Azure Data Engineer Interview Questions for Beginners

If you're new to Azure and looking to land a job as a Data Engineer, you're in the right place. This section will cover some essential Azure Data Engineer interview questions for beginners.

To start, let's talk about Azure Data Factory (ADF). ADF is a cloud-based data integration service that allows you to create, schedule, and manage data pipelines. It's a crucial tool for any Data Engineer working with Azure.

ADF is built on a cloud-based architecture, making it highly scalable and reliable. This means you can easily handle large volumes of data and ensure your pipelines run smoothly.

As a Data Engineer, you'll need to know how to use ADF to create data pipelines, which involves designing and implementing data workflows. This includes using ADF's various components, such as data flows, pipelines, and datasets.

Data flows are used to transform and process data, while pipelines are used to schedule and manage data pipelines. Datasets, on the other hand, are used to store and manage data.

Azure Data Engineer Interview Questions cover a range of topics, from basic to expert-level. To get started, you can expect basic questions like how to manage data pipelines using Azure Data Factory, or what the different integration runtimes are in Azure Data Factory.

Intermediate-level questions, on the other hand, dive deeper into specific tools and technologies, such as optimizing query performance in Azure Synapse Analytics or implementing schema-on-read in Azure Data Lake Storage. You can find a list of intermediate-level questions in the following areas:

Expert-level questions, meanwhile, focus on complex architecture and design considerations, such as architecting a multi-tier data lake using Azure services or designing a data engineering solution for streaming ETL using Event Hubs and Azure Stream Analytics.

As a data engineer, you'll likely be asked about Azure Data Factory, so let's start with that. The data source in Azure Data Factory is the source or destination system that comprises the data intended to be utilized or executed. Types of data can include binary, text, CSV files, JSON files, image files, video, audio, or a proper database.

Azure Data Factory allows you to merge or combine several rows into a single row using the "Aggregate" transformation. This is a useful feature for data engineers, as it enables them to simplify complex data sets.

To work with Azure Data Factory, you'll need to have either an Azure SQL Managed Instance or an Azure SQL Database as the hosting location for your SSIS IR and SSISDB catalog. This will provide you with the necessary infrastructure to execute your data pipelines.

Here are some key Azure Data Factory concepts to keep in mind:

These concepts will be useful to know when working with Azure Data Factory, and will likely be asked about in an interview.

Polybase is used to optimize data ingestion into the PDW and support T-SQL. It lets developers transfer external data transparently from supported data stores, no matter the storage architecture of the external data store.

Polybase is a powerful tool for Azure data engineers. In fact, it's used to transfer data from external data stores.

To use Polybase effectively, you need to understand its core functionality. Polybase optimizes data ingestion into the Parallel Data Warehouse (PDW).

With Polybase, you can transfer data from various external data stores. This includes data from different storage architectures.

As a data engineer, you'll likely encounter Azure Stream Analytics in your work. Azure Stream Analytics is the recommended service for stream analytics on Azure, and it allows you to handle, consume, and analyze streaming data from Azure Event Hubs and Azure IoT Hub.

You can also ingest static data from Azure Blob Storage. This service provides several benefits, including the ability to see and preview incoming data directly in the Azure interface.

One of the key features of Azure Stream Analytics is its SQL-like query language, called SAQL. SAQL's built-in functions enable you to detect patterns in the incoming stream of data.

You can write and test transformation questions using the Azure portal and SAQL. This allows you to quickly deploy your inquiries into production.

Here are the main benefits of using Azure Stream Analytics to process streaming data:

Azure Synapse Link is a game-changer for data engineers. It's a cloud-native HTAP capability that enables you to do near-real-time analytics on operational data stored in Azure Cosmos DB.

Azure Synapse Link allows Azure Synapse Analytics and Azure Cosmos DB to work together seamlessly. This means you can easily integrate your data warehouses and databases to get a unified view of your data.

With Azure Synapse Link, you can get fast and accurate insights from your operational data. This is especially useful for applications that require real-time analytics, such as fraud detection or supply chain management.

Azure Synapse Link for Azure Cosmos DB is a powerful tool that can help you make data-driven decisions. By providing near-real-time analytics, it enables you to respond quickly to changing business conditions.

Data Processing and ETL is a crucial aspect of Azure Data Engineer interview questions. To create an ETL process in Azure Data Factory, you need to follow these steps: create a Linked Service for the source data store, create a Linked Service for the destination data store, create a dataset for data saving, build the pipeline and add the copy activity, and schedule the pipeline by inserting a trigger.

Azure Data Factory supports both ETL and ELT paradigms, making it a versatile tool for data processing. To execute an SSIS package in Data Factory, you must create an SSIS integration runtime and an SSISDB catalog hosted in the Azure SQL server database or Azure SQL-managed instance.

Here are the steps to create an ETL process in Azure Data Factory in detail:

By following these steps and understanding the capabilities of Azure Data Factory, you can effectively process and transform data to meet your business needs.

Creating an ETL process in Azure Data Factory is a straightforward process that involves several key steps. You'll need to create a Linked Service for the source data store, such as a SQL Server Database.

To do this, you'll need to create a Linked Service for the destination data store, which in this case is the Azure Data Lake Store. This step is crucial for data saving purposes.

A dataset should be created for data saving, which will serve as a blueprint for the data you're trying to transfer. You can think of it as a template that outlines the structure and format of your data.

The pipeline is where the magic happens, and it's where you'll add the copy activity to transfer the data from the source to the destination. This is the core of the ETL process.

Here are the steps in detail:

By following these steps, you'll be able to execute an ETL process in Azure Data Factory with ease. However, there's one more thing to consider: executing an SSIS package. To do this, you'll need to create an SSIS integration runtime and an SSISDB catalog hosted in the Azure SQL server database or Azure SQL-managed instance.

ETL vs ELT is a crucial debate in the data processing world. Azure Data Factory is a cloud-based tool that supports both ETL and ELT paradigms.

ETL stands for Extract, Transform, Load, a traditional approach that involves extracting data from sources, transforming it into a suitable format, and then loading it into a target system.

ELT, on the other hand, is a newer approach that stands for Extract, Load, Transform. This means data is first extracted from sources, loaded into a data warehouse, and then transformed into the desired format.

Azure Data Factory supports both ETL and ELT paradigms, making it a versatile tool for data processing.

Azure provides a range of services and features that make it an attractive choice for data engineers. These include Azure Databricks, which is a fast, easy, and collaborative Apache Spark-based analytics platform.

Azure Synapse Analytics is another key feature, offering a unified analytics service that integrates enterprise data warehousing and big data analytics. It supports various data sources and provides a scalable and secure solution for data processing.

Azure Data Factory is also worth mentioning, as it enables data engineers to create, schedule, and manage data pipelines across different sources and destinations.

Structured data is data that follows a tight format and has the same fields or properties throughout. This allows it to be easily searched using query languages like SQL.

Structured data is often referred to as relational data, thanks to its shared structure. It's the type of data that can be easily managed and analyzed.

Structured data is different from unstructured data, which doesn't follow a specific format. This makes it harder to search and analyze.

Structured data is essential for many applications, including databases and data warehouses. It's the backbone of many business systems.

Structured data can be easily queried using SQL, which is a powerful language for managing relational data. This makes it a crucial tool for data analysis and business intelligence.

Linked services in Azure Data Factory are primarily used for two purposes.

One of the main purposes is for Data Store representation, which includes storage systems like Azure Blob storage accounts, file shares, or Oracle DB/ SQL Server instances.

These data stores serve as the foundation for your data pipelines, allowing you to connect and manipulate data from various sources.

Linked services can also be used for Compute representation, which involves the underlying VM executing the activity defined in the pipeline.

This allows for the execution of activities and the processing of data, making it an essential component of Azure Data Factory pipelines.

In summary, linked services play a crucial role in Azure Data Factory by enabling data store and compute representation, making it easier to manage and process data.

Here's a quick rundown of the two purposes of linked services in Azure Data Factory:

Serverless computing is a game-changer for data engineers, allowing them to focus on higher-level tasks without worrying about infrastructure management.

Azure Functions, for example, is a popular serverless offering that enables developers to build event-driven applications with minimal overhead.

Serverless computing can help reduce costs, as you only pay for the resources used, and increase scalability, as resources can be easily scaled up or down to match changing workloads.

Serverless Computing is a game-changer for developers, as it allows them to write code that doesn't require any infrastructure, making it a cost-effective option.

This means users only pay for the resources they use, and not for a fixed amount of resources.

The code is typically present on the client-side or the server, but serverless computing is stateless, so it doesn't need any infrastructure.

This approach is particularly useful for short-term tasks, where the code is executed for a brief period, and then stopped.

Users can access compute resources as needed, without having to provision or manage any servers.

Pipeline Scheduling is a crucial aspect of Serverless Computing and Automation.

To schedule a pipeline, you can take the help of the scheduler trigger or the time window trigger. This trigger uses the wall-clock calendar schedule and can plan pipelines at periodic intervals or calendar-based recurring patterns.

Scheduling pipelines allows you to automate tasks and workflows, making your workflow more efficient and streamlined.

In Azure Data Factory, parameters are defined at the pipeline level, allowing for the passing of arguments to execute the pipeline run on-demand or upon using a trigger.

You can pass parameters to a pipeline run in Data Factory to make your workflow more dynamic and flexible.

To deploy code to higher environments in Data Factory, you can trigger an automated CI/CD DevOps pipeline to promote code to environments like Staging or Production.

The Look-up activity in Data Factory can return the result of executing a query or stored procedure, making it a powerful tool for data transformation and control flow.

In Data Factory, parameters can be used to pass arguments to execute a pipeline run, giving you more control over your workflow.

You can use the Look-up activity in Data Factory to return a singleton value or an array of attributes, which can be consumed in subsequent activities like copy data or transformation.

Data Factory's pipeline level parameters allow you to pass arguments to execute a pipeline run, making it easier to manage complex workflows.

By using the Look-up activity, you can execute a query or stored procedure and return the result, which can be used to drive your workflow forward.

In a Data Factory, the default limit on any entities is set to 5000, including pipelines, data sets, triggers, linked services, Private Endpoints, and integration runtimes.

You can request to raise this limit to a higher number by creating an online support ticket.

The default limit of 5000 entities is a significant number, but it may not be enough for larger projects.

If you need to exceed this limit, you'll need to take the extra step of submitting a support ticket.

This process is straightforward, but it does require some extra effort.

In Azure, data storage options include Azure Cosmos DB, Azure SQL Database, and Azure Table Storage. You can also use services like Azure Files and Azure Blobs to store loose files.

Azure provides a variety of message storage and delivery options, including Azure Queues and Event Hubs. These services cater to different use cases and help manage data efficiently.

To secure and manage access to data in Azure, you can use Azure Active Directory, Azure Key Vault, Azure Policy, and Azure Access Control (ACS). These tools help control who can access your data and what they can do with it.

Here are some key tools and services for securing and managing data access in Azure:

Azure Storage offers a range of options for storing data.

You can use Azure Cosmos DB for a database, Azure SQL Database for relational data, or Azure Table Storage for NoSQL data.

Azure provides message storage and delivery options, including Azure Queues and Event Hubs.

Services like Azure Files and Azure Blobs allow you to store loose files.

Microsoft recommends using the General-purpose v2 option for new storage accounts. This is a great starting point for most users.

For those who need more control over their storage, the Blob Storage option is a good choice, but it's more suitable for large-scale applications.

The General-purpose v2 option is a good all-around choice because it offers a balance between performance and cost.

Access management and security are crucial aspects of any cloud computing implementation. Azure Active Directory is a centralized identity management solution that can be used to secure and manage access to data in Azure.

You can use Azure Active Directory to create and manage users, groups, and permissions, making it easy to control who can access your data and what they can do with it. This is especially useful when migrating a massive amount of data to the cloud, as I experienced with my project integrating Azure Cosmos DB with an on-premises SQL Server database.

Azure Key Vault is a secure storage solution that can be used to store secrets and keys for encrypting data in Azure. You can use Azure Key Vault to securely store encryption keys and manage their lifecycle.

Azure Policy is a service that can be used to enforce policies for resources in Azure. You can use Azure Policy to enforce policies for data access and management, such as ensuring that sensitive data is encrypted at rest and in transit.

Here are some key tools and services in Azure for securing and managing access to data:

By using these tools and services, you can ensure that your data is secure and that access to it is controlled and managed in a way that meets your needs.

Azure Event Hubs is a cloud-based event processing tool that can collect and handle millions of events per second.

It serves as the front entrance to an event pipeline, accepting and collecting data until processing resources are available. A publisher sends data to the Event Hubs, and a consumer or subscriber examines data from the Event Hubs, with Event Hubs acting as a decoupling layer between these two entities.

This decoupling helps manage circumstances in which the rate of event creation exceeds the rate of consumption, allowing for more efficient event handling.

An Event Hub is a cloud-based event processing tool that can collect and handle millions of events per second.

It serves as the front entrance to an event pipeline, accepting and collecting data until processing resources are available.

A publisher sends data to the Event Hub, and a consumer or subscriber examines data from it.

This decoupling allows for the management of circumstances in which the rate of event creation exceeds the rate of consumption.

Event Hubs stand between publishers and subscribers to spread an event stream's production and consumption.

This helps to manage the flow of events and ensure that data is processed efficiently.

Assessing Event Hub Resiliency is crucial to ensure data delivery even in the face of unexpected downtime. Event Hubs saves messages received from your sender application even if the hub is inaccessible.

This means that messages collected after the hub is down are strongly forwarded to your application as soon as the hub is up and running again. You can test this functionality by disabling your Event Hub in the Azure portal.

To do this, disable your Event Hub, then re-enable it and re-run your receiver application. Check Event Hubs metrics for your namespace to see if all sender messages were successfully transferred and received.

Azure Data Factory (ADF) is a powerful tool that helps you manage and process large amounts of data.

Datasets in ADF are essentially the data that you'll use in your pipeline activities as inputs and outputs, signifying the structure of data inside linked data stores like documents, files, folders, etc.

ADF is needed because of the increasing amount of big data that needs to be refined into actionable business insights.

A dataset in ADF can be any connected data store, such as a file, folder, document, or even a blob storage folder and container, and it determines where the data will be read from. An Azure blob dataset, for example, details the blob storage folder and container from which a particular pipeline activity must read data to continue processing.

Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create, schedule, and manage your data pipelines.

At its core, ADF is designed to help you move and transform data from one place to another. Datasets are the foundation of ADF, and they represent the structure of data inside linked data stores like documents, files, and folders.

Datasets can be thought of as a blueprint for your data, outlining what's inside and how it's organized. For example, an Azure blob dataset describes the folder and container in blob storage from which a specific pipeline activity must read data as input for processing.

With ADF, you can create datasets from a variety of sources, including Azure Blob Storage, Azure Data Lake Storage, and more. This flexibility makes it easy to integrate data from different systems and locations into your pipelines.

By understanding how datasets work in ADF, you'll be able to build more effective and efficient data pipelines that meet your business needs.

You can use breakpoints to debug specific activities in your pipeline. A breakpoint is a point where the pipeline execution is paused.

To add a breakpoint, click the circle present at the top of the activity. This allows you to debug up to the second activity only.

You can use breakpoints in pipelines with multiple activities to identify where issues are occurring.

Azure Data Factory Integration Runtime is the compute infrastructure for Azure Data Factory pipelines. It serves as a bridge between activities and linked services.

This compute environment allows activities to be performed in the closest region to the target data stores.

In Azure Data Engineer interviews, you'll often be asked about integrating Data Factory with machine learning data. Yes, you can train and retrain the model on machine learning data from pipelines and publish it as a web service.

Data Factory can handle large volumes of data and perform complex transformations, making it an ideal choice for machine learning data pipelines. This allows you to leverage the strengths of both tools to build robust and scalable machine learning models.

Data Factory and machine learning data can be integrated, allowing for model training and retraining on pipelines' data and publishing it as a web service.

This integration enables seamless flow of data from pipelines to machine learning models, making it easier to develop and deploy predictive models.

We can train and retrain models on machine learning data from pipelines, which is a game-changer for many industries.

With this integration, the possibilities for data-driven decision-making become endless.

Analytics is a powerful tool that simplifies storing and processing big data. It can be used to gain insights and make informed decisions.

Azure Data Lake Analytics is an on-demand analytics job service that allows you to store and process big data. You can use the REST-linked Service to set up authentication and rate-limiting settings.

Handling errors or timeouts is crucial in analytics, and you can configure a Retry Policy in the pipeline to address any issues during the process. Azure Functions or Azure Logic Apps can also be used to address any issues.

In summary, analytics is a vital component of machine learning and data processing, and Azure Data Lake Analytics is a key service that simplifies the process.

Azure SQL Database is an always up-to-date, fully managed relational database service built for the cloud for storing data.

You can integrate it with Data Factory to design data pipelines that read and write to SQL DB.

Azure SQL Database is part of the Azure SQL family, making it a reliable and scalable choice for data storage.

SQL Server Instances are a great way to manage your databases in the cloud. You can host SQL Server instances on Azure, which offers an intelligent, scalable cloud database service.

Azure SQL Managed Instance is a fully managed and evergreen platform as a service that combines broad SQL Server instance or SQL Server database engine compatibility.

This means you can have all the benefits of a cloud-based database without having to worry about the technical details.

A SQL database is a type of relational database service built for the cloud.

It's an always up-to-date and fully managed database, meaning you don't have to worry about maintenance or updates.

Azure SQL Database is part of the Azure SQL family, and it's designed for storing data.

We can easily design data pipelines to read and write to SQL DB using the Azure data factory.

This makes it a convenient option for managing and processing large amounts of data.

Azure SQL Database is a cloud-based solution, which means it's scalable and can grow with your needs.

It's also a fully managed service, so you don't have to worry about the underlying infrastructure.