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.
Frequently Asked Questions
What is Azure Data Lake, and what type of data can be stored in it?
Azure Data Lake is a cloud-based storage and analytics service that allows businesses to store, process, and analyze massive amounts of data. It can store structured, semi-structured, and unstructured data types, including images, videos, and logs.
What types of jobs are available in Azure Data Lake?
Jobs in Azure Data Lake can include data scientists, data analysts, data engineers, and data architects, among others. Each role involves working with data to some degree, but the specific responsibilities and required skills may differ.
What skills are required to work with Azure Data Lake?
Some key skills required for working with Azure Data Lake include expertise in data storage, processing, and analysis, as well as proficiency in data manipulation tools such as SQL and Python. Familiarity with Azure cloud computing and big data technologies is also beneficial.
How much experience is needed to work with Azure Data Lake?
Experience requirements vary depending on the specific job and level of seniority. Entry-level roles may require a bachelor’s degree in a related field and some experience with data analysis, while more senior positions may require several years of experience in data engineering or architecture.
What is the average salary for jobs related to Azure Data Lake?
Salaries for Azure Data Lake-related jobs can vary widely based on factors such as job title, location, and experience level.
However, some estimates suggest that data engineers can expect to earn an average of $100,000 to $150,000 per year.
What is the role of data security in Azure Data Lake?
Data security is a critical aspect of Azure Data Lake, as it involves storing and processing sensitive business information. The platform provides various security features such as access controls, encryption, and compliance with various regulatory standards.
How does Azure Data Lake compare to other big data platforms?
. Azure Data Lake offers many unique features and advantages compared to other big data platforms, including its integration with other Azure services, flexibility in data processing, and support for a wide range of data types.
Can Azure Data Lake be used for real-time data processing?
Yes, Azure Data Lake can be used for real-time data processing through integration with other Azure services such as Azure Stream Analytics.
What types of companies are using Azure Data Lake?
Many companies across various industries, including healthcare, finance, and retail, are using Azure Data Lake for data storage, processing, and analysis.
How can I learn more about working with Azure Data Lake?
. Azure Trainings offers a range of training and certification options for individuals interested in working with Azure Data Lake, including online courses, certifications, and documentation.
