Top 40 ETL Interview Questions and Answers 2024

ETL stands for Extract, Transform, Load. It is a process used to extract data from various sources, transform it into a usable format, and load it into a data warehouse.

• Extract: Data is extracted from different sources such as databases, files, APIs, etc.

• Transform: Data is cleaned, formatted, and transformed into a consistent structure.

• Load: The transformed data is loaded into a data warehouse for analysis and reporting.

• ETL is commonly used in data warehousing, business intel...read more

ETL is a process of extracting data from various sources, transforming it and loading it into a target system. ELT is a process of extracting data from various sources, loading it into a target system and then transforming it.

• ETL involves transforming data before loading it into the target system

• ELT involves loading data into the target system before transforming it

• ETL is best suited for structured data

• ELT is best suited for unstructured data

• ETL is a batch process

• ELT can be a...read more

ETL stands for Extract, Transform, Load and DWH stands for Data Warehouse.

• ETL is a process of extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a target system.

• DWH is a system used for storing and managing data from various sources for business intelligence purposes.

• ETL is a crucial step in populating a DWH with data.

• ETL involves data extraction, data transformation, and data loading.

• DWH is designed to support decis...read more

ETL stands for Extract, Transform, Load. It is a process of moving data from source systems to a target data warehouse.

• Extract: Data is extracted from various sources such as databases, files, APIs, etc.

• Transform: Data is transformed to fit the target data warehouse schema and to ensure data quality.

• Load: Data is loaded into the target data warehouse for analysis and reporting.

• ETL is a crucial process in building a data warehouse.

• ETL tools such as Informatica, Talend, and SSI...read more

Program to ETL pdf documents from sharepoint

• Use SharePoint API to access the pdf documents

• Extract data from pdf using libraries like PyPDF2 or pdfplumber

• Transform the extracted data as needed

• Load the transformed data into a database or another storage system

The option 'Ignore Case' is good when using table comparison transformation.

• Use 'Ignore Case' option when comparing text values to ignore differences in case.

• This option is helpful when comparing strings like names or addresses.

• It ensures that 'John' and 'john' are considered as equal during comparison.

I am using Talend version 7.3.1 for ETL development.

• I am currently using Talend version 7.3.1 for ETL development.

• The specific version of Talend being used is 7.3.1.

• Talend 7.3.1 offers various features and improvements for ETL processes.

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

Transform stage is used in ETL process to apply business rules, clean and enrich data before loading into target database.

• Transform stage is used to apply business rules to the data.

• It is used to clean and standardize data before loading into the target database.

• Transform stage can also be used to enrich data by combining multiple sources or adding calculated fields.

• Examples include converting data types, removing duplicates, and aggregating data.

There are several ways to read external data, such as using file input/output operations, database queries, web scraping, and API calls.

• File input/output operations: Reading data from files stored on the local system or network.

• Database queries: Retrieving data from databases using SQL or other query languages.

• Web scraping: Extracting data from websites by parsing HTML or using web scraping libraries.

• API calls: Fetching data from external systems or services through API endpo...read more

Comparing 2 flat files in GDE involves using the Join component and specifying the keys to match.

• Use the Join component in GDE to compare 2 flat files

• Specify the keys to match in the Join component

• Choose the type of join (inner, outer, left, right) based on the comparison needed

ETL stands for Extract, Transform, Load. It is a process of extracting data from various sources, transforming it into a consistent format, and loading it into a target database.

• Extract: Data is extracted from different sources such as databases, files, APIs, etc.

• Transform: Data is cleaned, validated, and transformed into a consistent format.

• Load: Transformed data is loaded into a target database or data warehouse.

• Stages of ETL: Extraction, Transformation, Loading.

I have worked on AWS Glue, AWS Data Pipeline, and AWS Lambda for ETL processes.

• AWS Glue for serverless ETL jobs

• AWS Data Pipeline for scheduling and orchestrating ETL workflows

• AWS Lambda for building custom ETL functions

I have worked on various types of transformations including Aggregator, Expression, Filter, Joiner, Lookup, Router, and Sorter.

• Aggregator transformation for performing calculations on groups of data

• Expression transformation for performing calculations and manipulating data

• Filter transformation for filtering out unwanted data

• Joiner transformation for joining data from multiple sources

• Lookup transformation for retrieving data from a database table

• Router transformation for direc...read more

Designing architecture for ETL involves identifying data sources, transformation processes, and target destinations.

• Identify data sources such as databases, files, APIs

• Design data transformation processes using tools like Apache Spark, Talend

• Implement error handling and data quality checks

• Choose target destinations like data warehouses, databases

Transformer stage is a processing stage in IBM InfoSphere DataStage used for data transformation.

• Used for transforming data from source to target in DataStage

• Can perform various operations like filtering, aggregating, joining, etc.

• Supports parallel processing for efficient data transformation

Set up ETL flow for data in Lake House using Databricks

• Connect Databricks to Lake House storage (e.g. Azure Data Lake Storage)

• Define ETL process using Databricks notebooks or jobs

• Extract data from Lake House, transform as needed, and load into target destination

• Monitor and schedule ETL jobs for automated data processing

ETL focuses on extracting, transforming, and loading data in a sequential process, while ELT involves loading data into a target system first and then performing transformations.

• ETL: Extract, Transform, Load - data is extracted from the source, transformed outside of the target system, and then loaded into the target system.

• ELT: Extract, Load, Transform - data is extracted from the source, loaded into the target system, and then transformed within the target system.

• ETL is sui...read more

ETL is required to extract, transform and load data from various sources into a target system.

• ETL helps to integrate data from multiple sources into a single system

• It ensures data accuracy, consistency and completeness

• ETL is used in data warehousing, business intelligence and analytics

• Examples of ETL tools include Informatica, Talend, and Microsoft SSIS

ETL stands for Extract, Transform, Load. It is a process of extracting data from various sources, transforming it into a usable format, and loading it into a target database.

• ETL tools include Informatica PowerCenter, Talend, Apache Nifi, Microsoft SQL Server Integration Services (SSIS), and IBM InfoSphere DataStage.

• Extract: Data is extracted from various sources such as databases, files, APIs, etc.

• Transform: Data is cleaned, validated, and transformed into a format suitable f...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

ETL architecture involves three main components: extraction, transformation, and loading.

• Extraction involves retrieving data from various sources such as databases, files, and APIs.

• Transformation involves cleaning, filtering, and converting data to make it usable for analysis.

• Loading involves storing the transformed data into a target database or data warehouse.

• ETL architecture can be designed using various tools such as Apache Spark, Talend, and Informatica.

• The architecture ...read more

ETL is better for batch processing, ELT is better for real-time processing.

• ETL is better for large volumes of data that need to be transformed before loading into a data warehouse.

• ELT is better for real-time processing where data can be loaded into a data warehouse first and then transformed as needed.

• ETL requires more storage space as data is transformed before loading, while ELT saves storage space by loading data first and transforming later.

ETL is Extract, Transform, Load where data is extracted, transformed, and then loaded into a target system. ELT is Extract, Load, Transform where data is extracted, loaded into a target system, and then transformed.

• ETL involves extracting data from source systems, transforming it, and then loading it into a target system.

• ELT involves extracting data from source systems, loading it into a target system, and then transforming it as needed.

• ETL is typically used when data needs t...read more

Data retrieval steps in Informatica ETL process

• Identify the source data to be extracted

• Create source and target connections in Informatica

• Design mappings to extract, transform, and load data

• Use transformations like Filter, Joiner, Lookup, etc.

• Run the ETL job to retrieve data from source to target

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.

ETL stands for Extract, Transform, Load and ELT stands for Extract, Load, Transform.

• ETL involves extracting data from various sources, transforming it to fit into a target schema, and loading it into a data warehouse.

• ELT involves extracting data, loading it into a target system, and then transforming it as needed within the target system.

• ETL is commonly used in traditional data warehousing scenarios, while ELT is often used in big data processing and cloud-based data platform...read more

ELT stands for Extract, Load, Transform while ETL stands for Extract, Transform, Load.

• ELT focuses on extracting data from the source, loading it into a target system, and then transforming it within the target system.

• ETL focuses on extracting data from the source, transforming it, and then loading it into a target system.

• In ELT, the target system has the processing power to handle the transformation tasks.

• In ETL, the transformation tasks are performed by a separate system or ...read more

ETL stands for Extract, Transform, Load while ELT stands for Extract, Load, Transform.

• ETL involves extracting data from source systems, transforming it, and then loading it into the target system.

• ELT involves extracting data from source systems, loading it into the target system, and then transforming it as needed.

• ETL is suitable for scenarios where data needs to be transformed before loading, while ELT is useful when raw data needs to be loaded first and then transformed.

• ETL...read more

Types of data ETL include batch processing, real-time processing, and change data capture.

• Batch processing involves processing data in large chunks at scheduled intervals.

• Real-time processing involves processing data as it is generated.

• Change data capture involves identifying and capturing changes made to data sources.

• Examples: SSIS for batch processing, Apache Kafka for real-time processing, and Debezium for change data capture.

ETL stands for Extract, Transform, Load. It is a process used to extract data from various sources, transform it into a consistent format, and load it into a target database.

• Extract: Data is extracted from different sources such as databases, files, APIs, etc.

• Transform: Data is cleaned, validated, and transformed into a consistent format suitable for analysis.

• Load: The transformed data is loaded into a target database or data warehouse for further analysis.

• ETL tools like Info...read more

ETL stands for Extract, Transform, Load while ELT stands for Extract, Load, Transform.

• ETL involves extracting data from source systems, transforming it, and then loading it into a data warehouse or data lake.

• ELT involves extracting data from source systems, loading it into a data lake or data warehouse, and then transforming it as needed.

• ETL is suitable for structured data while ELT is suitable for unstructured data.

• ETL requires a separate transformation engine while ELT leve...read more

ETL is a process of extracting data from various sources, transforming it and loading it into a target system. ELT is a process of extracting data from various sources, loading it into a target system and then transforming it.

• ETL involves transforming data before loading it into the target system

• ELT involves loading data into the target system before transforming it

• ETL is more suitable for structured data

• ELT is more suitable for unstructured data

• ETL is more commonly used in t...read more

ETL stands for Extract, Transform, Load. It is a process used to extract data from various sources, transform it into a consistent format, and load it into a target database or data warehouse.

• Extract: Involves extracting data from multiple sources such as databases, files, APIs, etc.

• Transform: Involves cleaning, filtering, aggregating, and converting the extracted data into a format suitable for analysis.

• Load: Involves loading the transformed data into a target database or da...read more

ETL is Extract, Transform, Load where data is extracted, transformed, and loaded into a data warehouse. ELT is Extract, Load, Transform where data is extracted, loaded into a data warehouse, and then transformed.

• ETL involves extracting data from source systems, transforming it according to business rules, and loading it into a data warehouse.

• ELT involves extracting data from source systems, loading it into a data warehouse, and then transforming it as needed.

• ETL is suitable f...read more

Initial load in ETL refers to the process of loading data from source systems into the data warehouse for the first time.

• Initial load is typically a one-time process to populate the data warehouse with historical data.

• It involves extracting data from source systems, transforming it as needed, and loading it into the data warehouse.

• Initial load is often done using bulk loading techniques to efficiently transfer large volumes of data.

• It is important to carefully plan and execut...read more

ETL and ELT tools are used for extracting, transforming, and loading data in data warehousing and analytics processes.

• ETL stands for Extract, Transform, Load and involves extracting data from various sources, transforming it into a usable format, and loading it into a data warehouse or database.

• ELT stands for Extract, Load, Transform and involves extracting data, loading it into a target system, and then transforming it as needed within the target system.

• ETL tools are typical...read more

ETL stands for Extract, Transform, Load. It is a process of extracting data from various sources, transforming it, and loading it into a target system.

• ETL is used to integrate data from different sources into a unified format.

• The fundamental factors to consider while working on any ETL tool include data extraction, data transformation, and data loading.

• Data extraction involves retrieving data from various sources such as databases, files, APIs, etc.

• Data transformation involve...read more

ETL stands for Extract, Transform, Load. It is a process used to extract data from various sources, transform it into a consistent format, and load it into a target database.

• ETL involves three main layers: Extraction, Transformation, and Loading.

• Extraction: Data is extracted from various sources such as databases, files, APIs, etc.

• Transformation: Data is cleaned, validated, and transformed into a consistent format.

• Loading: Transformed data is loaded into a target database or ...read more

Different stages in ETL include extraction, transformation, and loading of data.

• Extraction: Retrieving data from various sources such as databases, files, APIs, etc.

• Transformation: Cleaning, filtering, and converting the extracted data into a format suitable for analysis.

• Loading: Loading the transformed data into a data warehouse or target database for further processing.