20 Azure Data Lake Interview Questions and Answers

Azure Data Lake is a cloud-based data storage and analytics service from Microsoft. It is designed to handle large amounts of data, both structured and unstructured. As a result, it is a popular choice for businesses that need to process and analyze large data sets.

If you are interviewing for a position that involves working with Azure Data Lake, you can expect to be asked questions about your experience and knowledge of the platform. In this article, we will review some of the most common Azure Data Lake interview questions and how you should answer them.

Azure Data Lake Interview Questions and Answers

Here are 20 commonly asked Azure Data Lake interview questions and answers to prepare you for your interview:

1. 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.

2. 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.

3. 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?

A 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.

4. What’s the best way to get the list of blobs in Azure Data Lake Store?

The best way to get the list of blobs in Azure Data Lake Store is to use the Azure Data Explorer.

5. What’s the best way to find out how many files are present in a folder in Azure Data Lake Store?

The easiest way to do this is to use the Get-AzureRmDataLakeStoreItem cmdlet. This will give you a list of all the files and folders in a given directory, and you can use the -Recurse flag to get a count of all the files in subdirectories as well.

6. 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.

7. 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.

8. How do you load data into Azure Data Lake Store?

There are a few different ways to load data into Azure Data Lake Store. One way is to use the Azure Data Factory to create a pipeline that will move data from its original location into Azure Data Lake Store. Another way is to use the Azure Databricks platform to create a Spark cluster that can read data from its original location and then write it into Azure Data Lake Store.

9. 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.

10. 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.

11. 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.

12. 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.

13. 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.

14. 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.

15. 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.

16. 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.

17. Why are there so many different tools available for working with Big Data? Which one should I learn first?

There are a variety of different tools available for working with Big Data because there is no one-size-fits-all solution. The best way to determine which tool to learn first is to assess your specific needs and then choose the tool that best meets those needs.

18. 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.

19. 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.

20. 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.