Informatica Interview Questions and Answers
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Best Informatica Interview Questions and Answers
There is a huge need in organizations for certified and expert personnel to make use of the tools like Informatica to elevate their progress. And, if you are an aspirant looking for a career in the field of Informatica, then you have landed at the right destination. Coursejet is a one-stop resource where you can find the top 50 most frequently asked Informatica interview questions with answers. We have covered all the essential topics in Informatica. And, all these top 50 Informatica interview questions and answers are prepared in consultation with the top interviewers in this field. These questions will be suitable for both beginners and experienced personnel.
You can glance at these top 50 Informatica Interview Questions and Answers before attending an interview to crack the interview. Also, it has been analyzed that personnel in the field of Informatica are earning more in the recent days and there are huge openings for talented Informatica handlers. So, if you are preparing for Informatica interview, then do not miss reading all those questions, these top 50 Informatica interview questions and answers will certainly assist you in cracking your interview at ease. We wish you all success in your career!
Informatica is one of the highly used data integration tools. Informatica can easily interoperate with various standards, applications and systems and it is very fast too. It can also easily adapt consistent changes in the system, organization and field or market. It can also monitor tasks and identify failed task or job at ease.
The main components associated with Informatica are namely client tools or applications, server, repository server and repository.
There is no limit to the number of repositories in Informatica but the count actually depends on the number of ports.
Source definition, Target definition, Mappings, Mapplets and Transformations are some of the metadata types that are stored in the repository.
No, only one mapping can be validated at a time and simultaneous validation is not possible.
Informatica is used in majority of the organizations for data migration which means transferring a company’s old mainframe system to a new database system. It can also be used in data warehousing, in this case, Informatica is used to move a data that is available in the production system to the warehouse. Informatica is used in data integration in which Informatica is used to incorporate data from various databases and file-based systems.
A single unified database that contains all the business information of an organization and caters it all over the organization is known as an enterprise data warehouse. It undergoes a unified approach to sort and present data and these data can be accessed and classified in line with the subject.
In workflow manager, the Informatica workflow will build a workflow that is logically connected with tasks to perform to execute a code. And, then this workflow will execute all the associated tasks automatically in a specific order.
Some of the tools used in workflow manager are as follows:
- Task designer
- Worklet designer
- Workflow designer
The process of grouping workflow tasks in a set is known as a “Worklet” while those workflow tasks include timer, decision, command, event wait, mail, session, link, assignment, control and more.
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A domain is something that comprises of nodes and relationships that are covered by a single point of organization.
The various types of transformation are as follows:
- Aggregator transformation
- Expression transformation
- Filter transformation
- Joiner transformation
- Lookup transformation
- Normalizer transformation
- Rank transformation
- Router transformation
- Sequence generator transformation
- Stored procedure transformation
- Sorter transformation
- Update strategy transformation
- XML source qualifier transformation
Aggregate transformation allows users to perform aggregate calculations which might include sum, average and more.
Non aggregate transformations can be performed using expression transformation. In a single row, the values can be calculated before it is being written on the target.
Filter transformation can be used to filter rows in mapping; filter condition is applied only when the data is transformed through filter transformation. It has all the input/output ports and the rows that meet the conditions can only be passed through the particular filter.
Lookup transformation is used to search for or look up for a relevant data from a specific source, source qualifier and target. It can look up resources like flat file, relational table, view and synonym. This lookup transformation can be of active and passive or connected and unconnected transformation.
The several types of ports available to create a lookup transformation are as follows:
Input port (I)
Output port (O)
Look up Ports (L)
Return Port (R)
Connected Lookup | Unconnected Lookup |
It makes use of the both static and dynamic cache | Unconnected lookup cannot be dynamic |
All the lookup columns are cached | Only the lookup ports of the return port and lookup conditions are cached |
It supports only user-defined default values | It does not support any user-defined default values |
It returns more than one column values | Only one column value is returned |
Unconnected lookup may contain any number of input parameters. The input parameters can be represented as column 1, column 2 and more but it provides only one return value.
Informatica lookups can be both cached and un-cached where cached lookups can be of either form which includes static and dynamic. Lookup cache can be separated as persistent and non-persistent lookups based on the ability of the Informatica to retain or delete the cache after the completion of the session.
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The different types of lookup cache are as follows:
- Static cache
- Dynamic cache
- Persistent cache
- Shared cache
- Recache
Static Cache | Dynamic Cache |
Here, the cache memory will not be refreshed even if the record is either inserted or updated in the given lookup table. Only in the next session run, the cache memory will be refreshed | Here, the cache memory will be refreshed instantly immediately when the record is inserted, updated or deleted in the lookup table |
Aggregator cache is used to store the transition files that are found in the local buffer memory and to store the transformation value if addition memory is needed.
Persistent lookup cache stores the data on the server and so there is no need to develop a database query while a lookup is taking place and it also saves more time.
Active Transformation | Passive Transformation |
The number of rows passing through the transformation can be changed | The number of rows passing through the transformation cannot be changed |
It can change the transaction boundary | It maintain the transaction boundary |
Active transformation can change the type of row | The type of row cannot be changed |
“Sorter” actually comes under an active transformation as it removes the duplicates present in a key and consistently modifies the number of rows present.
The different types of join present within a joiner transformation are as follows:
- Normal join
- Master outer join
- Detail outer join
- Full outer join
Listed below are some of the output files that can be created by an Informatica server while a session is running:
- Informatica server log
- Session log file
- Session detail file
- Performance detail file
- Control file
- Reject file
- Post session email
- Indicator file
- Output file
- Cache file
Input group, Output group and Default group are the types of group present in the router transformation.
Router transformation is less complex, highly efficient and performance better than the filter transformation.
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Router Transformation | Filter Transformation |
It actually belongs to a single input and multi output group transformation | It is actually a single input and single output group transformation |
More than one filter conditions can be specified | Only one filter condition can be specified |
On partitioning a section, the server’s overall efficiency will be improved and the other transformations can be executed in parallel.
Firstly, we need to configure session to partition source data and then we will have to install the Informatica server machine in the multifold CPU.
In mapping variable, the values are actually changed while executing a session and in mapping parameter, the values do not undergo any changes on executing a session.
Session is actually a collection of commands with which a server moves the data to the target effectively while a batch is a collection of individual tasks.
There is no limit and as many sessions can be comprised in a batch but as lower the sessions, the migration turns simpler and easier.
The different dimensions present in Informatica are as follows:
- Junk
- Degenerative
- Conformed
The reusable objects that consist of a certain set of transformations are known as mapplets.
Mapplets can be reused and it contains only of the transformations and can be used for complex calculations while mapping cannot be reused and it contains all source, target and transformations, also mapping can be used for less complex calculations.
Some of the special features of complex mapping are as follows:
- The requirements are difficult
- It contains several numbers of transformations
- It undergoes complex business logic
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With the help of sorter transformation and select distinct options, we can delete the duplicate rows present in the flat files.
Listed below are the techniques used to filter rows with the help of Informatica transformation:
- Source Qualifier
- Joiner
- Filter
- Router
Source qualifier, lookup and target are some of the transformations where SQL override can be used.
SQL Override | Lookup Override |
It limits the number of incoming rows that are entering the mapping pipeline | It actually limits the number of lookup rows in order to neglect the overall table scan by storing the lookup time and caches being used |
It has to entered in the query manually if we require it | “Order By” clause is used by default |
Any type of joins can be provided by writing specific query | Only non-equi joins are provided |
Parallel processing in Informatica can be implemented using the ways listed below:
- Database partitioning
- Round-Robin partitioning
- Hash Auto-keys partitioning
- Hash User-keys partitioning
- Key Range partitioning
- Pass-through partitioning
The pipeline partition actually divides a pipeline into several reader or transformation or writer thread. All these partitions can be execution at a single time within a mapping and thus the overall efficiency is improved.
Target-based, source-based and user-defined are some of the configurable commit types in Informatica
Summary filter can be applied to the record group that contains common values while detail filter can be applied to every record present in a database.
The fact table is always de-normalized as it contains data from the dimension table known as primary key and it also has some of the foreign keys and measures.
It can be created with the help of file properties while creating mapping or by using session properties at the session level.
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