Azure Databricks Interview Questions

Top 20 Azure Databricks Interview Questions

If you’re looking for a job in data engineering, then you’ll need to know how to answer some tough Azure Databricks interview questions. Databricks is a powerful tool used by data engineers to create and manage big data clusters. It’s essential that you understand the ins and outs of this tool if you want to land a job in this field. In this blog post, we will discuss some of the most common Azure Databricks interview questions and provide tips on how to answer them. Let’s get started!

1. What is Azure Databricks?

Ans. Azure Databricks is a powerful Apache Spark-based platform for big data analytics. It’s easy to use and can be deployed on Azure in minutes. Databricks offers excellent integration with other Azure services, making it an ideal tool for data engineers who want to work with big data in the cloud.

2. What are the benefits of using Azure Databricks?

Ans. There are many benefits of using Azure Databricks, including:

  • Reduced costs: You can save up to 80% on your cloud bill by using Databricks’ managed clusters.
  • Increased productivity: Databricks makes it easy to build and manage big data pipelines with its user-friendly interface.
  • Increased security: Databricks offers a variety of features to help you secure your data, including role-based access control and encrypted communication.

3. What is DBU Framework?

Ans. The DBU Framework is a set of libraries and tools that make it easy to develop big data applications on Databricks. The framework includes a CLI, Python SDK, and Java SDK.

4. What are the different types of clusters in Azure Databricks?

Ans. There are four types of clusters in Azure Databricks:

Interactive: Interactive clusters are used for exploratory data analysis and ad-hoc queries. These clusters provide low latency and high concurrency.

Job: Job clusters are used to run batch jobs. These clusters can be autoscaled to meet the demands of your job.

Low-priority: Low-priority clusters are cheaper than other cluster types but have lower performance. These clusters are ideal for jobs that can tolerate lower performance, such as development and testing.

High-priority: High-priority clusters are more expensive than other cluster types but offer the best performance. These clusters are ideal for production workloads.

5. What is autoscaling in Azure Databricks?

Ans. Autoscaling is a feature of Databricks that allows you to automatically scale your cluster up or down based on your needs. This can save you time and money by ensuring that you’re only using the resources you need.

6. What are some common issues with Azure Databricks?

Ans. Some common issues with Azure Databricks include:

  • Cluster creation failures: This can happen if you don’t have enough credits or if your subscription doesn’t allow for more clusters.
  • Spark errors: Spark errors can occur if you’re using an unsupported version of Spark or if your code is incompatible with the Databricks runtime.
  • Network errors: Network errors can occur if there’s a problem with your network configuration or if you’re trying to access Databricks from an unsupported location.

7. How can I troubleshoot Azure Databricks issues?

Ans. If you’re having trouble with Azure Databricks, the best place to start is the Databricks documentation. The documentation includes a list of common issues and their solutions. You can also contact Databricks support for help.

8. What is the use of Databricks filesystem?

Ans. The Databricks filesystem is used to store data in Databricks. It’s a distributed file system that is designed for big data workloads. The Databricks filesystem is compatible with the Hadoop Distributed File System (HDFS).

9. What languages can be used in Azure Databricks?

Ans. You can use any language that is supported by the Apache Spark platform, including Python, Scala, and R. In addition, you can use SQL with Azure Databricks.

10. Can you use PowerShell to administer Databricks?

Ans. No, you cannot use PowerShell to administer Databricks. You can use the Azure portal, Azure CLI, or Databricks REST API.

11. What is the difference between an instance and a cluster in Databricks?

Ans. An instance is a virtual machine (VM) that runs the Databricks runtime. A cluster is a group of instances that are used to run Spark applications.

12. What is the management plane in Azure Databricks?

Ans. The management plane is responsible for managing and monitoring your Databricks deployment. It includes the Azure portal, Azure CLI, and Databricks REST API.

13. What is the control plane in Azure Databricks?

Ans. The control plane is responsible for managing Spark applications. It includes the Spark UI and Spark History Server.

14. What is the data plane in Azure Databricks?

Ans. The data plane is responsible for storing and processing data. It includes the Databricks filesystem and Apache Hive metastore.

15. Can you cancel an ongoing job in Databricks?

Yes, you can cancel an ongoing job in Databricks by clicking on the job in the Jobs page and then selecting Cancel Job from the drop-down menu.

16. What is the delta table in databricks?

Ans. A delta table is a type of table that stores data in the Databricks Delta format. Delta tables are optimized for fast reads and writes, and they support ACID transactions.

17. What is Databricks Runtime?

Ans. Databricks Runtime is a software platform that runs on top of Apache Spark. It includes libraries, APIs, and tools that make it easy to build and run Spark applications.

18. What is Databricks Spark?

Ans. Databricks Spark is a fork of Apache Spark. It includes enhancements to Spark that make it easier to use in Databricks.

19. What is azure databricks workspace?

Ans. An Azure Databricks workspace is a managed Apache Spark environment. It includes all the tools you need to build and run Spark applications, including a code editor, a debugger, and libraries for Machine Learning and SQL.

20. What is dataframe in azure databricks?

Ans. A dataframe is a type of table that stores data in the Databricks runtime. Dataframes are optimized for fast reads and writes, and they support ACID transactions.

21. What is the purpose of Kafka in Azure Databricks?

Ans. Kafka is used in Azure Databricks for streaming data. It can be used to ingest data from sources such as sensors, logs, and financial transactions. Kafka can also be used to process and analyze streaming data in real-time.

We hope this blog post has been helpful in preparing you for your next Azure Databricks interview! Best of luck!

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