Azure Data Engineer Questions – Analytics | Azure Data Engineering Interview Questions – Storage | Azure Data Factory Interview Questions – ADF | Frequently Asked Questions (FAQ) | Conclusion
In this blog, I will cover the Top 30+ Azure Data Engineer Interview Questions.
Microsoft Azure is one of the most used and fastest-growing cloud service providers. Azure is expected to grow in the future, and with more demand, more Azure professionals are required. Talking about professionals, data engineers have been the most demanding job role in the IT sector. Most learners are already preparing themselves to become skilled data engineers, and for those, we are here to cover some most asked topics in Azure Data Engineering Interview Questions.
Interview Questions for Azure Data Engineer – General
1) What is Microsoft Azure?
Microsoft Azure is a cloud computing platform that provides both hardware and software. The service provider creates a managed service here to enable users to access these services on demand.
2) What is the primary ETL service in Azure?
3) What are data masking features available in Azure?
Dynamic data masking plays various significant roles in data security. It restricts sensitive information to some specific set of users.
- It is available for Azure SQL Database, Azure SQL Managed Instance and Azure Synapse Analytics.
- It can be implemented as a security policy on all the SQL Databases across an Azure subscription.
- Users can control the level of masking as per their requirements.
- It only masks the query results for specific column values on which the data masking has been applied. It does not affect the actual stored data in the database.
4) What is Polybase?
Polybase optimises the data ingestion into PDW and supports T-SQL. It enables developers to query external data transparently from supported data stores, irrespective of the storage architecture of the external data store.
Polybase can be used to:
- Query data stored in Hadoop, Azure Blob Storage or Azure Data Lake Store from Azure SQL Database or Azure Synapse Analytics. It eliminates the need to import data from an external source.
- Import data from Hadoop, Azure Blob Storage, or Azure Data Lake Store without a need to install a third-party ETL tool by only using a few simple T-SQL queries.
- Export data to Hadoop, Azure Blob Storage, or Azure Data Lake Store. It supports the export and archiving of data to external data stores.
5) What is reserved capacity in Azure?
Microsoft provides an option of reserved capacity on Azure storage to optimise the Azure Storage costs. For the reservation period on Azure cloud, the reserved storage provides a fixed amount of capacity to customers. It is available for Block Blobs and Azure Data Lake to Store Gen 2 data in a standard storage account.
Interview Questions for Azure Data Engineer – Analytics
This section covers azure data engineer interview questions and answers on Azure Synapse Analytics and Azure Stream Analytics.
6) Which service would you use to create Data Warehouse in Azure?
Azure Synapse is a limitless analytics service that brings together Big Data analytics and enterprise data warehousing. It gives users the freedom to query data on individual terms for using either serverless on-demand or provisioned resources at scale.
7) Explain the architecture of Azure Synapse Analytics
It is designed to process massive amounts of data with hundreds of millions of rows in a table. Azure Synapse Analytics processes complex queries and returns the query results within seconds, even with massive data, because Synapse SQL runs on a Massively Parallel Processing (MPP) architecture that distributes data processing across multiple nodes.
Applications connect to a control node that acts as a point of entry to the Synapse Analytics MPP engine. On receiving the Synapse SQL query, the control node breaks it down into MPP optimised format. Further, the individual operations are forwarded to the compute nodes that can perform the operations in parallel, resulting in much better query performance.
Read More: Azure Synapse vs Apache Spark
8) Difference between ADLS and Azure Synapse Analytics
Both Azure Data Lake Storage Gen2 and Azure Synapse Analytics are highly scalable and can ingest and process vast amounts of data (on a Peta Byte scale). But there are some differences:
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9) What are Dedicated SQL Pools?
Dedicated SQL Pool is a collection of features that enable the implementation of the more traditional Enterprise Data Warehousing platform using Azure Synapse Analytics. The resources are measured in Data Warehousing Units (DWU) that are provisioned using Synapse SQL. A dedicated SQL pool uses columnar storage and relational tables to store data, improving query performance and reducing the required amount of storage.
Read More: Dedicated SQL Pool
10) How do you capture streaming data in Azure?
Azure provides a dedicated analytics service called Azure Stream Analytics that provides a simple SQL-based language that is Stream Analytics Query Language. It allows developers to extend the ability of query language by defining additional ML (Machine Learning) functions. Azure Stream Analytics can process a huge amount of data on a scale of over a million events per second and also deliver the results with ultra-low latency.
11) What are the various windowing functions in Azure Stream Analytics?
A window in Azure Stream Analytics refers to a block of time-stamped event data that enables users to perform various statistical operations on the event data.
Four types of windowing functions are available to partition and analyse a window in Azure Stream Analytics:
- Tumbling Window: The data stream is segmented into distinct fixed-length time segments in the tumbling window function.
- Hopping Window: In hopping windows, the data segments can overlap.
- Sliding Window: Unlike, Tumbling and Hopping window, aggregation occurs every time a new event occurs.
- Session Window: There is no fixed window size and has three parameters: timeout, max duration and partitioning key. The purpose of this window is to eliminate quiet periods in the data stream.
Azure Data Engineering Interview Questions – Storage
This section covers azure data engineer interview questions and answers related to database and storage.
12) What are the different types of storage in Azure?
There are five types of storage in Azure:
- Azure Blobs: Blob stands for a large binary object. It can support all kinds of files including, text files, videos, images, documents, binary data etc.
- Azure Queues: Azure Queues is a cloud-based messaging store for establishing and brokering communication between various applications and components.
- Azure Files: It is an organised way of storing data in the cloud. Azure Files has one main advantage over Azure Blobs, it allows organising the data in a folder structure, and it is SMB compliant, i.e. it can be used as a file share.
- Azure Disks: It is used as a storage solution for Azure VMs (Virtual Machines).
- Azure Tables: A NoSQL storage solution for storing structured data which does not meet the standard relational database schema.
13) Explore Azure storage explorer and its uses
It is a versatile standalone application available for Windows, Mac OS and Linux to manage Azure Storage from any platform. Azure Storage can be downloaded from Microsoft.
It provides access to multiple Azure data stores such as ADLS Gen2, Cosmos DB, Blobs, Queues, Tables, etc., with an easy to navigate GUI.
One of the key features of Azure Storage Explorer is that it allows users to work even when they are disconnected from the Azure cloud service by attaching local emulators.
14) What is Azure Databricks, and how is it different from regular data bricks?
It is the Azure implementation of Apache Spark that is an open-source big data processing platform. In the data lifecycle, Azure Databricks lies in the data preparation or processing stage. First of all, data is ingested in Azure using Data Factory and stored in permanent storage (such as ADLS Gen2 or Blob Storage). Further, data is processed using Machine Learning (ML) in Databricks and then extracted insights are loaded into the Analysis Services in Azure like Azure Synapse Analytics or Cosmos DB.
Finally, insights are visualised and presented to the end-users with the help of Analytical reporting tools like Power BI.
15) What is Azure table storage?
It is a storage service optimised for storing structured data. In structured data, table entities are the basic units of data equivalent to rows in a relational database table. Each entity represents a key-value pair, and the properties for table entities are as follows:
- PartitionKey: It stores the key of the partition to which the table entity belongs.
- RowKey: It identifies the entity uniquely within the partition.
- TimeStamp: It stores the last modified date/time value for the table entity.
16) What is serverless database computing in Azure?
In a typical computing scenario, the program code resides either on the server or the client-side. But Serverless computing follows the stateless code nature, i.e. the code does not require any infrastructure.
Users have to pay for the compute resources used by the code during a short period while executing the code. It is very cost-effective, and users only need to pay for the resources used.
17) What Data security options are available in Azure SQL DB?
The data security options available in Azure SQL DB are:
- Azure SQL Firewall Rules: Azure provides two levels of security. The first is server-level firewall rules that are stored in the SQL Master database and determine the access to the Azure database server. The second is database-level firewall rules that govern access to the individual databases.
- Azure SQL Always Encrypted: It is designed to protect sensitive data such as credit card numbers stored in the Azure SQL database.
- Azure SQL Transparent Data Encryption (TDE): The technology used to encrypt stored data in the Azure SQL Database. The encryption/decryption of database and backups/transactions of log files happens in real-time using TDE.
- Azure SQL Database Auditing: Azure provides auditing capabilities within the SQL Database service. It allows defining the audit policy at the database server or individual database level.
18) What is data redundancy in Azure?
Azure constantly retains several copies of data to provide high levels of data availability. Some data redundancy solutions are accessible to clients in Azure, depending on the criticality and duration necessary to provide access to the replica.
- Locally Redundant Storage (LRS): In this type, data is replicated across different racks in the same data centre. It is the cheapest redundancy option and ensures that there are at least three copies of the data.
- Zone Redundant Storage (ZRS): It ensures that data is replicated across three zones within the primary region. Azure takes care of DNS repointing automatically in case of zone failure. It might require a few changes to the network settings for any applications accessing data after the DNS repointing.
- Geo-Redundant Storage (GRS): In this type, data is replicated across two regions and ensures that data can be recovered if one entire region goes down. It may take some time for the Geo failover to complete and make data accessible in the secondary region.
- Read Access Geo Redundant Storage (RA-GRS): It is much similar to GRS but with the added option of reading access to the data in the secondary region in case of a failure in the primary region.
19) What are some ways to ingest data from on-premise storage to Azure?
While choosing a data transfer solution, the main factors to consider are:
- Data Size
- Data Transfer Frequency (One-time or Periodic)
- Network Bandwidth
Based on the above factors, data movement solutions can be:
- Offline transfer: It is used for one-time bulk data transfer. Thus, Microsoft can provide customers with disks or secure storage devices, or even customers can also ship their disks to Microsoft. The offline options for transfer are named data box, data box disk, data box heavy and import/export (customer’s own disks).
- Network transfer: Over a network connection, data transfer can be performed in the following ways:
- Graphical Interface: This is ideal while transferring a few files and when there is no need to automate the data transfer. Graphical interface options include Azure Storage Explorer and Azure Portal.
- Programmatic Transfer: Some available scriptable data transfer tools are AzCopy, Azure PowerShell, Azure CLI. Various programming language SDKs are also available.
- On-premises devices: A physical device called Data Box Edge and a virtual Data Box Gateway are installed at the customer’s premises, optimising the data transfer to Azure.
- Managed Data Factory pipeline: Azure Data Factory pipelines can move, transform and automate regular data transfers from on-prem data stores to Azure.
20) What is the best way to migrate data from an on-premise database to Azure?
To move data from existing on-premises SQL Server to Azure SQL database, Azure provides the following options:
- SQL Server Stretch Database: It moves some of the data from SQL Server 2016 to Azure. It can identify the cold rows that are accessed infrequently by users to move them to the cloud. This results in quicker backups for the on-premises database.
- Azure SQL Database: It is suitable for organisations that want to go ahead with a cloud-only strategy to move the whole database to Azure.
- SQL Server Managed Instance: It support configurations support on Azure Database as a Service (DBaaS). Microsoft takes care of the Database maintenance, and the database is almost 100% compatible with on-premises SQL Server.
- SQL Server on a Virtual Machine: It is a suitable option for a customer who wants complete control over database management and maintenance. It ensures 100% compatibility with the existing on-premises instance.
- Also, Microsoft provides a tool called Data Migration Assistant that can help customers identify suitable options based on the customers’ existing on-premises SQL Server setup.
21) What are multi-model databases?
Azure Cosmos DB is Microsoft’s premier NoSQL service offering on Azure. It is the first globally distributed, multi-model database offered on the cloud by any vendor. It is used to store data in various data storage models such as Key-value pair, document-based, graph-based, column-family based, etc. Low latency, consistency, global distribution and automatic indexing features are the same no matter what data model the customer chooses.
Read More: Azure Cosmos DB
22) What is the Azure Cosmos DB synthetic partition key?
It is crucial to select a good partition key that can distribute the data evenly across multiple partitions. We can create a Synthetic partition key when there is no right column with properly distributed values. The three ways to create a synthetic partition key are:
- Concatenate Properties: Combine multiple property values to form a synthetic partition key.
- Random Suffix: A random number is added to the end of the partition key value.
- Pre-calculated Suffix: A pre-calculate numbed is added to the end of the partition value to improve the read performance
23) What are various consistency models available in Cosmos DB?
Consistency models or consistency levels provide developers with a way to choose between better performance and high availability.
The consistency models available in Cosmos DB are:
- Strong: It fetches the most recent version of the data every time a read operation occurs. The cost of reading operation is higher compared to other consistency models in this model.
- Bounded Staleness: It allows setting a time lag between the write and the read operation. It is suitable for scenarios with equal priority for availability and consistency.
- Session: It is the default and most popular consistency level in Cosmos DB based on regions. A user will see the latest data after accessing the same region where the write was performed. Among all consistency levels, it offers the lowest latency reads and writes.
- Consistent Prefix: It guarantees that users do not see out-of-order writes, but there is no time-bound data replication across regions.
- Eventual: It does not guarantee any time-bound or version bound replication. It has the lowest read latency and level of consistency.
24) How is data security implemented in ADLS Gen2?
ADLS Gen2 has a multi-layered security model. The data security layers of ADLS Gen2 are:
- Authentication: It provides user account security with three authentication modes, Azure Active Directory (AAD), Shared Key and Shared Access Token (SAS).
- Access Control: It restricts access to the individual containers or files using Roles and Access Control Lists (ACLs).
- Network Isolation: It enables admins to enable or disable access to specific Virtual Private Networks (VPNs) or IP Addresses.
- Data Protection: Encrypts in-transit data using HTTPS.
- Advanced Threat Protection: It allows monitoring the unauthorised attempts to access or exploit the storage account.
- Auditing: It is the final layer of security in which ADLS Gen2 provides comprehensive auditing features where all account management activity is logged.
Azure Data Engineering Interview Questions – Azure Data Factory
This section covers Azure Data Engineer Interview Questions related to Azure Data Factory (ADF).
25) What are pipelines and activities in Azure?
The grouping of activities arranged to accomplish a task together is known as Pipelines. It allows users to manage the individual activities as a single group and provide a quick overview of the activities involved in a complex task with many steps.
ADF activities are grouped into three parts:
- Data Movement Activities – Used to ingest data into Azure or export data from Azure to external data stores.
- Data Transformation Activities – Related to data processing and extracting information from data.
- Control Activities – Specify a condition or affect the progress of the pipeline.
26) How do you manually execute the Data factory pipeline?
A pipeline can run with Manual or On-demand execution.
To execute the pipeline manually or programmatically, we can use the PowerShell command:
Invoke-AzDataFactoryV2Pipeline -DataFactory $df -PipelineName "DemoPipeline" -ParameterFile .\PipelineParameters.json
The term ‘DemoPipeline’ above is the pipeline’s name that will run, and the ‘ParameterFile’ specifies the path of a JSON file with the source and sink path.
Also, the format of the JSON file is to be passed as a parameter to the above PowerShell command is:
{
"sourceBlobContainer": "MySourceFolder",
"sinkBlobContainer": "MySinkFolder"
}
27) Azure Data Factory: Control Flow vs Data Flow
- Control Flow is an activity that affects the path of execution of the Data Factory pipeline. For example, an activity that creates a loop if conditions are met.
- Data Flow Transformations are used when we need to transform the input data, for example, Join or Conditional Split.
Some differences between Control Flow activities and Data Flow Transformations are:
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28) Name the data flow partitioning schemes in Azure
Partitioning Scheme is a way to optimise the performance of Data Flow. This partitioning scheme setting can be accessed on the Optimize tab of the configuration panel for the Data Flow Activity.
- ‘Use current partitioning’ is the default setting recommended by Microsoft in most cases that uses native partitioning schemes.
- The ‘Single Partition’ option is used when users want to output to a single destination, for example, a single file in ADLS Gen2.
Some partition schemes are:
- Round Robin: Simple Partition scheme that spreads data evenly across partitions
- Hash: Hash of columns used to create uniform partitions (similar values in a partition)
- Dynamic Range: Spark dynamic range based on given columns or expressions
- Fixed Range: Partition for fix range of values based on user-provided expressions
- Key: Partition for each unique value
29) What is the trigger execution in Azure Data Factory?
In Azure Data Factory, pipelines can be triggered or automated.
Some ways to automate or trigger the execution of Azure Data Factory Pipelines are:
- Schedule Trigger: It invokes a pipeline execution at a fixed time or on a fixed schedule such as weekly, monthly etc.
- Tumbling Window Trigger: It executes Azure Data Factory Pipeline at fixed periodic time intervals without overlap from a specified start time.
- Event-Based Trigger: It executes an Azure Data Factory Pipeline based on the occurrence of some event, such as the arrival or deletion of a new file in Azure Blob Storage.
30) What are mapping Dataflows?
Microsoft provides Mapping Data Flows that do not require writing code for a more straightforward data integration experience than Data Factory Pipelines. It is a visual way to design data transformation flows. The data flow becomes Azure Data Factory (ADF) activities and gets executed as a part of the ADF pipelines.
Read More: Top 25 Azure Data Factory Questions
Frequently Asked Questions (FAQs)
31) What is the role of Azure Data Engineer?
Azure Data Engineers are responsible for integrating, transforming, operating, and consolidating data from structured or unstructured data systems. They also build, implement and support Business Intelligence solutions by applying knowledge of technologies, methodologies, processes, tools and applications. In short, they handle all the data operations stored in the cloud, such as Azure.
32) What skills are required for Azure Data Engineer?
Various skills are required in Azure Data Engineer like Database system management (SQL or Non-SQL), Data warehousing, ETL (Extract, Transform and Load) tools, Machine Learning, programming language basics (Python/Java), working with APIs and more.
33) How you can become Azure Data Engineer?
Anyone can become Azure Data Engineer by learning the data engineering skills from the right source and getting certified from Microsoft. You can start your journey to become Azure Data Engineer by joining our DP-203 course: FREE Class.
34) What is the salary of an Azure Data Engineer?
The average annual salary of an Azure Data Engineer in:
- US region (in USD) is 100,000 and can range between 71,000 to 200,000.
- India (in INR) is 8,00,000 and can range between 4,00,000 t0 14,00,000.
Conclusion
Azure is the most used cloud platform, and companies always look for skilled employees. To help you secure a job, we put some effort and listed some preferred topics in Azure Data Engineer Interview Questions.
If you are a beginner and interested in looking for the right learning source, you can join our Free Class below.
Related/References
- Microsoft Certified Azure Data Engineer Associate | DP 203 | Step By Step Activity Guides (Hands-On Labs)
- Introduction to Big Data and Big Data Architectures
- Batch Processing Vs Stream Processing: All you Need To Know
- Azure Data Lake For Beginners: All you Need To Know
- Azure Free Account: Steps to Register for Free Trial Account
- Azure Fundamental Certification For Beginners: AZ-900 vs AI-900 vs DP-900
- Introduction To Azure Databricks Delta Lake Architecture
- Microsoft Azure Administrator Interview Questions and Answers
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