate in today's companies are in constant transition. Depending on the circumstances, it may not be necessary to use a historical system.

In other words, data warehouses may be too much for most companies. This is especially true for many small businesses and medium-sized businesses that are analyzing their dealings with the need for expensive programs. One of the criticisms that are usually made of data warehouses is complexity. Implementation of a data warehouse can be complex so that they can make business processes more difficult to deal with. Some experts said that even these complications can ultimately stifle business. If the company is able to put less emphasis on some of the processes, the data warehouse can cause in the business environment to become more cluttered than that.

The second problem which has become very common with data stores is its cost. Like all the advanced technology, and when it was displayed for the first time data warehouses, and can only be for wealthy companies really afford it. Even today, most data warehouses are outside the price range of companies that do not fall under the Fortune 500 or 1000 class. While sellers in recent years begun tailoring their products to small businesses and medium-sized enterprises, many of these companies may not see the need for use that are very complicated. Given the speed in the world of business, and many companies are not patient enough to wait for the implementation of the data warehouse.

In the past, it was not uncommon for a data warehouse project to take several months to implement. In one case, it took 18 months to fully implement the system. Most of today's companies want results, and they want them quickly. They do not see the need to wait for months on the system did not prove, and can become inevitably fail. The return on investment for data warehouse projects are usually much lower than the sellers promise, and it will take a long time before the company begins to see a return on their investment. Many companies simply do not have the patience to wait for the returns.

It is also important for companies to pay attention to the data side of the warehouse. Because of the complexity of these systems, it can be said that data warehouses can take on a life of their own. It must be emphasized that the development of the data in the data warehouse for adding not for any specific purpose can reduce its value.

When you combine this with the fact that the costs involved with maintaining a data warehouse can become bigger, it is easy to see why companies should be careful in their decision to use it.


Data warehouses is a technology that has brought a great deal of success for many companies. However, many of the vendors paint a rosy picture, and fail to talk about the challenges facing the company. This is done because of the fact that the seller wants to sell the product. Companies must carefully analyze the organization to decide whether the data warehouse is conducive to their needs. Only after they've done this analysis can decide whether the data warehouse and really worth the time and cost.

Datawarehouse Benfits



There are a number of reasons why many large companies have spent large sums of money implementing data warehouses. Favor of the most basic use of data warehouses is that they store information can be found in such a way that it allows the business to make important decisions.

Instead of looking to the organization where it includes departments, data warehouses allows businessmen to look at the company as a whole. Another benefit of data warehouses is the ability to deal with the functions of a server connected to the query that is not used by most systems deal. The vast majority of companies want to develop transactional systems so there is a good chance that these are completed transactions within a time frame desirable. The biggest problem with the reports and queries is that these entities can reduce the chances of a deal being made in a good time frame. Should also be emphasized that the reports that run on the server by trading systems, it can be very difficult. Because of these challenges, many companies are seeking to alleviate the problem by implementing a data warehouse system. Another strong benefit of data warehouses is that they allow compnies to use the data models for the query tasks that are very difficult to handle transactions.

There are a number of ways that can be modeled data, and the goal of modeling is generally to the speed of reporting. Often this is done by star scheme, and generally not recommended for transaction processing systems. The reason for this is because some modeling techniques can slow transaction processing systems. At the same time, the units may speed up the transaction process server, but it will slow down the query. Perhaps one of the most important benefits of data warehouses is that they paved the way for an environment where a small amount of technical knowledge about databases can be used to write queries and speed of maintenance of these queries.

Simply play an important role in the success of the data warehouse, and this is something that companies will want to pay attention to in early. Can set most data warehouses, even in such a way that simple queries can be written by workers who do not have a lot of technical skill. Until then, workers who do not have a lot of technical skill often experience problems when you try to perform certain tasks. Data warehouses are unique in the fact that it can serve as a warehouse, a warehouse for transaction processing systems that have been cleaned. The data can be reported against them, and it may not require treatment process systems to be fixed calibration.

That data warehouses can be extremely effective because it will allow the user to make queries of data on a regular basis. And this can be done from many trading systems, and can also be done from external sources. Before the advent of data warehouses, and companies that wanted reports from several systems for the production of extracts data and run programs special logic to combine this data. In most cases, this strategy worked fine. Despite this, and perhaps of companies that have large amounts of data problems if they wanted to sort through it often. While there are a number of challenges facing these scenarios, the company can deal with them if they take the time to put the correct procedures.

On older systems, and often is removed data that has been considered to be older than transaction processing systems. This has been done for the purpose of making the response time is easier to maintain. For tasks that require a query, the data may be stored ancient and recent data in the data warehouse in a way that gives the user control over the response time. Workers may not play in some of the challenges based on the information that they need. When the implementation of data warehouses and designed properly, they can bring a large number of advantages for companies that use them. The company can give predictions about how the performance of the company as a whole, and they can allow executives and managers in making critical decisions that can help companies succeed.

Difference Between Datawarehouse and Database



What is the difference between Datawarehouse and Database?

There are a number of fundamental differences that separate the data warehouse from a database. The biggest difference between the two is that most databases put the focus on a single application, and this application will be one based on transactions. If the data were analyzed, and will be done within one area, but many areas are not uncommon.

Some of separate modules that can be included within the database include salaries or stock. Each system will have to put the focus on a single theme, and it will not deal with other areas. In contrast, data warehouses dealing with multiple domains simultaneously.

Because it deals with multiple subject areas, the data warehouse to find connections between them. This allows the data warehouse to show how the performance of the company as a whole, not in individual areas. Another aspect of data warehouses strong is its ability to support the analysis of trends. They are not volatile, and the information stored therein does not change as much as you do in a common database. The two types of data that you will want to become familiar with is the operational data and data to support decision-making. This purpose, shape, and structure of these types of data are completely different. In most cases, the operational data will be placed in a relational database.

In a relational database, and are often used tables, and they may be normal. Calibration will be operational data in a way that allows them to deal with transactions that are made on a daily basis. Each time an item is sold to the customer by the company, must be a record of it. As can be expected, and this data will be updated on a frequent basis. To ensure the efficiency of the system, the data must be placed in a certain number of tables, and tables must be fields. Because of this, it may be a single transaction at least five areas. While this system may be highly effective in the operational database, it is not conducive to the queries. In this case, the data to support decision-making is often useful, and it offers support for things that are not easily used by operational data.
If you want to get one bill, and often there is a need to join multiple tables. While operational data will mostly deal with transactions that are made daily, and data to support decision-making gives meaning to data that is operational. Can be divided into the differences between the data to support decision-making and operational data into three categories, and these are the dimensions of time, and granularity. Dimensions is a concept which indicates that the data is connected in different ways. The data that is stored in the data warehouse is often a multi-dimensional, and it is very different from the simple point of view, which is often seen with operational data. And many analysts fear the data with many aspects of the data dimensions.

The time deals with atomic transactions that are, or current. These transactions dealing with things like the movement of inventory, or for the purchase order. Generally, we will deal with operational data a short period of time. However, data to support decision-making tends to deal with the long time frames. Cares about many of the managers of the companies in transactions that have occurred over a certain period of time. Instead of dealing with the purchase of one client, and managers are often more interested in group buying patterns of customers. If it had just been a sale, will not be found in the data warehouse to support decision-making.


Granularity is the third concept that separates the data from operational data to support decision-making. Operational data will deal with transactions that occurred within a certain period of time. However, it is imperative to break the data to support decision-making to different parts of the assembly. While it can be summarized, it may also be more current. The managers within the organization need information that is summarized in different degrees. Data warehouses become more important in the information age, and they are a necessity for many large companies, as well as some medium-sized companies. They are more complicated than just a database, and they can find links in the data that can not be found easily in most databases.

HOW TO APPLY OBIEE PATCH



Steps for applying patch OBIEE 11.1.1.6.7

1.       Download the list  of patches as in zip format

1.1   15959887
1.2   15959877
1.3   15929063
1.4   15959899
1.5   15959861
1.6   15894670
1.7   15959917

2.       Extract the zip files in the folder d:\obiee11g\oracle_BI1

(Just extract the folders where we installed the oracle bi 11g\Oracle_BI1)
3.       Clear the files in the catalogmanager binary cache.
(Obiee11g\Oracle_BI1\bifoundation\web\catalogmanager\configuration\..)

Remove the files “org.eclipse.osg, org”, “eclipse.eqimnox.app”.

Hint: If u installing the patches for first time the appropriate files wont be their in the folder.

If the files were ther delete it.

4.       Open the command prompt set the path.
5.       Oracle11g\Oracle_BI1> set  ORACLE_HOME=d:\Obiee11g\Oracle_BI1
6.       Oracle11g\Oracle_BI1> set  PATH=%ORACLE_HOME%\bin;%PATH%
7.       Oracle11g\Oracle_BI1> set  JAVA_HOME=%ORACLE_HOME%\jdk
8.       Oracle11g\Oracle_BI1> set  PATH=%JAVA_HOME%\bin;%PATH%
9.       Oracle11g\Oracle_BI1> set  PATH=%ORACLE_HOME%\OPatch;%PATH%(here in OPatch OP is in upper case)
10.   Oracle11g\Oracle_BI1> cd 15959887

11.   Oracle11g\Oracle_BI1\15959887>opatch appy ( here in opatch everything is in lower case)

12.   Do the same for the next all patches don’t change the preference

13.   After applying the patches want to see the list of patches applies use the command
In command prompt “Obiee11g\Oracle_BI1>opatch lsinventory

14.   Copy the files BipublisherDesktop32.exe and Bipublisher64.exe from the following directorty
Obiee11g\Oracle_BI1\clients\bipublisher\repository\tools to Obiee11g\user_projects\domains\bifoundation_domain\config\bipublisher\repository\tools


15.   Start the services.

Increase temporary tablespace size in oracle



If you want to know the location of temporary tablespace:

SELECT tablespace_name, file_name, bytes
FROM dba_temp_files WHERE tablespace_name = 'TEMP';

If you want to delete the temporary tablespace:

ALTER DATABASE 
TEMPFILE 'C:\APP\BI\ORADATA\ORCL\TEMP01.DBF' 
DROP INCLUDING DATAFILES;


Recreate your tablespace with maxsize:

ALTER TABLESPACE temp ADD TEMPFILE
'C:\APP\BI\ORADATA\ORCL\TEMP01.DBF' SIZE 512m
AUTOEXTEND ON NEXT 250m MAXSIZE UNLIMITED;




Monday, 15 July 2013

Oracle Tablespace in LINUX




Want to create tablespace in linux which handle any sort of data?

If you creating any Production environment, Please do create your own tablespace and assign users for that tablespace


Here is the query for you 


 CREATE TABLESPACE oracle DATAFILE
'/oracle/obi_tablespace/oracle.dbf' SIZE 31G AUTOEXTEND ON NEXT 10M MAXSIZE UNLIMITED
LOGGING
PERMANENT
EXTENT MANAGEMENT LOCAL AUTOALLOCATE
BLOCKSIZE 8K
SEGMENT SPACE MANAGEMENT AUTO;

Tuesday, 9 July 2013

ODBC Database Connections for DAC



Install and configure on the Developer Machine that will host the DAC Client and Informatica PowerCenter Tools

The machines that will host these components require connectivity to the Oracle Business Analytics Warehouse (target) database, transactional (source) database(s), and the DAC and Informatica repository databases.

Start up ODBC Data Source Administrator.  In the System DSN table, click Add





You must use the Oracle Merant ODBC driver to create the ODBC connections.
The Oracle Merant ODBC driver is installed by the Oracle Business Intelligence Applications installer. Therefore, you will need to create the ODBC connections after you have run the Oracle Business Intelligence Applications installer and have installed the DAC Client










Select the ODBC driver “Oracle Merant ODBC Driver”. Click Finish






                                                                                                                                                                                                 Enter the information in the Oracle ODBC Driver Configuration Dialog Box; for example:



Data Source Name = DataWarehouse
Description = DataWarehouse
Server Name = bimaui
Client Version = 10gR1

Keep all other default values and settings intact.  Click “Test Connection”















Enter User Name “bawdev” and Password “bawdev”. Click OK











Click OK to return to Oracle ODBC Driver Configuration screen.  Click OK

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