Top 50 Data Integration Interview Questions and Answers

Duplicates in mappings can be handled using various techniques in Informatica.

• Use the Sorter transformation to sort the data and then use the Aggregator transformation to eliminate duplicates.

• Use the Expression transformation with the DISTINCT keyword to remove duplicates.

• Use the Rank transformation to rank the data and then filter out the duplicates based on the rank.

• Use the Lookup transformation to check for duplicates in a reference table and filter them out.

• Use the Update...read more

Event handlers in SSIS are workflows that are triggered by specific events during the execution of a package.

• Event handlers can be used to perform additional tasks or actions based on the success or failure of specific tasks within a package.

• Common events that can trigger event handlers include OnError, OnPreExecute, OnPostExecute, OnWarning, etc.

• Event handlers can be configured to send emails, log information, execute scripts, or perform other actions based on the event that...read more

Handle data from multiple sources by integrating, cleaning, and validating it before analysis.

• Integrate data using ETL tools like Informatica or Talend.

• Cleanse data by removing duplicates, correcting errors, and standardizing formats.

• Validate data by performing quality checks and ensuring consistency.

• Use data governance practices to maintain data integrity and security.

• Examples: Combine sales data from CRM, website analytics, and POS systems for comprehensive analysis.

Delta load in ADF is achieved by comparing source and target data and only loading the changed data.

• Use a Lookup activity to retrieve the latest watermark or timestamp from the target table

• Use a Source activity to extract data from the source system based on the watermark or timestamp

• Use a Join activity to compare the source and target data and identify the changed records

• Use a Sink activity to load only the changed records into the target table

IDOC can be imported using SAP PI/PO through IDOC adapter.

• Create a sender IDOC adapter in SAP PI/PO

• Configure the adapter with the necessary parameters

• Create a receiver channel to receive the IDOC

• Map the IDOC fields to the target system fields

• Test the IDOC import process

I have been working on the BODS system for 5 years.

• I have 5 years of experience working on the BODS system.

• I have successfully implemented multiple projects using the BODS system.

• I am familiar with the latest updates and features of the BODS system.

• I have trained and mentored junior consultants on the BODS system.

• I have a deep understanding of the BODS system's architecture and functionality.

Inbound IDoc implementation involves configuring partner profiles and processing the IDoc data. Extension in IDoc allows adding custom fields to the standard IDoc structure.

• Create a partner profile for the sender system in transaction WE20

• Define the IDoc type and message type in transaction WE82

• Create a port and assign it to the partner profile in transaction WE21

• Configure the inbound processing in transaction BD51

• Extension in IDoc involves adding custom segments or fields to...read more

I have worked on various PI adapters including PI JDBC, PI ODBC, PI Web API, and PI AF SDK.

• PI JDBC adapter for connecting to PI data archive using Java

• PI ODBC adapter for connecting to PI data archive using ODBC drivers

• PI Web API adapter for accessing PI data archive through web services

• PI AF SDK adapter for accessing PI Asset Framework data

To connect SQL server to Databricks, use JDBC/ODBC drivers and configure the connection settings.

• Install the appropriate JDBC/ODBC driver for SQL server

• Configure the connection settings in Databricks

• Use the JDBC/ODBC driver to establish the connection

Data can be called into framework using various methods.

• Data can be called using APIs

• Data can be called using database connections

• Data can be called using file imports

• Data can be called using web services

• Data can be called using data providers

Design an app to bring data from external vendors in different formats

• Create a data ingestion pipeline to collect data from various vendors

• Implement data transformation processes to standardize formats

• Utilize APIs or web scraping techniques to retrieve data from vendors

• Use a database to store and manage the collected data

• Implement data validation and cleansing techniques to ensure data quality

ALE is a technology used for exchanging business data between different systems within an organization, while EDI Idoc is a specific format used for exchanging data between different systems outside an organization.

• ALE (Application Link Enabling) is used for communication within an organization's systems.

• EDI (Electronic Data Interchange) Idoc is a specific format used for exchanging data between different systems outside an organization.

• ALE is real-time data exchange, while E...read more

IR stands for Integration Runtime. Dataset is a representation of data, while linked service is a connection to the data source.

• IR is a compute infrastructure used to provide data integration capabilities

• Dataset is a structured representation of data used in data engineering tasks

• Linked service is a connection to a data source, providing access to the data

• IR enables data movement and transformation between different data sources

• Dataset defines the schema and structure of the ...read more

To select the last 10 lines from a file in Informatica mapping, use the Source Qualifier transformation with a SQL override.

• Add a Source Qualifier transformation to the mapping.

• In the Source Qualifier transformation, specify the file as the source and set the appropriate file properties.

• Write a SQL override in the Source Qualifier transformation to select the last 10 lines from the file.

• Use the ROWNUM pseudocolumn or the ROW_NUMBER() function to order the rows in descending o...read more

To kill a job in Datastage

• Stop the job manually from the Director client

• Terminate the job from the command line using the dsjob command

• Kill the job process from the operating system level

• Delete the job from the Datastage repository

Data can be extracted from various sources like databases, APIs, flat files, etc. and transformed using ETL tools before loading into BI.

• Identify data sources and their accessibility

• Choose appropriate ETL tool for data extraction and transformation

• Design data model and schema for BI

• Load data into BI using ETL tool

• Validate and verify data accuracy and completeness

Connected lookup is used in mapping flow, while unconnected lookup is used in expression transformation.

• Connected lookup is used in mapping flow, while unconnected lookup is used in expression transformation.

• Connected lookup receives input values directly from the pipeline, while unconnected lookup receives input values from the calling transformation.

• Connected lookup returns a value to the pipeline, while unconnected lookup returns a value to the calling transformation.

• Conne...read more

Incremental load in ADF and SSIS involves identifying new or updated data and loading only those changes.

• In ADF, use watermark columns to track the last loaded value and filter data based on this value

• In SSIS, use CDC (Change Data Capture) components or custom scripts to identify new or updated data

• Both ADF and SSIS support incremental loading by comparing source and target data to determine changes

ETL tools connect to servers through various methods such as JDBC, ODBC, APIs, and FTP

• ETL tools can connect to servers using JDBC (Java Database Connectivity) for databases like MySQL, Oracle, etc.

• ODBC (Open Database Connectivity) can be used to connect ETL tools to servers for data extraction and loading

• APIs (Application Programming Interfaces) are commonly used to connect ETL tools to web services and cloud platforms

• FTP (File Transfer Protocol) can be utilized to transfer f...read more

CDC stage refers to the Centers for Disease Control and Prevention's classification system for the progression of a disease.

• CDC stage is used to categorize the severity and progression of diseases.

• It helps in determining the appropriate treatment and management strategies.

• CDC stages are often used in the context of HIV/AIDS, cancer, and other chronic illnesses.

• Each stage is defined based on specific criteria, such as symptoms, laboratory results, and disease progression.

• For e...read more

ADF stands for Azure Data Factory, a cloud-based data integration service that allows you to create, schedule, and manage data pipelines.

• ADF allows you to create data-driven workflows for orchestrating and automating data movement and data transformation.

• You can use ADF to ingest data from various sources, process and transform the data, and then publish the data to different destinations.

• ADF supports a wide range of data integration scenarios, including batch processing, rea...read more

Integration Runtime (IR) is a compute infrastructure that provides data integration capabilities across different network environments.

• IR is used in Azure Data Factory to provide data integration capabilities

• There are three types of IR: Azure, Self-hosted, and Azure-SSIS

• Azure IR is fully managed by Microsoft and is used for data movement in the cloud

• Self-hosted IR allows data movement between on-premises and cloud data stores

• Azure-SSIS IR is used for running SQL Server Integr...read more

Integrating data in EPBcS involves mapping data sources, defining data mappings, and setting up data integration processes.

• Map data sources to EPBcS data model

• Define data mappings between source systems and EPBcS

• Set up data integration processes to transfer data into EPBcS

• Use ETL tools like Informatica or Talend for data integration

• Ensure data quality and consistency during integration

A mapplet is a reusable object in Informatica PowerCenter that contains a set of transformations.

• Mapplets can be used in multiple mappings to perform the same set of transformations.

• They can simplify mapping development by encapsulating common logic.

• Mapplets can have input and output ports to connect with other transformations.

There are 4 main transformations in SAP BODS.

• There are 4 main transformations in SAP BODS: Query transform, Case transform, Map operation transform, and Validation transform.

• Query transform is used to extract data from a source and apply filters, joins, and other operations.

• Case transform is used for conditional processing and data manipulation.

• Map operation transform is used for data mapping and transformation.

• Validation transform is used to validate data based on predefined...read more

PowerCenter is an on-premise data integration tool, while IICS is a cloud-based integration platform.

• PowerCenter is on-premise, IICS is cloud-based

• PowerCenter is a traditional ETL tool, IICS is a modern integration platform

• PowerCenter requires infrastructure setup, IICS is managed by Informatica

• PowerCenter is more suitable for large enterprises with on-premise data, IICS is better for cloud-based integrations

FBDI is used for bulk data imports while ADFDI is used for interactive data entry and updates.

• FBDI stands for File-Based Data Import and is used for bulk data imports into Oracle Cloud applications.

• ADFDI stands for Application Development Framework Desktop Integration and is used for interactive data entry and updates.

• FBDI requires users to prepare data in predefined templates and upload them in batches.

• ADFDI allows users to directly interact with Oracle Cloud applications fr...read more

Integrating entities involves connecting different systems or components to work together seamlessly.

• Identify the entities that need to be integrated

• Determine the communication protocols and data formats for integration

• Develop APIs or middleware to facilitate communication

• Test the integration to ensure proper functionality

• Monitor and maintain the integration for ongoing performance

The source row will be treated as an update, but the target object will be deleted.

• The session level property 'Treat source row as Update' will be applied to the source row.

• The target object will be deleted regardless of the update status of the source row.

• This can result in data loss if the source row contains important information.

Yes, integration between SAP DataSphere and SAC is possible.

• SAP DataSphere can be integrated with SAP Analytics Cloud (SAC) to enable data sharing and analysis.

• Integration allows users to access and analyze data from SAP DataSphere within SAC.

• Data can be imported from SAP DataSphere into SAC for further analysis and visualization.

• Integration enables real-time data updates and synchronization between SAP DataSphere and SAC.

• Users can create dashboards, reports, and visualizatio...read more

For each loop in SSIS

• For each loop is used to iterate through a collection of items in SSIS

• It can be used to loop through files, folders, database tables, etc.

• The loop can be configured to run until a certain condition is met

• Variables can be used to store the current item being processed

• The loop can be nested within other loops or control flow tasks

Indirect file loading is a process of loading data from a file that is not directly connected to the target system.

• Indirect file loading involves using an intermediate file to load data into the target system.

• This method is useful when the source file is not compatible with the target system.

• Indirect file loading can be achieved using Informatica's indirect file method or by using a custom script.

• Examples of indirect file loading include loading data from a mainframe system o...read more

Joiner transformation - connected and unconnected look up difference

• Joiner transformation is used to join two sources based on a common key

• Connected lookup is used when the lookup table is in the same mapping

• Unconnected lookup is used when the lookup table is outside the mapping

• Connected lookup returns multiple rows while unconnected lookup returns only one row

• Joiner transformation can be used with both connected and unconnected lookup

Talend provides a unified platform for data integration and management, enabling organizations to easily connect and analyze data from various sources.

• Talend simplifies data integration processes by providing a drag-and-drop interface for designing workflows.

• It supports a wide range of data sources and formats, making it versatile for different use cases.

• Talend offers built-in connectors for popular databases, cloud services, and applications, reducing the need for custom cod...read more

Types of EDI mapping include structural mapping, cross-referencing mapping, and code set mapping.

• Structural mapping involves mapping data from one format to another based on the structure of the data

• Cross-referencing mapping involves mapping data by using a cross-reference table to match values between different systems

• Code set mapping involves mapping data by translating codes from one system to another, such as translating product codes or currency codes

IICS implementation involves integrating cloud applications with on-premises RDBMS for seamless data flow.

• Utilize Informatica Intelligent Cloud Services (IICS) to connect cloud applications with on-premises RDBMS

• Leverage connectors provided by IICS for seamless integration

• Ensure proper security measures are in place for data transfer between cloud and on-premises systems

I have worked with various Talend components including tMap, tFilterRow, tAggregateRow, tSortRow, tFileInputDelimited, tFileOutputDelimited, tJavaFlex, tJavaRow, tNormalize, tDenormalize, tPivotToColumnsDelimited, tUnpivot, tReplace, tExtractRegexFields, tExtractXMLField, tExtractJSONField, tConvertType, tJoin, tReplicate, tSample, tRandom, tSystem, tSleep, tWarn, tDie, tSendMail, tFTP, tSSH, tS3Input, tS3Output, tRedshiftInput, tRedshiftOutput, tSalesforceInput, tSalesforceO...read more

I am using the Extract, Transform, Load (ETL) methodology and implementing it via Syniti.

• I am extracting data from various sources such as databases, files, and applications.

• I am transforming the data to meet the requirements of the target system or database.

• I am loading the transformed data into the target system using Syniti's data integration tools.

• For example, I may be using Syniti Data Replication to replicate data from one database to another in real-time.

The process involves analyzing and merging data from various sources to create a single, accurate, and reliable 'golden' record.

• Identify all sources of data that need to be merged

• Cleanse and standardize the data to ensure consistency

• Use data matching and deduplication techniques to eliminate duplicates

• Create rules for resolving conflicts between different data sources

• Implement a master data management system to store and manage the golden record

SCD Type 2 is a technique used to track historical changes in data over time in a data warehouse.

• SCD Type 2 maintains a separate row for each change in data, with a start and end date.

• It requires a surrogate key to uniquely identify each row.

• Informatica provides a built-in SCD Type 2 transformation to implement this technique.

• Example: tracking changes in customer addresses over time.

Different row transformations in SSIS include Conditional Split, Derived Column, Lookup, and Merge.

• Conditional Split: Routes data rows to different outputs based on specified conditions.

• Derived Column: Adds new columns or modifies existing columns using expressions.

• Lookup: Retrieves data from a related table based on a specified key.

• Merge: Combines data from multiple sources into a single dataset.

I would approach different sources of data by first understanding the data structure, cleaning and transforming the data, and then integrating it for analysis.

• Identify the different sources of data and their formats (e.g. CSV, Excel, databases, APIs)

• Assess the quality of data and perform data cleaning and transformation processes

• Integrate the data from various sources using tools like SQL, Python, or BI tools

• Create a data model to combine and analyze the integrated data

• Perfor...read more

Use GetMetadata activity to get recent files and then copy them using Copy Data activity in ADF.

• Use GetMetadata activity in Azure Data Factory to retrieve information about recent files.

• Filter the files based on their last modified date to get the most recent ones.

• Use Copy Data activity to copy the recent files to the desired destination.

• Configure the Copy Data activity with the source and destination datasets and mappings.

BODS stands for Business Objects Data Services, a data integration tool by SAP for ETL processes.

• BODS is used for extracting, transforming, and loading data between different systems.

• It can connect to various data sources like databases, applications, and files.

• BODS allows for data quality management, data profiling, and metadata management.

• It is commonly used for data migration, data warehousing, and data synchronization.

• Example: Using BODS to extract customer data from a CR...read more

Use logical path to send IDOC flat file to application layer

• Define logical path in SAP system using transaction code FILE

• Assign logical path to a physical directory where the IDOC flat file is stored

• Use program RSEOUT00 to send the IDOC flat file to the application layer

PDI stands for Process Data Interchange. It is used to exchange data between different systems in a standardized format.

• PDI is used to transfer data between different systems in a standardized way.

• It helps in ensuring data integrity and consistency during data exchange.

• PDI can be used in various industries such as manufacturing, healthcare, and finance.

• Example: Using PDI to transfer production data from a manufacturing plant to a central database for analysis.

Yes, I have worked on BODS system.

• I have experience working with BODS system in my previous role as a Lead Consultant.

• I have successfully implemented data integration solutions using BODS system.

• I am familiar with the various components and functionalities of BODS system.

• I have worked on data extraction, transformation, and loading tasks using BODS system.

• I have also worked on integrating BODS system with other systems and databases.

In SSIS, you can manage different sets of columns in two files by using conditional splits and dynamic column mapping.

• Use a conditional split transformation to separate the data flow based on the file type or column presence

• Create separate data flows for each file type and handle the columns accordingly

• Use dynamic column mapping to map the columns dynamically based on the file type

• You can use expressions and variables to dynamically handle the column mapping

• Handle any additio...read more

There are three types of LKM used in file to file loads: LKM File to SQL, LKM SQL to File, and LKM File to File.

• LKM File to SQL is used to load data from a file to a SQL database

• LKM SQL to File is used to extract data from a SQL database to a file

• LKM File to File is used to move data from one file to another

Informatica is a data integration tool used for ETL (Extract, Transform, Load) processes.

• Used for data integration and ETL processes

• Can connect to various data sources and targets

• Provides a graphical interface for designing and managing workflows

• Supports scheduling and monitoring of workflows

• Can handle large volumes of data

• Examples of use cases include data warehousing, data migration, and data synchronization

Containers in SSIS are used to group and organize tasks and workflows.

• Containers provide a way to group related tasks together.

• They help in organizing and managing complex workflows.

• There are different types of containers in SSIS, such as Sequence Container, For Loop Container, and Foreach Loop Container.

• Containers can be nested within each other to create hierarchical structures.

• They allow for better control flow and error handling in SSIS packages.

Triggers in ADF are events that can initiate the execution of a pipeline or a specific activity within a pipeline.

• Event-based triggers

• Schedule-based triggers

• Tumbling window triggers

• Blob storage event triggers

Transformations in BODS are used to manipulate data during the ETL process.

• Transformations are used to extract, transform, and load data in BODS

• Common transformations include Query, Case, Map_Operation, etc.

• Transformations can be used to filter, join, aggregate, and cleanse data

• Example: Query transformation is used to filter data based on specific criteria

To perform a full load in SSIS, you can use the Data Flow Task with a source and destination component.

• Create a Data Flow Task in the Control Flow tab of the SSIS package.

• Add a source component to extract data from the source system.

• Add a destination component to load data into the destination system.

• Map the columns from the source to the destination.

• Run the package to execute the full load.

Push down optimization is a technique used in Informatica to improve performance by pushing data transformation operations to the source or target database.

• Push down optimization reduces the amount of data transferred between the source and target systems.

• It allows the database engine to perform transformations, aggregations, and filtering, leveraging its processing power.

• By pushing down operations, Informatica minimizes network latency and improves overall performance.

• Exampl...read more

IICS is a cloud-based integration platform while Power Center is an on-premise data integration tool.

• IICS is a subscription-based service while Power Center is a perpetual license product.

• IICS offers pre-built connectors to various cloud applications while Power Center requires custom coding for cloud integrations.

• IICS has a web-based interface while Power Center has a desktop-based interface.

• IICS offers real-time data integration while Power Center is more batch-oriented.

• IIC...read more

SCD Type 2 in Informatica can be achieved using multiple transformations.

• Use a Source Qualifier transformation to read data from the source.

• Use an Expression transformation to derive new columns or modify existing ones.

• Use a Lookup transformation to look up data in a reference table.

• Use a Router transformation to route data to different targets based on conditions.

• Use a Filter transformation to filter out unwanted data.

• Use a Sequence Generator transformation to generate surro...read more

Informatica is a data integration tool used for ETL (Extract, Transform, Load) processes in data engineering.

• Informatica is used for extracting data from various sources like databases, flat files, etc.

• It can transform the data according to business rules and load it into a target data warehouse or database.

• Informatica provides a visual interface for designing ETL workflows and monitoring data integration processes.

• It supports scheduling, monitoring, and managing data integra...read more

To load files in Informatica, use the Source Analyzer to import the files, create a mapping, and then run the workflow.

• Use the Source Analyzer to import the files into Informatica

• Create a mapping in Informatica to define how the data should be transformed and loaded

• Use the Workflow Manager to create a workflow that executes the mapping and loads the data

Implementing SCD Type 2 in Informatica involves using Slowly Changing Dimension transformations and mapping variables.

• Use Slowly Changing Dimension (SCD) transformations in Informatica to track historical changes in data.

• Create mapping variables to keep track of effective start and end dates for each record.

• Use Update Strategy transformations to handle inserts, updates, and deletes in the target table.

• Implement Type 2 SCD by inserting new records with updated data and marking...read more

SCD2 logic in Informatica is used to track historical changes in data by creating new records for each change.

• SCD2 stands for Slowly Changing Dimension Type 2

• It involves creating new records for each change in data, while maintaining a link to the previous record

• It typically includes effective start and end dates to track the validity of each record

• SCD2 logic is commonly used in data warehousing to maintain historical data accurately

Lookups in Informatica are used to retrieve data from a reference table based on a matching condition.

• Lookups are used to perform data transformations and enrich the data in a mapping.

• There are three types of lookups in Informatica: Connected, Unconnected, and Static.

• Connected lookup returns multiple rows and can be used in any transformation.

• Unconnected lookup returns a single value and is used in expressions or transformations.

• Static lookup is used to retrieve a single row ...read more

Informatica Router and Union are used for data integration and transformation.

• Informatica Router is used to route data based on certain conditions.

• Informatica Union is used to combine data from multiple sources.

• Router can be used to split data into multiple targets based on conditions.

• Union can be used to merge data from multiple sources into a single target.

• Router can be used to implement complex data flows.

• Union can be used to eliminate duplicates from the data.

Slowly Changing Dimension (SCD) in Informatica is used to track historical data changes in a data warehouse.

• SCD Type 1: Overwrite old data with new data

• SCD Type 2: Add new row for each change with effective start and end dates

• SCD Type 3: Add columns to track changes without adding new rows

Push down optimization in Informatica is a technique used to improve performance by pushing processing tasks to the source or target database.

• Push down optimization reduces the amount of data transferred between Informatica and the database

• It can improve performance by leveraging the processing power of the database

• Examples include using SQL queries in Source Qualifier transformations to push filtering and aggregation operations to the database