Azure is one of the most popular cloud platforms that offer a multitude of solutions and services to enterprises, professionals, and government agencies across the world at competitive prices. Having a strong understanding of Azure services makes professionals highly relevant in the current job market. Whether you are a beginner, Intermediate, or expert Azure professional, these Azure Databricks IQAs will help you increase your knowledge and confidence to face interviews related to Azure Databricks roles. The questions are divided into multiple categories like questions for Freshers (covering all fundamental Azure Databricks concepts), Intermediate (covering some azure data bricks scenario-based interview questions), and experienced (covering practical important concepts and azure data bricks interview questions for experienced). With these interview questions, you can confidently prepare for your next interview and even crack it easily. This guide serves as a one-stop solution for you if you are looking to advance your career in Azure Databricks.
Cloud computing refers to a virtualization-based technology that enables organizations to access third-party data centers located outside their premises over the internet to host their servers, apps, and data. These data centers are owned and managed by cloud service providers. Cloud computing offers all the IT infrastructure required for business organizations as a service. This includes storage, computer, networking, and other resources.
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
Cloud service providers offer mainly three types of service models to cater to customer needs:
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
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:
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:
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:
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:
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:
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.
The major difference between data analytics workloads and data engineering workloads is automation. Data analytics workloads cannot be automated whereas data engineering workloads can be automated.
You cannot automate data analytics workloads on Azure Databricks. For example, consider commands inside Azure Databricks notebooks that run on Spark clusters. They keep running until terminated manually. This is because they do not support automation. Spark clusters can be shared among multiple users to analyze them collaboratively.
You can automate data engineering workloads because they are jobs that can automatically start and terminate the cluster they run on. For example, you can trigger a workload using the Azure Databricks job scheduler. This will launch an Apache Spark cluster exclusively for the job and terminate the cluster automatically once the job is done.
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:
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:
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.
What is a Databricks personal access token? How do you create a Databricks personal access token?
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:
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
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:
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:
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:
When you create an Azure Databricks cluster, you have three cluster modes to choose from. You can decide which cluster mode suits best for you based on your requirements. Editing or changing of modes is not supported.
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:
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.
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.
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
You can transfer data from on-premises to Azure in two ways:
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.
Data transfer over a network
Data can also be transferred from on-premises to Azure over a network using the following methods:
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.
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
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.
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:
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:
Is the implementation of PySpark DataFrames completely unique when compared to that of other Python DataFrames like Pandas?
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:
You must consider a few things to decide which method is best for you:
Suppose you have just started your job at ABC Corp. Your manager asked you to develop business analytics logic in the Azure notebook using some of the general functionality code written by other team members. What would be your first step?
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:
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:
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:
DataFrames have some characteristics in common with Resilient Distributed Datasets (RDDs).