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If you want to become a data engineer, you should prepare for the interview process. To assist you, we’ve compiled a list of the most important interview questions for this position. To help you get a head start on your preparation, I’ve compiled a list of the Top 30+ Azure Data Engineer Interview Questions.
Microsoft Azure is one of the most popular and rapidly expanding cloud service providers. Azure is expected to grow in the future, necessitating the hiring of more Azure professionals. When it comes to professionals, data engineers are the most in-demand in the IT industry. Most students are already preparing to become skilled data engineers, and we are here to cover some of the most frequently asked topics in Azure Data Engineering Interview Questions.
Interview Questions for Azure Data Engineer – General
Microsoft Azure is a cloud computing platform that includes hardware as well as software. In this case, the service provider creates a managed service that allows users to access these services on demand.
|Multi-cloud and on-prem hybrid copy data
|Design code-free data pipelines
|Code-free data transform-ations that execute in Spark
|Build and maintain operational schedules for your data pipelines
|View active executions and pipeline history
|90+ native connectors
|Generate pipeline via SDK
|Scale-out with Azure Integration Runtimes
|Wall clock, event-based tumbling, windows, chained
|Detail activity and data flow executions
|Serverless and auto-scale
|Utilize workflow construct loops,branches, conditional execution, variables, parameters…
|Generate data flows via SDK
|Establish alerts and notifications
|Use wizard for quick copy jobs
|Designers for data engineers and data analytics
Dynamic data masking serves several important functions in data security. It restricts sensitive information to a small group of users.
Data entry into PDW is optimized by Polybase, which also supports T-SQL. It allows developers to query external data from supported data stores transparently, regardless of the storage architecture of the external data store.
One can use polybase:
To reduce the cost of Azure Storage, Microsoft offers the option of reserved capacity. The reserved storage on Azure cloud offers customers a set amount of capacity during the reservation period. Gen 2 data can be stored in a standard storage account for Block Blobs and Azure Data Lake.
Azure Synapse Interview Questions – Analytics
The interview questions and responses for azure data engineers for synapse analytics and stream analytics are covered in this section.
Azure Synapse is a boundless analytics service that combines enterprise data warehousing and Big Data analytics. Users are given the choice to query data on specific terms for using either serverless on-demand or scale-out provisioned resources.
It is intended to process enormous amounts of data, including tables with hundreds of millions of rows. Due to Synapse SQL’s Massively Parallel Processing (MPP) architecture, which distributes data processing across multiple nodes, complex queries are processed by Azure Synapse Analytics, which returns the query results in a matter of seconds even when there is a large amount of data.
Applications communicate with a control node that serves as the gateway to the Synapse Analytics MPP engine. The control node converts the Synapse SQL query into MPP-optimized format after receiving it. Additionally, the individual operations are sent to the compute nodes so they can be completed in parallel, greatly improving query performance.
Fig: Image by Microsoft
Highly scalable and capable of ingesting and processing enormous amounts of data, Azure Data Lake Storage Gen2 and Azure Synapse Analytics are both available (on a Peta Byte scale). However, there are some distinctions.
|Azure Synapse Analytics
|Enhanced for the storage and processing of both structured and unstructured data
|A well-defined schema that is optimized for processing structured data
|Used by data scientists and engineers for data exploration and analytics
|utilized for business analytics or distributing data to users in the business world
|Built to function with Hadoop
|Powered by SQL Server
|There is no adherence to regulations
|Adhering to legal requirements like HIPAA
|For data access, USQL (a C# and TSQL hybrid) and Hadoop are used.
|For data access, Synapse SQL, an enhanced version of TSQL, is used.
|Able to manage streaming data using tools like Azure Stream Analytics
|Data streaming capabilities and built-in data pipelines
Fig: From Microsoft
A group of features known as Dedicated SQL Pool make it possible to use Azure Synapse Analytics to implement the more conventional Enterprise Data Warehousing platform. Data Warehousing Units (DWUs), which are provisioned using Synapse SQL, are used to measure the resources. A dedicated SQL pool stores data using relational tables and columnar storage, which enhances query performance and lowers the necessary amount of storage.
10) How do you capture streaming data in Azure?
Azure offers a specialized analytics service called Azure Stream Analytics, which offers the straightforward SQL-based Stream Analytics Query Language. By defining additional ML (Machine Learning) functions, it enables developers to expand the capabilities of the query language. Over a million events per second can be processed by Azure Stream Analytics, and the results can be delivered with extremely low latency.
A block of time-stamped event data known as a window in Azure Stream Analytics allows users to run different statistical operations on the event data.
To divide and analyse a window in Azure Stream Analytics, there are four different types of windowing functions available:
Azure Data Engineering Interview Questions – Storage
This section includes azure data engineer interview questions and solutions pertaining to databases and storage.
Azure offers five different types of storage:
It is a flexible standalone application that can manage Azure Storage from any platform and is available for Windows, Mac OS, and Linux. Microsoft offers a download for Azure Storage.
It offers simple GUI access to a variety of Azure data stores, including Blobs, Queues, Tables, ADLS Gen2, Cosmos DB, and more.
By attaching local emulators, one of the key features of Azure Storage Explorer is that it enables users to continue working even when they are not connected to the Azure cloud service.
An open-source big data processing platform is Apache Spark in its Azure version. Azure Databricks is a component of the data preparation or processing phase of the data lifecycle. Data is initially imported into Azure through Data Factory and kept in permanent storage (such as ADLS Gen2 or Blob Storage). Additionally, Databricks processes data using Machine Learning (ML), and the insights that are gleaned are then loaded into Azure Analysis Services like Azure Synapse Analytics or Cosmos DB.
Finally, with the aid of analytical reporting tools like Power BI, insights are visualised and presented to the end users.
It’s a storage service that is designed to store structured data efficiently. The basic units of structured data, which correspond to the rows in a relational database table, are called table entities. Table entities each represent a key-value pair and have the following characteristics:
Program code typically resides either on the client-side or the server in a computing environment. However, serverless computing adheres to the stateless nature of code, which means that the code does not need any infrastructure.
Users are required to pay for the compute resources that the code uses while being executed for a brief time. Users only have to pay for the resources they actually use, which makes it extremely cost-effective.
The following are the data security choices offered by Azure SQL DB:
To ensure high levels of data availability, Azure continuously keeps multiple copies of the data. According to the urgency and time required to grant access to the replica, some data redundancy solutions are available to clients in Azure.
Data is replicated across various racks in the same data centre with locally redundant storage (LRS). It ensures that there are at least three copies of the data and is the least expensive redundancy option.
Data replication across three zones within the main region is ensured by zone redundant storage (ZRS). When a zone fails, Azure handles DNS repointing automatically. Any applications that access data after the DNS repointing may need to make a few adjustments to the network settings.
Geo-Redundant Storage (GRS): This type ensures that data can be recovered even if one entire region goes down by replicating data across two regions. The completion of the Geo failover and the availability of data in the secondary region may take some time.
RA-GRS: Read Access Geo Redundant Storage It is very comparable to GRS but adds the capability of reading access to the data in the secondary region in the event of a primary region failure.
The following are the main things to think about when selecting a data transfer solution:
The following data movement solutions are possible based on the aforementioned variables:
Offline data transfer: This is done in bulk once. Thus, Microsoft can offer discs or secure storage devices to customers, or customers can send Microsoft their own discs. Data Box, Data Box Disk, Data Box Heavy, and Import/Export (Customer’s Own Disks) are the offline transfer options.
Network transfer: The following methods for performing data transfer over a network connection:
When only a few files need to be transferred and no automation is required, a graphical interface is the best option. Azure Storage Explorer and Azure Portal are graphical interface choices.
Programmatic Transfer: AzCopy, Azure PowerShell, and Azure CLI are a few scriptable data transfer tools that are readily available. There are also many different SDKs for programming languages.
On-site equipment At the customer’s location, a physical device called Data Box Edge and a virtual Data Box Gateway are installed to optimize the data transfer to Azure.
Managed Data Factory pipeline: Azure Data Factory pipelines are able to automate routine data transfers from on-premises data stores to Azure and move, transform, and move data.
Azure offers the following options for data migration from an existing on-premises SQL Server to an Azure SQL database:
Data is transferred from SQL Server 2016 to Azure using the SQL Server Stretch Database. In order to move those rows to the cloud, it can identify the cold rows that users access infrequently. As a result, the on-premises database’s backups are completed more quickly.
Azure SQL Database: It is appropriate for businesses that want to move their entire database to Azure as part of a cloud-only strategy.
Azure Database as a Service configurations are supported by SQL Server Managed Instance (DBaaS). The database’s upkeep is handled by Microsoft, and it is almost entirely compatible with SQL Server installed on-site.
On a virtual machine, SQL Server: It is an appropriate choice for a customer who desires total control over database management and upkeep. It makes sure that the current on-premises instance is completely compatible.
Additionally, Microsoft offers a tool called Data Migration Assistant that can assist users in finding appropriate options based on their current on-premises SQL Server configuration.
Microsoft’s top NoSQL service on Azure is Azure Cosmos DB. It is the first globally distributed, multi-model database that any vendor is making available in the cloud. It is employed to store data in a number of different data storage models, including key-value pair, document-based, graph-based, column-family-based, etc. Regardless of the data model the customer chooses, features like low latency, consistency, global distribution, and automatic indexing remain the same.
It is essential to choose a solid partition key that can evenly distribute the data across several partitions. When there is no right column with evenly distributed values, we can create a synthetic partition key. The three methods for producing a fake partition key are as follows:
Concatenate Properties: Concatenate several property values to create a fake partition key.
Random Suffix: The partition key value is finished off with a random number.
To enhance the read performance, a pre-calculated suffix is added to the end of the partition value.
Developers have a choice between better performance and high availability thanks to consistency models or consistency levels.
The following consistency models are offered by Cosmos DB:
A multi-layered security model is used by ADLS Gen2. The ADLS Gen2 data security layers are as follows:
The last layer of security is auditing, and ADLS Gen2 offers thorough auditing features that log all account management activity.
The interview questions for Azure Data Engineer for Azure Data Factory are covered in this section (ADF).
Pipelines are the arrangement of activities designed to complete a task simultaneously. Users can manage individual tasks as a single group with its help, and it offers a quick overview of all the steps in a multi-step, complex task.
ADF operations are divided into three categories:
A pipeline may be executed manually or on demand.
We can use the PowerShell command to manually or automatically run the pipeline:
Invoke-AzDataFactoryV2Pipeline -DataFactory $df -PipelineName
“DemoPipeline” -ParameterFile .PipelineParameters.json
The pipeline’s running name is “DemoPipeline,” and the “ParameterFile” specifies the location of a JSON file containing the source and sink paths.
In addition, the following JSON file format must be supplied as a parameter to the PowerShell command mentioned above:
An activity called Control Flow has an impact on the Data Factory pipeline’s execution path. For instance, a process that starts a loop if certain criteria are met
When we need to transform the input data, such as with a join or conditional split, we use data flow transformations.
The following are some distinctions between Data Flow Transformations and Control Flow Activities:
|Control Flow Activity
|Data Flow Transformation
|Has an impact on the pipeline’s path or order of execution.
|Reworks the data that was ingested
|can be recursive
|Neither a sink nor a source
|You need a source and a sink.
|Pipeline level implementation
|At the activity level, implemented
A partitioning scheme can improve the efficiency of data flow. The Optimize tab of the configuration panel for the Data Flow Activity contains a link to the partitioning scheme setting.
In most situations where native partitioning schemes are used, Microsoft recommends using the default setting of “Use current partitioning.”
When users want to output to a single destination, such as a single file in ADLS Gen2, they use the “Single Partition” option.
Several partitioning plans include:
Pipelines in Azure Data Factory can be automated or triggered.
The following are some techniques for automating or starting Azure Data Factory Pipelines:
For a simpler data integration experience than Data Factory Pipelines, Microsoft offers Mapping Data Flows that doesn’t require writing any code. It is a method of creating data transformation flows visually. The data flow is transformed into Azure Data Factory (ADF) tasks and carried out as a component of ADF pipelines.
The most popular cloud platform is Azure, and businesses are constantly looking for qualified personnel. We worked hard to compile a list of popular subjects for Azure Data Engineer Interview Questions in order to aid you in landing a job.
If you want to get trained in Azure data engineer certification, then check out the Azure Data Engineer Certification Training by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe.
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