Azure Databricks Interview Questions and Answers for 2024 Cloud Computing

A public cloud is a service platform that provides you with all IT infrastructure over the Internet. It is managed by a third party known as a cloud service provider. They offer cloud services to the public at a nominal charge. It is a platform shared by numerous cloud users. On the public cloud, you only pay for the services you use. Popular public cloud providers include Amazon Web Services, Google Cloud Platform, and Microsoft Azure.

Though there are multiple benefits that cloud computing offers, some prominent ones include 

• High Accessibility: Cloud services can be accessed at anytime from anywhere. 
• Low maintenance cost: You do not have to pay for setting up and maintaining the cloud infrastructure. 
• Per as you go: You only pay for the services that are actually used by you. 
• High scalability: Cloud services are highly scalable so you can use them for all kinds of workloads. They can scale up or down as per the volume of the workload. 
• Data security: Cloud platforms employ better security controls to secure data stored on their platforms. 
• Limitless storage: With the cloud, you get unlimited storage for storing data. 

Cloud service providers offer mainly three types of service models to cater to customer needs: 

• IaaS: With IaaS, you can provision the resources you want without buying and maintaining them. Examples of IaaS include AWS, Azure, and GCE.  
• PaaS: With PaaS, you get a platform where you can build applications. Examples of PaaS include Windows Azure and Azure Databricks. 
• SaaS: With SaaS, you get the software as a service and you just pay to use it.  Examples of SaaS include Skype and Slack. 

Azure is a cloud computing platform owned & managed by Microsoft. It is one of the leading cloud platforms in the market. It has numerous data centers spread across the world to support cloud operations. After AWS, it is the second most preferred platform by industry professionals. 

As a cloud service provider, Azure offers a host of services such as computing, storage, networking, monitoring, analytics, and troubleshooting services. You can create IT infrastructure for your enterprise within a short time using these Azure services. It is a convenient alternative for huge, complex on-premises IT infrastructure, which requires capital, maintenance, and labor expenses

Azure Databricks is a popular cloud-based data analytics service offered by Microsoft Azure. It allows you to perform data analytics on huge amounts of data on Azure. Azure Databricks is a result of the close collaboration between Databricks and Microsoft Azure to help data professionals handle a large amount of data conveniently using the cloud.

Built on top of Apache Spark, Azure Databricks exploits the flexibility of cloud computing along with Apache Spark's data analytics features to deliver the best AI-powered solutions. Because Azure Databricks is a part of Azure, it can easily integrate with other Azure services (e.g., Azure ML). This is the reason why it is getting popular among Data Engineers for processing and transforming large amounts of data.

SLA in this context refers to the service level agreement that is done between Azure and a cloud customer. It includes the service availability and acceptable downtime for customer reference. Azure provides a guaranteed SLA of 99.95% to Azure Databricks users. This means that the Azure Databricks service can be down for up to 4.38 hours in a year, not more than that.

Azure Databricks is a widely used data engineering platform that helps process and transform data to create powerful solutions. Though there are various advantages of using Azure Databricks, some of these include 

1. Powerful: As Azure Databricks is a cloud-native solution, it can process huge amounts of data. 
2. Cost-effective: You can save up to 80% of expenses associated with cloud computing by using managed clusters of Azure Databricks. 
3. Seamless integration: Azure Databricks helps you to seamlessly integrate with numerous open-source libraries and offers the latest version of Apache Spark. 
4. Enhanced productivity: Azure Databricks has shared workspaces and common languages that help in boosting productivity. 
5. AD for authentication: You get Active Directory integrated with Azure Databricks which is useful in the authentication. 
6. Supports multiple languages: Azure Databricks supports multiple languages such as Scala, R, Python, and SQL. 
7. Secure: Azure Databricks employs multiple security layers that protect your data, such as encryption and role-based access control (RBAC). 

A continuous integration/continuous delivery (CI/CD) pipeline automates the entire process of software application delivery. Besides automation, it introduces continuous monitoring throughout the software lifecycle. A CI/CD pipeline involves various operations like building code, running tests, and deploying software automatically without much hassle. Thus, it minimizes manual errors, offers feedback to developers, and provides quick product iterations.

There are four main stages in a CI/CD pipeline: 

• Source: At this stage, changes made to the code trigger a CI/CD pipeline. 
• Build: At this stage, a build package of the software is created by merging the source code and its dependencies. 
• Test: The build is tested for its performance via various automated tests. 
• Deploy: After successful testing, the software product is deployed into production where it is live. 

Azure Databricks supports multiple programming languages, including Python, R, Java, and Scala. These programming languages are compatible with the Apache Spark framework. Programmers who know any of these languages can work with Azure Databricks. Besides these languages, it also supports the Standard SQL database language and language APIs such as Spark SQL, PySpark, SparkR, SparklE, Spark.api.java, and Spark.

When talking about Azure Databricks, the management plane refers to all the means that help us manage Databricks deployments. This means all the tools by which we can control deployments. We have the Azure portal, Azure CLI, and Databricks REST API as part of the management plan. Without the management plane, data engineers cannot run and manage Databricks deployments smoothly. This makes it a crucial part of Azure Databricks.

Azure Databricks supports two different pricing tiers: 

• Standard Tier: This tier offers basic data management features. 
• Premium Tier: This tier offers some extra features apart from the ones offered in Standard Tier. 

Each tier has multiple features and capabilities that cater to different data requirements. You can choose any of these two tiers based on your data requirements and affordability. Pricing differs based on the region, pricing tier opted, and pay per month/hour. Azure offers the flexibility to pay in currencies of different countries as it provides Azure Databricks services globally. 

Azure Databricks has an interesting concept of Databricks secrets that allows you to store your secrets (e.g., credentials) in notebooks or jobs and reference them later easily. You can create and manage secrets using the Databricks CLI. Secrets can be stored inside a secret scope. Once you create a secret scope, you can manage secrets easily.  

Secrets scopes are of two types: 

1. Azure Key Vault-backed scopes: You must create a secret scope backed by Azure Key Vault to reference secrets stored in it. Once created, you can then reference all secrets stored in this scope instance. You cannot use some Secrets API 2.0 operations here like PutSecret and DeleteSecret as this secret scope is a read-only interface. You can use the Azure portal UI or Azure SetSecret REST API to manage secrets in Azure Key Vault. 
2. Databricks-backed scopes: An encrypted database stores Databricks-backed secret scopes. This database is fully owned and managed by Azure Databricks. Naming a scope requires you to follow some rules, including keeping names unique, names should have dashes, alphanumeric characters, @, underscore, and periods, and must not exceed 128KB. 

Azure Databricks falls under the PaaS category of cloud services. This is because Azure Databricks provides an application development platform to run data analytics workloads. In Platform as a Service (PaaS), the customer or user is responsible for using the platform capabilities while the underlying infrastructure management lies with the cloud provider.

Users working on Azure Databricks are responsible for leveraging the platform's capabilities to design and develop their applications without worrying about the underlying infrastructure. Here, users are responsible for both data and applications they build on the platform. Azure is only responsible for providing the platform and managing its infrastructure.

Databricks runtime is the core element of Azure Databricks that enables the execution of Databricks applications. It consists of Apache Spark along with various other components and updates that help improve the performance, usability, and security of big data analytics significantly. Apache Spark forms the largest and most important component of Databricks runtime.

Databricks runtime helps you develop and run Spark applications by providing all the essential tools required to construct and run them such as application programming interfaces (APIs), libraries, and other components. Databricks runtime is managed by Azure Databricks directly so we do not need to manage it directly

One of the most frequently posed Azure Databricks interview questions, be ready for it. A Databricks unit in Azure Databricks often referred to as DBU, is a computational unit measuring the processing capability and is billed per every second of use. Azure bills you for every virtual machine and other resources (e.g., blob, managed, disk storage that you provision in Azure clusters on the basis of Databricks units (DBUs).

This unit indicates how much power your VM utilizes per second and helps Azure Databricks to bill you based on your usage. The Databricks unit consumption is directly linked to the type and size of the instance on which you run Databricks. Azure Databricks has different prices for workloads running in the Standard and Premium tiers.

Widgets play a crucial role in building notebooks and dashboards that are re-executed with more than one parameter. While building notebooks & dashboards in Azure Databricks, you cannot neglect to test the parameterization logic. You can use widgets to add parameters to dashboards and notebooks and even test them.

Apart from being used for building dashboards and notebooks, they help in exploring results of a single query with multiple parameters. With the Databricks widget API, you can create different kinds of input widgets, get bound values, and remove input widgets. The widget API is consistent in languages like Python, R, and Scala, but differs slightly in SQL.

A DataFrame is a specified form of tables used to store data inside the Databricks runtime. It is a data structure in Azure Databricks that arranges data into 2-D tables of rows and columns for better accessibility. Because of their flexibility and easiness, DataFrames are widely used in modern data analytics.

Each DataFrame has a schema (a kind of blueprint) that specifies the data name and type of each column. DataFrames are very similar to spreadsheets. The only difference between them is that a single spreadsheet resides in one computer whereas a single DataFrame can span numerous computers. This is why DataFrames facilitates data engineers to perform analytics on Big Data using multiple computing clusters.

A cache is a temporary storage that stores frequently accessed data to reduce latency and improve speed. Caching refers to the act of storing data in cache memory. When some data is cached in cache memory, the data recovery becomes faster when the same data is accessed again. 

For example, modern browsers store website cookies to boost performance and reduce latency. Cookies are stored in the browser's cache memory and when the user accesses the same website again, it loads faster because the browser already has some of its data. Caching not only improves website loading speed but also reduces the burden on the website server.

Caching can be classified into four types:  

• Data/information caching: This caching involves storing frequently-accessed data in local memory to avoid multiple trips to the database. It boosts the data retrieval speed in databases. 
• Web caching: Web caching mainly is used in websites, proxies, and gateways to improve speed and reduce latency. 
  • Browser caching helps users quickly access websites they visit frequently.  
  • Gateway & proxy caching help users share the cached information with a group of users.  
• Application caching: It is similar to web caching but stores raw HTML data using server-level caching. It effectively reduces server overhead and website load time. 
• Distributed caching: This type of caching is used by applications that handle large volumes of data, for example, YouTube, Google, etc. Multiple machines are connected in a cluster to serve as cache memory for improving data usability and performance while reducing latency.

A common question in Azure Databricks interview questions for beginners, don't miss this one. Databricks is an open-source platform that allows data engineers, data analysts, and data scientists to run scheduled and interactive data analysis workloads on its collaborative platform. Databricks is not specifically attached to Azure or AWS. It is itself an independent data management platform.

Azure partnered with Databricks and offered its cloud platform to host Databricks services. Azure Databricks is the result of this integration and partnership. Azure Databricks is more popular than regular Databricks because of the enhanced capabilities and features offered by the Azure platform. This is due to better integration with Azure services like Azure AD and Azure ML.

An ETL tool helps in extracting, transforming, and loading data from one source to another. The Azure ETL tool empowers you to use different operations, such as parse, join, filter, and pivot rank, to transform data into Azure Synapse. Data travels from one source to another or is stored somewhere for a short term in Azure Databricks.

ETL operations mainly include data extraction, transformation, and loading (ETL). In Azure Databricks, the following ETL operations are performed on data:

• The data is transformed to the data warehouse from Databricks. 
• The data is loaded using Azure Blob Storage. 
• The data is temporarily stored in Azure Blob Storage. 

In Azure Databricks, a cluster refers to a collection of instances that run Spark applications. On the other hand, an instance is a VM on Azure Databricks that runs the Databricks runtime.

A cluster is a combination of various computational resources and configurations that help run data analytics, data engineering, and data science workloads. A cluster can run multiple instances inside it.

Databricks file system (DBFS) is an integral part of Azure Databricks that is used to store the data saved in Databricks. It is a distributed file system that is mounted into Databricks workspaces. The Databricks file system is available on Azure Databricks clusters and can store large amounts of data easily.

It provides data durability in Azure Databricks even after a Databricks node or cluster is deleted. With DBFS, you can map cloud object storage URIs to relative paths and interact with object storage using file and directory semantics. If you mount object storage to DBFS, you can access objects present in object storage as if you have them in your local file system.

In Azure Databricks, Delta lake tables refer to the tables that contain data in the delta form. You can consider delta lake as an extension to present data lakes, which allows you to configure it as per your requirements. Being one of the core components of Azure Databricks, the delta engine supports the delta lake format for data engineering. This format helps you create modern data lakehouse/lake architectures and lambda architectures.

Major benefits that data lake tables provide include data reliability, data caching, ACID transactions, and data indexing. With the delta lake format, preserving the history of data is easy. You can use popular methods like creating pools of archive tables and slowly changing dimensions to preserve the data history.

The data plane is a part of Azure Databricks that is responsible for processing and storing data. All data ingested by Azure Databricks is processed and stored in the data plane. Unlike the management plane, the data plane is not managed by us because it is auto-managed by our Azure account.

Databricks is an independent open-source data management platform that helps data engineers to run data workloads in its collaborative environment. It is not proprietary to Azure or AWS. Instead, it offers its features on these cloud platforms for better outreach.

Azure Databricks is an end product of the integration of Databricks features and Azure. Similarly, Databricks integrated its features with the AWS platform, which is referred to as AWS Databricks. Since Azure Databricks offers more functionalities, it is more popular in the market. This is because Azure Databricks can make use of Azure AD authentication and other useful Azure services to deliver better solutions. AWS Databricks just serves as a hosting platform for Databricks and has comparatively fewer functionalities than Azure Databricks.

Widgets are an important part of notebooks and dashboards. They facilitate us in adding parameters to notebooks and dashboards. You can use them to test the parameterization logic in notebooks.

Azure Databricks has four types of widgets: 

1. Text widgets: They help you input values in a text field. 
2. Dropdown widgets: They help you find a value from a list of given values. 
3. Combobox widgets: They are a combination of text and dropdown widgets that allow you to either choose a value from the list or enter one in the text field. 
4. Multiselect widgets: They help you choose one or more options from a given list of values. 

A staple in interview questions on Azure Databricks, be prepared to answer this one. Though there are many issues one may face while working with Azure Databricks, some common ones are listed below: 

• Cluster creation failures: You may often face cluster creation failures while working with Azure Databricks. This could be due to the fact that you do not have the credits required to create more clusters. Before creating a cluster, ensure that you have enough credits. This will more likely eliminate cluster creation failures. 
• Spark errors: Spark errors are quite common in Azure Databricks. You may face Spark errors due to incompatibility of your code with Databricks runtime. To avoid spark errors, make sure you do not have any compatibility issues between Databricks runtime and your code. 
• Network errors: Network errors are also common in Azure Databricks. You may face such errors when you access Azure Databricks from an unsupported location or if your network is not properly configured. 

Both data warehouses and data lakes are used to handle Big Data but are not the same.  A data warehouse refers to a storage repository for structured and filtered data that has been processed already for a certain purpose. As the data warehouse has data managed and processed locally, its structure cannot be changed easily. Mainly business professionals use data warehouses. It is costly to make changes to data warehouses and they are complicated.

A Data lake refers to a large pool of unstructured, raw data for an undefined purpose (yet to be determined). It contains all forms of data, including unstructured, old, and raw data. As data lakes have unstructured data, the data can be easily scaled up and the data structure can be modified without any problem. Data lakes have most users as data engineers. Data lakes are easily accessible and easy to update.

Azure Databricks allows you to store your confidential information (e.g., credentials) in notebooks or jobs which you can retrieve later via referencing them. A secret in Azure Databricks refers to a key-value pair that contains some secret information. This secret information has a unique key name inside a secret scope. A maximum of 1000 secrets can be stored in one secret scope, and each secret can be a maximum of 128KB.

Secret names are not case-sensitive. A secret can be referred to using a Spark configuration property or a valid variable name. You can create secrets using the CLI and REST API. Before creating a secret, you must consider which secret scope you are using. The process of creating a secret varies for different secret scopes. You can read a secret using the Secrets utility in a job or notebook.

Any Azure Databricks identity is verified using credentials. In Azure Databricks, credentials may be a username & password or personal access token. A personal access token is basically a means to authenticate an Azure Databricks entity (a user, group, or service principal) as it helps Azure Databricks verify the identity of the entity.

Creating an Azure Databricks personal access token just takes you a few steps: 

• Click your username present at the top bar in your Azure Databricks workspace. From the drop-down, choose User Settings. 
• Click Generate new token on the Access tokens tab. 
• Click Generate. 
• Once you copy the token, click Done. 

Databricks is an independent, open-source data analytics platform. It is currently optimized to run only on the two public clouds: AWS and Azure. This is because these two platforms are optimized to provide better integration and performance with Databricks features. Databricks has an official agreement and feature integration with these platforms to provide Databricks services so you get more features and optimized performance on these cloud platforms. Data management requires better integration and optimized platforms to offer quality data analytics services.

As Databricks is an open-source, free data analytics platform, you can set up and run your own Databricks cluster on-premises or on a private cloud infrastructure. However, the kind of features you get on Azure and AWS will not be available on that cluster running on the private cloud. If you require more advanced capabilities and control over data workloads, Azure Databricks is the best option followed by AWS Databricks.

This question is a regular feature in Azure Databricks interview questions for a data engineers, be ready to tackle it. Azure Databricks has various components that help in its functioning. However, some components that play a pivotal role include 

• Workspace: Developers mainly use Databricks using interactive and collaborative workspaces. A workspace is a notebook-based environment with the following features: 
  • In-built version control and integration features with Git/Github. 
  • Enterprise-level security. 
  • ML lifecycle management from development to production. 
  • Query visualizing, algorithm building, and dashboard generation. 
• Apache Spark: It is an open-source processing engine that is responsible for memory-based data processing. Spark is the basic component of Azure Databricks that handles queries and workloads on the Databricks platform. It is widely known to work well with processing large data and machine learning.  
• Managed Infrastructure: Managed clusters are a part of the managed infrastructure. A cluster is a set of VMs that fasten the delivery of results by distributing work. You can create customized clusters with popular data analytics and data science libraries to meet your specific requirements. You can auto-scale your clusters when required to meet the volume demand with just a click.  
• Delta: It is an open-source file format that was designed to solve the challenges posed by standard delta lake file formats. Delta is built using a columnar format, known as Parquet, that is used for large data applications with transaction logs and metadata. 
• ML Flow: It is an open-source machine language framework created for managing the ML lifecycle. It solves the major challenge of implementing ML in production. 
• SQL Analytics: It is a part of Azure Databricks that serves as a home within Databricks for SQL analysts. SQL Analytics is driven by SQL Endpoints. These endpoints are spark clusters specifically designed for SQL workloads. You can connect to these endpoints using BI tools and SQL Analytics UI to access data from your data lake. 

Yes, Azure Key Vault can serve as an alternative to secret scopes. Azure Key Vault is a storage that can store any confidential information. To use Azure Key Vault as an alternative, you need to set it up first.

Create a key value that you want to save in Azure Key Vault with restricted access. It is not required to update the scoped secret even if the value of the secret has to be modified in any way. Using Azure Key Vault as an alternative to secret scopes has many associated benefits, of which the most important one is getting rid of the headache of keeping track of secrets in multiple workspaces at the same time.

When an Azure Databricks user anticipates a predetermined amount of workloads they may get on Azure Databricks in advance and wants to reserve Azure storage to meet the workload requirements, they can do so with the reserved capacity option. Azure provides this option for Azure users who are keen on saving storage costs without compromising on service quality.

With this option, Azure users are assured of uninterrupted access to the amount of storage space they have already reserved. Azure provides two cost-effective storage solutions (Azure Data Lake and Block Blobs) that can store Gen2 data in a standard storage account.

Azure Databricks offers autoscaling for catering to dynamic workloads. The amount of dynamic workloads is never the same and has varying spikes in workload volumes. Autoscaling allows Azure clusters to meet variable workloads by automatically scaling up or down as per the amount of workload.

Autoscaling not only improves resource utilization but also saves the costs of resources. Resources are scaled up when the volume of the workload rises and scaled down when the volume of the workload drops. Resources are created and destroyed in Azure clusters based on the volume of workloads, so autoscaling is a smart way to manage workloads efficiently.

Formerly known as SQL data warehouse, the dedicated SQL pool refers to a standalone service running outside of an Azure Synapse workspace. Though the dedicated SQL pool runs outside Azure Synapse, it is a part of Azure Synapse Analytics. It is a set of technologies that allows you to use the platform for enterprise data warehousing. Dedicated SQL pools assist you in improving the efficiency of queries and reducing the amount of data storage required by storing data in both relational and columnar tables.

Azure Synapse is an analytics service on the Azure platform that integrates Big Data analytics and data warehousing. You can consider Azure Synapse as an evolved form of SQL data warehouse. Azure Synapse SQL (DWU) helps you provision resources in Data Warehousing Units (DWU).

Handling issues effectively while working with Azure Databricks is an essential skill to have for data engineers and data scientists. If we face any problem while performing any task on Azure Databricks, we must go through the official Azure Databricks documentation to check for possible solutions.

Azure Databricks documentation has a list of common issues one can face while working on the platform along with their solutions. With detailed step-by-step procedures and other relevant information, you can troubleshoot problems easily.

If the documentation does not help and you require more information, you can connect with the Databricks support team for further assistance on your issue. The support team has knowledgeable staff who will guide you on how to solve the problem.

A data lake in Azure Databricks refers to a pool of raw data of all types. This includes unstructured, old, and raw data collected for a purpose that is yet to be determined. Data lakes are a cheap option to store and process data efficiently. They can store data in any format of any nature.

A data lakehouse in Azure Databricks is an advanced data management architecture that integrates the features of data lakes with the ACID transactions and data management of data warehouses. It combines the flexibility and economical features of data lakes with the data management features of data warehouses to implement machine learning (ML) and business intelligence (BI) on all data.

Yes, code can be reused in the Azure notebook. To reuse code, we must first import the code into your notebook from Azure notebook. Without getting the code in your notebook, it cannot be reused. 

We can import code in two ways: 

• Code resigning in a different workstation: If the code is present on a different workstation, we should create a component (module/jar file) for the code first before we integrate it into the module/jar file. 
• Code present on the same workstation: If the code is present on the same workstation, importing is easier and simpler. We can use the code right away once we import it.

Azure Recovery Services Vault (RSV) is a storage entity on the Azure platform that stores backup data. Backup data consists of a wide range of data, including multiple copies of data and configuration information for VMs, workstations, workloads, and servers. By using Azure RSV, you can store backup data for many Azure services and organize backup data in a better way. This helps minimize backup data management overhead.

You can use Azure RSV along with other Azure services, including Windows Server, System Center DPM, and Azure Backup Server. Azure RSVs follow the Azure Resource Manager model. Azure RSV comprises Azure Backup and Azure Site Recovery.  Azure Backup replicates your data to Azure as a backup. Azure Site Recovery provides a failover solution for your server when your server is not live.

With Azure RSV, you can do the following: 

• Protect backup data: You can secure your cloud backups and restore them later when required.  
• Monitor your hybrid IT environment: You can centrally monitor your (on-prem as well as cloud) IT environment using the Azure RSV portal. 
• Cross Region Restore: You can pair your Azure region with other regions to restore your backups there. This is called cross region restore. 

In Azure Databricks, workspaces are functional instances of Apache Spark that classify objects like experiments, libraries, queries, notebooks, and dashboards into folders. They provide access to data, jobs, and clusters, thereby serving as an environment for accessing all Azure Databricks assets.

Workspaces can be managed using Databricks CLI, workspace UI, and Databricks REST API reference. The workspace UI is most commonly used to manage workspaces. There can be multiple workspaces in Azure Databricks for different projects. Every workspace has a code editor, a debugger, and Machine Learning (ML) & SQL libraries as its main components. Multiple other components exist in a workspace that performs different functions.

Data is imported from Azure Data Factory into Azure for the big data pipeline and is stored in a data lake. Azure Databricks reads data from several resources and transforms it into actionable insights.

No, Azure Databricks officially cannot be managed via PowerShell. This is because PowerShell is not compatible to work with Azure Databricks. We can use other commonly used methods like the Azure portal, the Databricks REST API, and the Azure Command Line Interface (CLI) for Azure Databricks administration. However, it supports PowerShell modules that can be used for this purpose.

Among all these methods, the Azure portal is the simplest one to use, followed by Azure CLI. Managing Azure Databricks using the Databricks Rest API is very complex and requires some level of expertise. Due to its complexity, most data engineers avoid using Databricks REST API and use the other two methods for managing Azure Databricks.

Azure Command-line Interface (CLI) is a powerful tool that helps you connect to Azure and execute administrative commands for managing Azure resources. Commands are executed using interactive scripts through a terminal.  

Azure Databricks CLI can help you perform the following tasks in Azure Databricks: 

• Provision resources: You can use Azure Databricks CLI to provision compute resources in Databricks clusters. 
• Create and run tasks: You can create and run data processing and data analysis tasks. 
• Manage notebooks: You can easily manage notebooks (list, import, and export them) and folders in a workspace. 
• Troubleshoot issues: Azure CLI helps you troubleshoot technical issues easily. 

A cluster in Azure Databricks is a collection of computation resources and configurations like streaming analytics, production ETL pipelines, machine learning, and ad-hoc analytics. An Azure Databricks cluster provides a suitable environment for data science, data engineering, and data analytics workloads to run. These workloads can be run as an automated job or a series of commands in a notebook.  

Azure Databricks has four different types of clusters: 

1. All-purpose/interactive clusters: These clusters are used to collaboratively analyze data using interactive notebooks. You can share this type of cluster with multiple users for collaborative interactive analysis. You get high concurrency and low latency with interactive/all-purpose clusters. 
2. Job clusters: These clusters facilitate running automated jobs. When you create a new job, a job cluster is created by Azure Databricks job scheduler to run the job. It also terminates the cluster once the job is complete.  
3. Low-priority clusters: They are mainly used for jobs that do not require high performance like testing and development jobs. As they are low-priority clusters, they are comparatively cheaper than other cluster types.  
4. High-priority clusters: They are mainly used for jobs that do require high performance like production workloads. As they are high-priority clusters, they are costlier than other cluster types. 

A staple in Azure Databricks interview questions and answers for experienced, be prepared to answer this one. Apache Kafka is a streaming platform in Azure Databricks that is mainly used for constructing stream-adaptive applications and real-time streaming data pipelines. Azure Databricks uses it for streaming data. In Azure Databricks, data is collected from multiple sources (e.g., logs, sensors, and financial transactions) and later analyzed.

Data sources like action hubs and Kafka supply data to Azure Databricks when it collects or streams data. Kafka also helps in processing and analyzing streaming data in real time. Databricks Runtime has Apache Kafka connectors for structured streaming. The message broker functionality is also provided by Kafka like a message queue, where you can subscribe to and publish named data streams.

Serverless computing refers to the concept of building and running applications and services without managing the underlying servers. It enables you to focus on application development rather than worry about server management. You can build and run data processing applications on serverless computing. Serverless computing runs code independently irrespective of whether the code is present on the user end or on the server.

Serverless data processing apps provide simpler, faster, and more efficient data processing. With serverless computing, you only pay for computing resources used by your data processing apps when they run. This is applicable when you run these apps even for a short time. As the users pay only for the resources that are used, they end up saving a lot with serverless data processing.

Azure SQL Database (DB) is a highly scalable, managed database service that stores data on the Azure platform. While providing high availability and scalability, Azure SQL DB protects data stored in it by using various data protection options available. 

1. SQL Server Firewall Rules: Azure SQL DB employs two layers of security. The first layer is a set of firewall rules stored in the SQL Master database, which are applied to the Azure database server. The second layer consists of security measures taken to prevent unauthorized access to data, including firewall rules at the database level. 
2. Confidential information protection: Azure SQL DB secures credit card numbers and other sensitive information stored in the SQL database from unauthorized access using its Always Encrypted feature. 
3. Data Encryption: Data stored in Azure SQL DB is protected using Transparent Data Encryption (TDE). In addition, all transactions and backups of log files and databases are encrypted and decrypted using TDE in real-time. 
4. Azure SQL DB audit: Azure SQL DB has an in-built auditing feature. It allows you to set the audit policy for specific databases or the complete database server.

Azure maintains multiple copies of the data stored in it at different levels to ensure that the data is available and accessible all the time. Azure storage facilities have a number of data redundancy solutions that ensure data security and availability. Each solution is tailored to meet specific requirements of Azure customers such as the time to retrieve replicas and the importance of data being replicated.

1. Locally Redundant Storage (LRS): To keep data highly available, Azure replicates data in multiple storage areas located in the same data center. As the copies of data are stored in three different places in the same physical location, it is also referred to as locally redundant storage (LRS). This is the most economical solution to ensure data redundancy.  
2. Zone Redundant Storage (ZRS): Storage data is replicated to three different availability zones (AZs) of the primary zone. Data is copied to these three AZs to ensure that if the primary site becomes unavailable, data can be retrieved from the copies stored at these AZs. This data redundancy feature is called zone redundant storage because data is stored in different data centers of the same region. 
3. Geographically Redundant Storage (GRS): Storage data is replicated to data centers located in different geographical regions. Azure provides this data redundancy option for the cases where the entire region becomes unavailable. Copies of data are stored in at least two distinct locations across different geographies. A geo-failover is required to access data from the secondary location in the case when the primary location is unavailable. 
4. Read Access Geo Redundant Storage (RA-GRS): This data redundancy option ensures that the data stored in the secondary region can be accessed when the primary region is down. 

Choosing a method for transferring data depends on some important factors that must be considered. These factors help you decide which method would be more suitable for the data you want to transfer from on-premises to Azure. The factors you must consider for data transfer include 

• Data size 
• Network bandwidth 
• Data transfer frequency (periodic or one-time transfer) 

You can transfer data from on-premises to Azure in two ways: 

1. Offline transfer via devices 

Offline data transfer is best suited when you want to transfer a large amount of data in one go. For offline data transfer, you can use large discs or other storage devices supplied by Microsoft Azure or send your own discs to Azure. Some of the devices that you can use for transferring data include Azure data box heavy, Azure data box,  and Azure Import/Export. 

2. Data transfer over a network 

Data can also be transferred from on-premises to Azure over a network using the following methods:  

1. Graphical interface: This data transfer method is suitable when you have a few files to transfer without using automation. You can use graphical interface tools such as Azure Storage Explorer to transfer data from on-premises to the Azure cloud. 
2. Programmatic or scripted transfer: Scripted data transfer involves using software tools provided by Azure to transfer data from on-premise to Azure. Some software tools for programmatic transfer are Azure PowerShell, AzCopy, and Azure CLI. You can use SDKs for PHP, Ruby, Python, Java, and Node/JS to transfer data programmatically. 
3. On-premises devices: Physical or virtual devices supplied by Azure can be used for data transfer. These devices will be at your data center and help in fast data transfer over a network connection. Azure's physical device for data transfer is Azure Stack Edge. Azure Data Box Gateway is a virtual device that helps in fast data transfer. 
4. Managed Data pipelines:  Managed data pipelines can help you transfer data over a network connection frequently from your data center to Azure. These data pipelines can be set up and managed using Azure Data Factory. 

Azure Cosmos DB supports five consistency models or levels to provide enhanced performance and high availability of data stored. Cosmos DB provides customers with 100% consistency of the read requests for the selected consistency model. 

1. Strong consistency: This consistency model guarantees linearizability. This means all requests are served concurrently, and the reads will return the most recent version of an item. It enables users to read only the latest committed write. This consistency model is most expensive. 
2. Bounded staleness consistency: In this consistency model, reads from a secondary region may not return the latest version of a data item globally. However,  reads return the latest version of the data in that region. This model works well in cases where consistency and availability are not priority.   
3. Session consistency: For Azure Cosmos DB, Session consistency is the default model. It is the most widely used mode across all regions. When a user goes to the location where a write was executed, they are guaranteed to get the most recent version of that data. It has the fastest throughput for reading and writing operations.   
4. Consistent prefix consistency: In this model, single document writes have eventual consistency. Users will not see out-of-order writes in this consistent prefix consistency model. However, data here is not replicated across different regions at a fixed frequency. 
5. Eventual consistency: This model provides the weakest consistency of all. This model provides no assurance that data is replicated within a set amount of time or a set version. Users may read data that is older than what they had read before. It offers the highest read latency. 

In Azure Data Factory, visually designed data transformations are called mapping data flows. Data engineers use mapping data flows to develop data transformation logic without scripting. With data transformation logic, data flows are executed inside Azure Data Factory (ADF) pipelines as activities. These pipelines use optimized Apache Spark clusters. Data flow activities can be invoked with the help of existing capabilities of Azure Data Factory such as scheduling, flow, control, and monitoring.

Without any coding, mapping data flows offer an impressive visual experience. ADF-managed execution clusters run data flows for scaled-out data processing. ADF manages all important aspects like code translation, path optimization, and data flow job execution. Mapping data flows are used by data engineers for data integration with no coding involved.  

You may face many critical challenges for continuous integration/continuous delivery while building a data pipeline. However, some challenges are critical and worth mentioning such as  

• Data exploration: you may find it difficult to explore data while building a data pipeline when multiple users collaborate on the same project. 
• Iterating unit tests: Changing code and writing unit cases iteratively pose a challenge as the process becomes cumbersome. 
• Continuous build and integration: New code is continuously merged. The build server needs to pull the latest changes and perform unit testing for multiple components and publish the artifacts on an ongoing basis.  
• Staging data pipelines: Pushing data pipelines to the staging environment poses a challenge as the pipeline is tested against a much larger data set (which is similar to production data) for data quality and performance. 
• Pushing data pipelines to production: Pushing data pipelines from the staging environment to the production environment may often pose challenges.

Git and Microsoft Team Foundation Server (TFS) are collaborative and version controlling tools. They both help you manage code easily. When it comes to Azure Databricks, working with TFS is not supported. As of now, you can use only Git or a similar repository system with Azure Databricks. Git is a free, open-source repository that allows users globally to manage more than 15 million lines of code but Team Foundation Server (TFS) has comparatively less capacity than Git. It has the capacity to handle 5 million lines of code.

Azure Databricks notebooks can easily integrate with Git. For managing Databricks code easily, we need to create a Databricks notebook, upload the notebook to Git, and then update it when required. We can consider the Databricks notebook as a replica of our project.

Azure Data lake (ADL) Gen2 employs a comprehensive and robust security mechanism to protect stored data. Its security mechanism has six layers of protection. 

1. Authentication: For authentication, it uses Azure Active Directory (AD), Shared Access Token, and Shared Key to keep user accounts secure. 
2. Access control: It uses access control lists (ACLs) and roles for granular control over who is entitled to access which resources. 
3. Network isolation: It allows admins to isolate networks logically by using firewalls to accept or refuse network traffic from specific IP addresses or VPNs .  
4. Data protection: As part of the data protection security measure, it encrypts data in transit via HTTPs to secure confidential information. Also, it encrypts data at rest. 
5. Advanced threat protection: This layer helps in monitoring any attempts to exploit or access the storage account. It will alert in case of any malicious activity detected. 
6. Auditing: All management activities are logged by ADL Gen2. ADLS logs will provide information on activities that took place in your account. This auditing capability will help in identifying malicious activities and trace threat actors.

Yes, we get the access control feature with Azure Delta Lake for enhanced security and governance. We can use the access control lists (ACLs) to restrict user access to workspace objects, pools, tasks, dashboards, clusters, tables, schemas, etc. Workspace objects include notebooks, models, folders, and experiments.

This access control feature prevents unauthorized access to Azure Delta lake and protects the data stored in it. Admins and selected users having delegated ACL management rights can manage the access control lists. Access control can be enabled or disabled for workspace objects, clusters, data tables, pools, jobs by admin users at the workspace level.

Azure Data Factory pipelines can be run either manually or through a trigger. An instance of pipeline execution in Azure Data Factory is defined as a pipeline run. These pipelines can be programmed to run automatically on a trigger or in response to external events.

Below is the list of  triggers that can make Azure Data Factory pipelines run automatically: 

1. Schedule trigger: This trigger executes a Data Factory pipeline run at a set time or schedule.  
2. Tumbling window trigger: This trigger executes at a periodic interval without termination. It retains the previous state. 
3. Event-based trigger: This trigger executes a pipeline run in a response to an event. 

Cloud object storage like Azure Blob offers a simple, scalable, and cost-effective solution to store important data on the cloud. Though data replication improves the availability of data stored in Azure Blob storage, it does not completely eliminate the need of having a backup.  

Azure Blob backups are important to handle incidents where the entire cloud storage is damaged. Data retrieval is possible only via backups. Consider some scenarios where Azure Blob backups can safeguard data stored in the Blob storage: 

1. Safeguard against accidental or malicious deletion: Cloud storage can be deleted accidentally or as a result of malicious activities. Having a backup can help you retrieve data and avoid data loss.  
2. Ransomware: If the cloud storage is attacked by ransomware, backups can help you restore the data without paying ransom. 
3. Data corruption and recovery point objectives: If data stored in cloud storage gets corrupted, you can restore to the previous known healthy state of data with the help of backups. 

A DataFrame is a specified form of tables that are used to store data inside Databricks runtime, whereas Pandas is a free-to-use Python package popular for machine learning and data analysis tasks. Apache Spark DataFrames are different from Pandas. Though DataFrames function in a manner similar to Pandas, they have differences.

In Apache Spark, Pandas cannot be used as an alternative to DataFrames. Being native to Azure, DataFrames get an edge over Pandas. Moving between these two frameworks will impact the performance, so users of DataFrames and Pandas can switch to Apache Arrow to improve performance and get better features and functionalities.

You can import data into Azure Delta lake from cloud storage using two ways: 

• Auto Loader: It provides a structured streaming source (CloudFiles) to process files that come in Azure cloud storage. It effectively and incrementally processes data without any additional setup.  
• COPY INTO: It helps SQL users to load data from cloud object storage to Azure Delta lake tables. This method can be used in Databricks notebooks, SQL, and Databricks jobs. 

You must consider a few things to decide which method is best for you: 

• If you want to ingest millions of files, you can use Auto Loader. If you have a few thousand files to ingest, you can go with COPY INTO. This is because Auto Loader is cheaper and more efficient to handle large amounts of files. 
• If you have a dynamic data schema that changes frequently, Auto Loader suits you best as it offers primitives around schema evolution and inference. 
• You can use COPY INTO for loading a subset of reuploaded files efficiently as it makes the process easier. 

Before developing business analytics logic, you must have the Databricks code written by other team members in your notebook first. Having the code in your notebook will help you reuse it to build business analytics logic. For copying the code to your notebook, you need to import it.  

You can use two methods for importing the code based on the place the code is present: 

• You can import the code and start using it if the code lies in the same workspace. 
• If the code is in another workspace, you have to make a jar or module file and then import it to your workspace. 

The process of dividing a huge dataset (DataFrame) into multiple small datasets while writing to disk (based on columns) is called PySpark Partition. On a filesystem, data partitioning can help to improve the performance of queries while dealing with large datasets in the Data lake. This is because transformations on partitioned data run smoothly and quickly, so the speed of query execution is improved.

There are two partitioning methods supported by PySpark: 

• Partition in memory: Using this method, partition or repartition the DataFrame by invoking coalesce() or repartition() transformations. 
• Partition on disk: You can partition data while writing the DataFrame to disk based on columns using PartitionBy().  

A PySpark DataFrame refers to a distributed group of structured data in Apache Spark. PySpark DataFrames are equivalent to relational database tables or an Excel sheet. They are comparatively more optimized than R or Python. You can create PySpark DataFrames using multiple sources such as Hive Tables, Structured Data Files, existing RDDs, and external databases.

You can create PySpark DataFrames using three methods: 

• You can use data formats like XML, CSV, JSON, etc. 
• You can import data from existing RDDs. 
• You can use programmatically specifying schema. 

DataFrames have some characteristics in common with Resilient Distributed Datasets (RDDs). 

• Immutable: Once DataFrames are created, they cannot be changed but can be transformed after applying transformations. 
• Distributed: Like RDDs, DataFrames are distributed in nature. 
• Lazy evaluations: Lazy evaluations mean that no task is executed until you perform an action.