INFORMATICA INTERVIEW QUESTIONS AND ANSWERS

Introduction

Informatica is software that specializes in data warehousing, which makes it a top choice for most industries across the world. Informatica extracts, transforms, and loads data from diverse sources to meet the requirements of clients. Informatica and its sub-products, like PowerCenter, mold data from sources and store it, which makes the maintenance of large amounts of data relatively easy. It is quite common for job seekers in the IT field to aspire to work for Informatica. So, here’s a list of commonly asked Informatica interview questions and answers to land a job in Informatica enterprises with ease.

Informatica interview questions and answers

What are lookup caches in Informatica?

When data is stored, usually it is stored as some discombobulating expression, which makes maintenance hard. Lookup caches are records that begin their entry as soon as data enters lookup transformation through integrated services. Lookup caches store values across rows as opposed to columns. Lookup caches are generally generated every time a lookup transformation is processed.

What are the types of lookup caches? (Informatica PowerCenter interview questions)

The following are the different types and configurations of lookup caches:

1. Static cache: The processing of the lookup is done by the integration services; the kind of cache that remains static is called static cache. Since the static cache does not change, the integration services need to rebuild it each time they process the lookup.
2. Persistent cache: Unlike the static cache, here the integration services will save the initial form of the cache during the lookup transformation process and use that next time. When the integration services process the lookup, the persistent caches do not change. Persistence caches can therefore be rebuilt using the initial saved form.
3. Dynamic Cache: A dynamic lookup is a cache that changes while the integration service processes the lookup. A dynamic lookup cache is built by the integration services when the lookup processes the first lookup request. During the processing of each row, the integration service dynamically inserts or updates data in the lookup cache and passes the data to the target. The dynamic cache is in sync with the target. Dynamic cache can be used when one wants to update a target based on new and changed records. Dynamic caches can also be used when the mapping requires a lookup on target data, but connections to the target are slow.
4. Shared Cache: In the same mapping, shared caches can be used in multiple lookup transformations. One cache is generated for each lookup transformation, as opposed to generating multiple lookup caches each time the lookup transformation is processed.

What are the different types of transformations?

• Expression transformation
• Update strategy transformation
• Router transformation
• Lookup transformation
• Filter transformation’
• Aggregator transformation
• Joiner transformation
• Sorted transformation
• Normalizer transformation
• Router transformation
• Rank transformation’
• Sequence generator transformation
• Stored procedure transformation
• XML source qualifier transformation

What makes the Informatica Power Center unique? (Informatica PowerCenter interview question)

The Informatica PowerCenter is unique in the following ways:

• Metadata: Metadata is the contextual information about a particular piece of data that accompanies the data itself. Informatica has a separate application called Metadata Manager, which is used to analyze and manage metadata from its respective repositories. This feature makes Informatica one of the most distinct software programs out there to include metadata in its UI.
• ETL-Informatica PowerCenter is used to extract, transform, and load data from various sources into the required database warehouse for conglomerate industries. The use of Informatica by various conglomerate giants makes this software a popular one in the mix.
• Support for various data sources: Since Informatica PowerCenter supports various data sources in its functionality, such as Oracle, Teradata, SQL, XML, etc., it can be considered one of the most reliable software out there.
• Workflow influence: Informatica has specially designated tools like the Workflow Designer, which is used to connect various tasks in the workflow window with links, which makes the tasks efficient in the interface. Softlogics Institute provides both live online and classroom Informatica training in Chennai; don’t forget to check it out.

How does SOAP enhance the UI of Informatica?

SOAP enhances the user interface of Informatica in the following ways:

• SOAP stands for Simple Object Access Protocol. It is similar to an internet protocol format, but this is used for messaging and communication in web services.
• The SOAP envelope defines the skeleton of the message, filtering out the unnecessary parts of the message.
• The SOAP header adds features in a scattered manner.
• The SOAP body contains and protects some of the most important parts of the information.
• SOAP is platform- and operating system-independent; it can be operated over numerous protocols, enabling communication between apps with different programming languages on both Windows and Linux.
• SOAP can easily pass through a firewall during its functional routine, which is a privilege that not all protocols enjoy.
• Accessibility with HTTP: Most protocols across the world use the HTTP protocol, which is supported by SOAP.

Explain the process of hierarchical-to-relational transformation.

The hierarchical-to-relational transformation is the process where the XML or JSON hierarchical input is converted into relational output. In this process, the hierarchical input from input ports is read, and the data is transformed into relational output at the transformation output ports. The definition of hierarchical data needs to be done using a schema file to do the hierarchical-to-relational transformation. The Hierarchical to Relational Transformation Wizard can be used to automatically map the data, eventually mapping to the relational output ports on the Transformation Overview view.

After the wizard generates the transformation, the data can be passed from the relational output ports to another transformation in a mapping.

Explain the reasons for using static caches. (Informatica interview questions for 5 years of experience)

The following are reasons to use static cache:

• The lookup in the static cache is unconnected. The static cache is one of the only caches that goes with an unconnected lookup.
• Increasing performance: usually, the integration service does not update the cache while it processes the lookup transformation, so the processing of a lookup transformation is faster in a static cache than the processing of a lookup transformation in a dynamic cache or any other cache.
• When the mapping runs, the lookup source does not change.
• The Integration Service should return to the default value for connected transformations or NULL for unconnected transformations when the lookup condition is false.

Enunciate the process of SQL transformation (Informatica interview questions for experienced)

During SQL transformation, the SQL queries are processed midstream during mapping. The input port values can be passed to parameters in the query or stored procedure. The transformation can delete, update, insert, and retrieve rows from a database. The SQL DDL statements can be run to create a table or drop a table midstream in a mapping. The SQL transformation is active. The transformation can return very well multiple rows for each input row.

It is possible to import a stored procedure from a database into the SQL transformation. When the stored procedure is imported, the developer tool creates the transformation ports that correspond to the parameters in the stored procedure. The developer tool also creates the stored procedure call.

How do you configure an SQL transformation to run a stored procedure?

To configure an SQL transformation to run a stored procedure, perform the following tasks:

• Transformation properties should be defined, including the database type to connect.
• A stored procedure should be imported to define the ports and create the stored procedure call.
• Ports should be manually defined to get result sets or additional stored procedures that one needs to run.
• Additional stored procedure calls should be added in the SQL editor.
• An SQL query can be configured in the transformation SQL editor. When running the SQL transformation, the transformation processes the query, returns rows, and returns any database error. SLA Institute has one of the best SQL server training in Chennai click here to learn more.

How do I configure an SQL transformation to run a query? (Informatica developer interview)

• To configure an SQL transformation to run a query, perform the following tasks:
• Transformation properties should be defined, including the database type to connect.
• The input and output ports should be defined.
• A SQL query should be created in the SQL editor.
• After the configuration and the transformation, the SQL transformation in a mapping should be configured to connect the upstream ports. The results of the data should be verified by a preview.

Describe the source-qualifier transformation and the tasks it is used for.

Source Qualifier Transformation changes source data types to their indigenous power center data types. It is compulsory for flat files.

Source Qualifier Transformation can be used to accomplish the following task:

• A custom query should be created to do a sum of calculations.
• The ORDER BY clause should be added to the default SQL query by integrated service when the number of sorted ports is defined.
• It’s possible to join two or more tables with primary key-foreign key relationships by linking the sources to one Source Qualifier transformation.
• The Select Distinct clause can make the Integrated Service add the Select Distinct statement to the primary SQL query.
• If the filter condition is included, then the integration service adds the WHERE clause to the default query.

Explain the data processor transformation. (Informatica interview question for 4 years of experience)

The process of processing unstructured and semi-structured file formats in a mapping is called data processor transformation. Messaging formats like HTML, XML, JSON, and PDF can be configured. Structured formats like ACCORD, HIPAA, EIFACT, and SWIFT can also be converted.

A data processor transformation can potentially contain multiple components to process data. Each component might contain other components.

For example, receiving customer invoices in Microsoft Word files. Configuring a data processor transformation to parse the data from each word file. Customer data should be extracted from the customer table. Order information should be extracted from the Orders table.

When creating a data processor transformation, defining an XMap, script, or library is important. An XMap converts an input hierarchical file into an output hierarchical file of another structure. A library converts an industry messaging type into an XML document with a hierarchy structure or from XML to an industry-standard format. A script can parse source documents into a hierarchical format, convert the hierarchical format to other file formats, or map a hierarchical document to another hierarchical format.

What kinds of scripts can be defined in data processor transformation? (Informatica interview questions for 10 years of experience)

The following are the types of scripts that can be defined in Data Processor Transformation:

Serializer: This converts an XML file to an output document of any format. The serializer’s output can be of any format, such as a text document, an HTML document, or a PDF.

Mapper: This converts XML source documents to another XML schema.

Transformer: By using this, any format can be modified. It can also be used to add, remove, convert, and make changes to text. Transformers can be used with a parser, mapper, or serializer. It can also function as a stand-alone component.

Parser: This converts source documents to XML. The parser will always output as XML, but the input can be of any format, such as HTML, Word, PDF, etc.

Streamer: Managing large mega-byte data dreams can be difficult, so this splits large input documents into segments. The streamer predominantly processes documents that have multiple messages, such as EDI or HIPAA files.

How does ETL help in the management of data? (Informatica interview questions and answers)

Extracting is the process of deriving data from different sources. The types of derived data are usually databases, XML, APIs, IMS, JSON, etc. Extracting data will help reduce so much diversity in file formats, which will make accessibility easy for users.

Transforming is the process of shaping the data into a usable form. Usually, in the transformation part of the process, things like correcting errors, removing duplicates, establishing a hierarchy in data, and making adjustments are done to meet the standards of the business. Transforming makes the data more reliable, accurate, and error-free.

Loading is the process where the transformed data is loaded into a data warehouse per requirement. This is the final part of the process. Loading will make the data secure, and posterity will greatly benefit from this. The longevity of the data is extended due to loading. The SLA Institute provides the best ETL testing training in Chennai.

Conclusion

Informatics is one of the fastest-developing areas of expertise in current times. A person with a well-versed knowledge of Informatica has a major advantage over people who don’t. Thus, the gates are open at the SLA Institute has Informatica Training in Chennai; do check them out.