Azure Data Lake Interview Questions

azure data lake interview questions

What is an Azure Data Lake ?

In simple words Azure data lake can be described as a building a capability which can store massive amount of data (i.e. azure data lake storage), having the power and tools to transform, analyze and process data of any size (i.e. azure data lake analytics, HDInsight) included with the security provided by (Azure IAM, Azure AD). Whole idea of Azure Data lake is to create an enterprise data solution which can store massive amounts of data and jobs can be run on it without worrying of complexities of data ingestion and storage. Azure IAM is associated with it to provide high security to the data stored and execution permission.

What are the core features of the Azure blob storage service?

1. Durable and highly available. 2. Secure 3. Scalable 4. Managed 5. Accessible

Assume that you are working for XYZ organization as azure developer and your organization is moving to cloud from on premise location. As a part of this activity you may need to store data which is not to be accessed from outside the virtual machine to which the disk is attached.. Which Azure storage solution would you prefer for this situation and why?

Azure Disk would be the right choice here. Azure disks allow data to be persistently stored and accessed from an attached virtual hard disk.

Assume that you are working for ABC organization as azure architect and your organization is building enterprise solutions having multiple applications. As a part of this solution multiple components want to communicate with each other using the asynchronous messages. Which Azure storage solution would you prefer for this situation and why?

Azure Queues would be the right choice here. It allows asynchronous message queuing between application components.

Assume that you are working as a data engineer for Azurelib.com. Your application is storing the data with the cloud as your blob storage. Application is generating some reports which need to be accessible to third-party applications. However, you want this to be accessible only for the next 7 days. After that, it should automatically not be allowed access to these reports. How could you solve this problem?

Application is generating the data into the Azure blob storage. We have SAS token available with azure storage solution. We can create a SAS token for these reports where we can mention the time duration of the next 7 days for this token. Share this SAS token with other applications so that they can use the token to get the reports. After the 7 days, this token automatically expires and will not be allowed to access anyone after the seven days.

Which protocol is used by the Azure file for accessing the share files?

Server Message Block (SMB) protocol is used by the Azure file share.

What are the main components of Azure Data Lake Analytics?

The main components of Azure Data Lake Analytics are the Data Lake Store, the Analytics Service, and the U-SQL language. The Data Lake Store is a repository for storing data in any format, including structured, unstructured, and semi-structured data. The Analytics Service is a managed cloud service that allows you to run analytics jobs on your data stored in the Data Lake Store. The U-SQL language is a query language designed specifically for big data analytics. It is a combination of SQL and C# that allows you to easily process and analyze large amounts of data.

Can you explain what blob objects are in the context of Azure Data Lake?

Blob objects are essentially files that can be stored in Azure Data Lake. These files can be of any type, and can be accessed and processed by various Azure services.

What is your understanding of a job in the context of Azure Data Lake? How does it differ from other platforms like Spark or Yarn?

Job in Azure Data Lake is a unit of work that is submitted to the platform in order to be executed. This can be a batch job, a streaming job, or a query. Jobs are submitted as code, and they are then compiled and executed by the platform. The main difference between a job in Azure Data Lake and a job on other platforms like Spark or Yarn is that a job in Azure Data Lake can be written in any language, while jobs on other platforms are usually written in Java.

What is the process used by Azure Data Lake Analytics to transform data?

The process used by Azure Data Lake Analytics to transform data is known as a U-SQL job. This job will take the data that is stored in your data lake and will apply a series of transformations to it in order to clean it up and prepare it for analysis. The U-SQL job will then output the transformed data into a new location in your data lake so that it can be used for further analysis.

What is an object store in context with Data Lake?

An object store is a type of storage that is optimized for storing large amounts of data that is unstructured or semi-structured. This data can include things like images, videos, and log files. Object stores are a good choice for storing data that is not easily queried or analyzed, and they are often used in conjunction with data lakes.

What is the default retention period for an object in Azure Data Lake Store? How can it be changed?

The default retention period for an object in Azure Data Lake Store is 120 days. This can be changed by altering the object’s metadata.

What types of files can be stored in Azure Data Lake Store?

Azure Data Lake Store can store any type of file, including text, binary, and Avro.

What is the max size of a file that can be uploaded to Azure Data Lake Object Storage?

There is no maximum file size for Azure Data Lake Object Storage. You can upload files of any size to Azure Data Lake Object Storage.

What are some use cases for Azure Data Lake?

Azure Data Lake can be used for a variety of tasks including data warehousing, data mining, data analysis, and data visualization. It can also be used to process and store large amounts of data, making it an ideal platform for big data applications.

What is the maximum size allowed for a batch in Azure Data Lake Analytics?

The maximum size for a batch in Azure Data Lake Analytics is 100 MB.

What are the differences between Azure Data Lake and other cloud-based big data solutions like AWS S3, Google Cloud Storage, or IBM Bluemix?

Azure Data Lake is a cloud-based big data solution that is optimized for processing and storing large amounts of data. It is designed to be scalable and to handle a variety of data types. Azure Data Lake is also integrated with other Azure services, making it easy to build big data solutions on the Azure platform.

What are the differences between Azure Data Lake and other cloud-based big data solutions like AWS S3, Google Cloud Storage, or IBM Bluemix?

Azure Data Lake is a cloud-based big data solution that is optimized for processing and storing large amounts of data. It is designed to be scalable and to handle a variety of data types. Azure Data Lake is also integrated with other Azure services, making it easy to build big data solutions on the Azure platform.

What is the advantage of using Azure Data Lake over Amazon Web Services S3?

Azure Data Lake offers a number of advantages over Amazon Web Services S3, including the ability to scale to accommodate large amounts of data, the ability to process data in real time, and the ability to integrate with a number of other Azure services.

What do you understand about Big Data? What challenges does it solve?

Big Data is a term used to describe data sets that are too large and complex to be processed using traditional methods. Big Data can come from a variety of sources, including social media, sensors, and transactional data. The challenges that Big Data poses include storage, analysis, and visualization. Big Data solutions can help organizations to make better decisions, improve operational efficiency, and gain insights into customer behavior.

What is Hadoop? How does it work?

Hadoop is a distributed file system that is used to store and process large amounts of data. It is designed to be scalable and fault-tolerant, and it works by breaking up files into smaller pieces and distributing them across a cluster of nodes.

What are the three V’s of Big Data?

The three V’s of Big Data are volume, velocity, and variety. Volume refers to the amount of data that is being generated. Velocity refers to the speed at which that data is being generated. Variety refers to the different types of data that are being generated.

What are containers? What’s the difference between Docker and Kubernetes?

Containers are a type of virtualization that allows you to package an application with all of its dependencies and run it on any other machine with the same operating system. Docker is a popular container platform that makes it easy to package, deploy, and manage containers. Kubernetes is an open-source container orchestration system that can be used to manage large numbers of containers.