Top 150 Data Engineering Interview Questions and Answers

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

You can load multiple tables at a time using Azure Data Factory by creating a single pipeline with multiple copy activities.

• Create a pipeline in Azure Data Factory

• Add multiple copy activities to the pipeline, each copy activity for loading data from one table

• Configure each copy activity to load data from a different table

• Run the pipeline to load data from all tables simultaneously

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

Design a data pipeline for processing and analyzing large volumes of data efficiently.

• Identify data sources and types of data to be processed

• Choose appropriate tools and technologies for data ingestion, processing, and storage

• Design data processing workflows and pipelines to transform and analyze data

• Implement data quality checks and monitoring mechanisms

• Optimize data pipeline for performance and scalability

Build a pipeline to connect http source and bring data in adls

• Set up a data ingestion tool like Apache NiFi or Azure Data Factory to pull data from the http source

• Transform the data as needed using tools like Apache Spark or Azure Databricks

• Store the data in Azure Data Lake Storage (ADLS) for further processing and analysis

There are three types of triggers available in Azure Data Factory: Schedule, Tumbling Window, and Event.

• Schedule trigger: Runs pipelines on a specified schedule.

• Tumbling Window trigger: Runs pipelines at specified time intervals.

• Event trigger: Runs pipelines in response to events like a file being added to a storage account.

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

A datapipeline is a system that processes and moves data from one location to another in a structured and efficient manner.

• Datapipelines are designed to automate the flow of data between systems or applications.

• They typically involve extracting data from various sources, transforming it into a usable format, and loading it into a destination for analysis or storage.

• Examples of datapipelines include ETL (Extract, Transform, Load) processes in data warehousing and streaming dat...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 data warehouse.

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

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

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

• Example: Extracting custome...read more

You can load data using delta table in ADF by using the Copy Data activity and specifying the delta format.

• Use the Copy Data activity in ADF to load data into a delta table

• Specify the delta format in the sink settings of the Copy Data activity

• Ensure that the source data is compatible with the delta format

Data pipeline is built by extracting, transforming, and loading data from various sources to a destination for analysis and reporting.

• Data extraction: Collect data from different sources like databases, APIs, logs, etc.

• Data transformation: Clean, filter, and transform the data to make it usable for analysis.

• Data loading: Load the transformed data into a destination such as a data warehouse or database for further processing.

• Automation: Use tools like Apache Airflow, Apache Ni...read more

ETL process involves extracting data from various sources, transforming it to fit business needs, and loading it into a target database.

• Extract: Retrieve data from different sources like databases, files, APIs, etc.

• Transform: Clean, filter, aggregate, and convert data to meet business requirements.

• Load: Insert the transformed data into a target database or data warehouse.

• Example: Extracting sales data from a CRM system, transforming it to calculate total revenue, and loading ...read more

Variables are used to store values that can be changed, while parameters are used to pass values into activities in ADF.

• Variables can be modified within a pipeline, while parameters are set at runtime and cannot be changed within the pipeline.

• Variables are defined within a pipeline, while parameters are defined at the pipeline level.

• Variables can be used to store intermediate values or results, while parameters are used to pass values between activities.

• Example: A variable ca...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.

• 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

Data pipeline is a series of tools and processes used to collect, process, and move data from one system to another.

• Data pipeline involves extracting data from various sources

• Transforming the data into a usable format

• Loading the data into a destination for storage or analysis

• Examples include ETL (Extract, Transform, Load) processes, Apache Kafka, and AWS Data Pipeline

In my organization, we followed a standard ETL process for data integration and transformation.

• Extracted data from various sources such as databases, flat files, and APIs

• Transformed the data using business rules and data mapping

• Loaded the transformed data into a target database or data warehouse

• Used tools such as Informatica PowerCenter and Talend for ETL

• Performed data quality checks and error handling during the ETL process

I have used activities such as Copy Data, Execute Pipeline, Lookup, and Data Flow in Data Factory.

• Copy Data activity is used to copy data from a source to a destination.

• Execute Pipeline activity is used to trigger another pipeline within the same or different Data Factory.

• Lookup activity is used to retrieve data from a specified dataset or table.

• Data Flow activity is used for data transformation and processing.

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

Data pipeline flow involves data ingestion, processing, storage, and analysis.

• Data is first ingested from various sources such as databases, APIs, or files.

• The data is then processed to clean, transform, and enrich it for analysis.

• Processed data is stored in a data warehouse, data lake, or other storage solutions.

• Finally, the data is analyzed using tools like SQL, Python, or BI platforms to derive insights.

• Example: Data is ingested from a CRM system, processed to remove dupli...read more

ETL process involves extracting data from various sources, transforming it to fit business needs, and loading it into a target system.

• Extract data from various sources such as databases, flat files, and web services

• Transform data by cleaning, filtering, and aggregating it to fit business needs

• Load transformed data into a target system such as a data warehouse or a database

• ETL tools such as Informatica, Talend, and SSIS are used to automate the ETL process

• ETL process is crucia...read more

ADF stands for Azure Data Factory, a cloud-based data integration service. ADB stands for Azure Databricks, an Apache Spark-based analytics platform.

• ADF is used for data integration and orchestration, while ADB is used for big data analytics and machine learning.

• ADF provides a visual interface for building data pipelines, while ADB offers collaborative notebooks for data exploration and analysis.

• ADF supports various data sources and destinations, while ADB is optimized for pr...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.

Architecting a data pipeline involves designing a system to collect, process, and analyze data efficiently.

• Identify data sources and determine how to extract data from them

• Design a data processing workflow to clean, transform, and enrich the data

• Choose appropriate tools and technologies for data storage and processing

• Implement monitoring and error handling mechanisms to ensure data quality and reliability

• Consider scalability and performance requirements when designing the pip...read more

ETL process involves extracting data from various sources, transforming it to fit business needs, and loading it into a target database.

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

• Transform the data by cleaning, filtering, aggregating, and structuring it.

• Load the transformed data into a target database or data warehouse.

• ETL tools like Informatica, Talend, and SSIS are commonly used for this process.

IR in ADF pipeline stands for Integration Runtime, which is a compute infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments.

• IR in ADF pipeline is responsible for executing activities within the pipeline.

• It can be configured to run in different modes such as Azure, Self-hosted, and SSIS.

• Integration Runtime allows data movement between on-premises and cloud data stores.

• It provides secure connectivity and data en...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

Data pipeline is a series of processes that collect, transform, and move data from one system to another.

• Data pipeline involves extracting data from various sources

• Data is then transformed and cleaned to ensure quality and consistency

• Finally, the data is loaded into a destination for storage or analysis

• Examples of data pipeline tools include Apache NiFi, Apache Airflow, and AWS Glue

The ETL flow of our project involves extracting data from various sources, transforming it according to business rules, and loading it into a data warehouse.

• Extract data from multiple sources such as databases, APIs, and flat files

• Transform the data using ETL tools like Informatica or Talend

• Apply business rules and data cleansing techniques during transformation

• Load the transformed data into a data warehouse for analysis and reporting

Incremental load in ADF is achieved by using watermark columns to track the last loaded data and only loading new or updated records.

• Use watermark columns to track the last loaded data

• Compare the watermark column value with the source data to identify new or updated records

• Use a filter condition in the source query to only select records with a timestamp greater than the watermark value

• Update the watermark column value after each successful load

Design a scalable data pipeline for processing large volumes of data efficiently.

• Utilize distributed computing frameworks like Apache Spark or Hadoop for parallel processing

• Implement data partitioning and sharding to distribute workload evenly

• Use message queues like Kafka for real-time data ingestion and processing

• Leverage cloud services like AWS S3 for storing and accessing data

• Implement data quality checks and monitoring to ensure data integrity

The ETL process involves extracting data from a source, transforming it to fit the target system, and loading it into the destination.

• Extract data from source system

• Transform data to fit target system

• Load transformed data into destination system

Linked services in ADF are connections to external data sources or destinations that allow data movement and transformation.

• Linked services are used to connect to various data sources such as databases, file systems, and cloud services.

• They provide the necessary information and credentials to establish a connection.

• Linked services enable data movement activities like copying data from one source to another or transforming data during the movement process.

• Examples of linked se...read more

Data pipelines are essential for processing and transforming data from various sources to a destination for analysis.

• Data pipelines involve extracting data from different sources such as databases, APIs, or files.

• Data is then transformed and cleaned to ensure consistency and accuracy.

• Finally, the processed data is loaded into a destination such as a data warehouse or analytics platform.

• Tools like Apache Airflow, Apache NiFi, or custom scripts can be used to create and manage ...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.

• It supports a wide range of data sources, including Azure Blob Storage, Azure SQL Database, and on-premises data sources.

• You can use ADF to ingest data from various sources, transform the data using compute services such as A...read more

My data pipelines architecture involves a combination of batch and real-time processing using tools like Apache Spark and Kafka.

• Utilize Apache Spark for batch processing of large datasets

• Implement Kafka for real-time data streaming

• Use Airflow for scheduling and monitoring pipeline tasks

Control flow activities in Azure Data Factory (ADF) are used to define the workflow and execution order of activities.

• Control flow activities are used to manage the flow of data and control the execution order of activities in ADF.

• They allow you to define dependencies between activities and specify conditions for their execution.

• Some commonly used control flow activities in ADF are If Condition, For Each, Until, and Switch.

• If Condition activity allows you to define conditiona...read more

Activities in ADF and their uses

• Data movement activities like Copy Data and Data Flow

• Data transformation activities like Mapping Data Flow and Wrangling Data Flow

• Data orchestration activities like Execute Pipeline and Wait

• Control activities like If Condition and For Each

• Integration Runtimes for executing activities in ADF

Triggers in Azure Data Factory (ADF) are events that cause a pipeline to execute.

• Types of triggers in ADF include schedule, tumbling window, event-based, and manual.

• Schedule triggers run pipelines on a specified schedule, like daily or hourly.

• Tumbling window triggers run pipelines at specified time intervals.

• Event-based triggers execute pipelines based on events like file arrival or HTTP request.

• Manual triggers require manual intervention to start a pipeline.

Types of triggers in Azure Data Factory include schedule, tumbling window, event-based, and manual.

• Schedule trigger allows you to run pipelines on a specified schedule

• Tumbling window trigger runs pipelines at specified time intervals

• Event-based trigger runs pipelines based on events like file arrival or HTTP request

• Manual trigger allows you to manually trigger pipeline runs

Get meta data in ADF is used to retrieve information about datasets, tables, and columns in Azure Data Factory.

• Get meta data in ADF can be used to understand the structure and properties of data sources.

• It helps in designing data pipelines by providing insights into the data being processed.

• Examples of meta data include column names, data types, and schema information.

Copy Activity in ADF is used to move data between supported data stores

• Copy Activity is a built-in activity in Azure Data Factory (ADF)

• It can be used to move data between supported data stores such as Azure Blob Storage, SQL Database, etc.

• It supports various data movement methods like copy, transform, and load (ETL)

• You can define source and sink datasets, mapping, and settings in Copy Activity

• Example: Copying data from an on-premises SQL Server to Azure Data Lake Storage usin...read more

Pipeline design on Azure Data Factory involves creating and orchestrating data workflows.

• Identify data sources and destinations

• Design data flow activities

• Set up triggers and schedules

• Monitor and manage pipeline runs

Linked Service connects to external data sources, while Dataset represents the data within the data store.

• Linked Service is used to connect to external data sources like databases, APIs, and file systems.

• Dataset represents the data within the data store and can be used for data processing and analysis.

• Linked Service defines the connection information and credentials needed to access external data sources.

• Dataset defines the schema and structure of the data stored within the d...read more

Dynamic file ingestion in ADF involves using parameters to dynamically load files into Azure Data Factory.

• Use parameters to specify the file path and name dynamically

• Utilize expressions to dynamically generate file paths

• Implement dynamic mapping data flows to handle different file structures

Different kinds of triggers in Data Factory and their use cases

• Schedule Trigger: Runs pipelines on a specified schedule, like daily or hourly

• Tumbling Window Trigger: Triggers pipelines based on a defined window of time

• Event Trigger: Triggers pipelines based on events like file arrival or HTTP request

• Data Lake Storage Gen2 Trigger: Triggers pipelines when new data is added to a Data Lake Storage Gen2 account

ADF activities include data movement, data transformation, control flow, and data integration.

• Data movement activities: Copy data from source to destination (e.g. Copy Data activity)

• Data transformation activities: Transform data using mapping data flows (e.g. Data Flow activity)

• Control flow activities: Control the flow of data within pipelines (e.g. If Condition activity)

• Data integration activities: Combine data from different sources (e.g. Lookup activity)