30 AWS Glue Interview Questions and Answers [2024]

If you’re planning to land a job that involves AWS Glue, preparing for the interview requires a good understanding of the subject. In this article, we have compiled a list of some of the most frequently asked questions along with their detailed answers to help you prepare.

Through these 30 AWS Glue Interview Questions and Answers, we aim to assist you in your interview preparation. These questions cover various aspects of AWS Glue, from basic to advanced, ensuring a comprehensive review of the topic. Whether you’re a beginner or have some experience with AWS Glue, this list will help you brush up on your knowledge and boost your confidence before the big day.

1. What Is AWS Glue and What Are Its Main Components?

Tips to Answer:

  • Start by explaining what AWS Glue is in a simple and clear manner. AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics.
  • Describe the main components of AWS Glue. These include the AWS Glue Data Catalog, AWS Glue Crawlers, ETL Jobs, and Development Endpoints. Be prepared to go into detail about each component.

Sample Answer: I would define AWS Glue as a fully managed, cloud-based ETL service that simplifies and automates the difficult and time-consuming tasks of data discovery, conversion mapping, and job scheduling. AWS Glue is designed to make it easy for users to prepare and load their data for analytics.

As for its main components, AWS Glue consists of a Data Catalog which is a centralized metadata repository, AWS Glue Crawlers that connect to your source or target data store, extract metadata, and create table definitions in your Data Catalog. Then, there’s ETL Jobs that you can create and manage in the AWS Management Console or with their API. ETL jobs can be used to transform, cleanse, validate and relocate the data. Lastly, there are Development Endpoints which allows you to develop and test your ETL scripts.

2. How Does AWS Glue Relate to AWS Lake Formation?

Tips to Answer:

  • It is crucial to understand both AWS Glue and AWS Lake Formation in depth. Explain how AWS Glue supports data discovery, cataloging, and ETL, while AWS Lake Formation facilitates the creation of secure data lakes.
  • Emphasize on the integration of both services. AWS Lake Formation uses AWS Glue for certain tasks such as setting up, configuring, and managing data catalog, storage, access, and ETL jobs.

Sample Answer: As a data engineer, I use AWS Glue and AWS Lake Formation in tandem to manage my data workflows. AWS Glue is used for data discovery, cataloging, and ETL (Extract, Transform, Load) tasks. It provides a unified view of our data, regardless of where it’s stored in AWS, and it prepares and loads the data for analysis.

On the other hand, AWS Lake Formation is specifically for setting up and securing data lakes, a task that often requires a lot of manual, time-consuming set up. With AWS Lake Formation, I can define the policies once, and then apply them across the entire data lake. It integrates with AWS Glue to handle tasks such as data cataloging and ETL jobs. In essence, AWS Glue is the underlying service that supports AWS Lake Formation.

3. What Are AWS Glue Crawlers And What Is Their Purpose?

Tips to Answer:

  • Understand and explain what AWS Glue Crawlers are in an easy-to-understand manner.
  • Discuss the importance and purpose of AWS Glue Crawlers in the context of AWS Glue and how they help in managing data.

Sample Answer: In AWS Glue, Crawlers are a powerful component that plays a key role in managing data. They have a special purpose to automatically traverse your data stored in Amazon S3, Amazon RDS, and other data stores. They then extract metadata and create table definitions in the AWS Glue Data Catalog. This feature is essential as it simplifies and automates the time-consuming task of cataloging data, which is a critical step in the ETL process. It’s important to note that Crawlers can even handle version changes, so if your data changes, they can update the schema in the Data Catalog to reflect these changes. Also, you can schedule Crawlers to run at specific times to ensure that your Data Catalog stays up-to-date with your data.

4. What Are Development Endpoints In AWS Glue And How Are They Used?

Tips to Answer:

  • Explain the concept of Development Endpoints in AWS Glue, what they are, and their main functionality.
  • Discuss how Development Endpoints are used in AWS Glue, providing specific examples and scenarios to illustrate your points.

Sample Answer: Development Endpoints in AWS Glue are an interactive environment that allows AWS Glue ETL job authors to develop, debug, and test AWS Glue scripts. This interactive environment is especially useful for complex ETL jobs involving multiple data sources, transformations, and loads.

For instance, you can use Development Endpoints to run AWS Glue ETL job scripts in a notebook like Jupyter, where you can interactively edit, debug, and test your script before moving it to production. This greatly enhances the development and debugging process, saving time and effort in identifying and fixing script issues.

So, in essence, Development Endpoints in AWS Glue provide an environment for iterative development and testing of ETL scripts to ensure their correctness and efficiency before they are deployed in a production setting.

5. What Are AWS Tags In AWS Glue And How Can They Be Utilized?

Tips to Answer:

  • Understand the functionality of AWS Tags and their use cases in AWS Glue context. Tags are metadata that you assign to AWS resources, they can be used for cost tracking, automation, access control, and organization.
  • Familiarize yourself with the process to utilize AWS Tags in AWS Glue. This includes creating tags, assigning them to resources, and using them in cost allocation reports or for access control.

Sample Answer: In the context of AWS Glue, AWS Tags are key-value pairs that can be associated with AWS Gl overall, conclusion, moreover, furthermore. They are a type of metadata that can be assigned to resources to help manage, sort, and filter them. For example, you might assign tags to your AWS Glue resources to indicate their purpose, the owner, or other relevant information.

The primary benefits of using AWS Tags in AWS Glue include cost tracking, automation, access control, and organization. For instance, you can assign tags to your AWS Glue resources to categorize them by cost center or owner. This can then be used to generate detailed cost allocation reports. Similarly, tags can be used in IAM policies to control access to resources. For example, you might restrict access to only those resources that have a certain tag.

6. What Is The AWS Glue Schema Registry And What Are Its Benefits?

Tips to Answer:

  • Understand the concept of AWS Glue Schema Registry, its purpose and how it works. This includes knowledge on how it integrates with other AWS services and how it is used in data streams.
  • Be clear on the benefits of AWS Glue Schema Registry. This could be in terms of data consistency, cost-effectiveness, scalability and so on.

Sample Answer: The AWS Glue Schema Registry is a feature of AWS Glue that allows you to centrally discover, control, and evolve data stream schemas. This ensures that all data producers and consumers are using a standard schema, leading to consistent data quality. The Schema Registry stores versioned Avro and JSON schemas and can integrate with Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, and Apache Kafka clusters in Amazon Managed Streaming for Apache Kafka (Amazon MSK).

One of the primary benefits of the AWS Glue Schema Registry is that it improves data quality by ensuring that producers and consumers use the same schema. This reduces data inconsistencies and errors. It’s also cost-effective as there is no additional charge for using the AWS Glue Schema Registry. You only pay for the AWS Glue crawler that is used to discover and register your schemas. Another benefit is that it’s fully managed, so you don’t need to worry about setting up, managing, and scaling your own infrastructure to handle schema versioning and enforcement.

7. What Is An AWS Glue Database And How Does It Differ From A Regular Database?

Tips to Answer:

  • Focus on the fundamentals: Explain what an AWS Glue Database is, its purpose, and its main features.
  • Highlight the differences: Compare and contrast an AWS Glue database with a traditional database. Discuss aspects like management, scalability, and cost.

Sample Answer: As an AWS user, I find the AWS Glue Database to be a key component of the AWS Glue service. It is a metadata repository created by AWS Glue, which is different from your conventional database. An AWS Glue Database holds table definitions, job definitions, and other control information to help manage your AWS Glue environment. The key difference between an AWS Glue Database and a regular database lies in their purpose and structure. A regular database is designed to store data while an AWS Glue Database is designed to store metadata. The AWS Glue Database does not store actual data. Instead, it stores metadata that points to the location of the actual data. This makes it efficient for managing and processing large volumes of data.

8. Explain The AWS Glue Job System And How It Works.

Tips to Answer:

  • Understand the AWS Glue job system thoroughly. It is AWS Glue’s primary data processing framework and knowing how it works will go a long way to answer this question effectively.
  • Use specific examples where possible. This will help illustrate your understanding of the AWS Glue job system and its workings.

Sample Answer: As an AWS Glue user, I can tell you that the AWS Glue job system is a significant part of the service. It is designed to provide an environment for running our ETL (Extract, Transform, Load) scripts. The AWS Glue job system allows us to design our ETL jobs and workflows using either Python or Scala. Once we have created a job, AWS Glue handles everything else. It allocates the necessary resources, monitors the job’s progress, and even retries failed jobs. This way, we can focus on writing our ETL scripts and leave the infrastructure management to AWS Glue. Additionally, AWS Glue job system is serverless, so we only pay for the compute time we use, which makes it a cost-effective solution for data processing.

9. What Is AWS Glue Elastic Views And What Problem Does It Solve?

Tips to Answer:

  • Understand the concept of AWS Glue Elastic Views, how it works and what it is used for.
  • Identify the problems that AWS Glue Elastic Views are designed to solve and explain how it solves them.

Sample Answer: AWS Glue Elastic Views is a feature of AWS Glue that allows you to create virtual tables, or “views”, of your data stored in different AWS data stores. It serves as an easy-to-use tool for data replication across multiple data stores without having to manage the underlying infrastructure. The main problem it solves is the fragmentation of data across different databases. With Elastic Views, you can combine and replicate data across multiple data stores to enable real-time analytics, without having to deal with the complexity of setting up and managing data pipelines. This increases efficiency and reduces the time and effort needed to extract insights from data.

10. How Does AWS Glue Integrate With Other AWS Services Like Amazon S3, Amazon Redshift, and AWS Lambda?

Tips to Answer:

  • It’s important to understand and mention the specific functionalities of AWS Glue that allow it to integrate with these other AWS services. This includes how AWS Glue can read data from Amazon S3, transform and prepare it, and then write the results back to an Amazon S3 bucket or load them into Amazon Redshift.
  • AWS Lambda’s role in triggering ETL jobs in AWS Glue can also be discussed. It’s beneficial to demonstrate how these services work together to automate data workflows.

Sample Answer: As a fully managed ETL service, AWS Glue integrates seamlessly with other AWS services. For instance, it can read data from Amazon S3, apply certain transformations to clean and prepare the data, and then write the results back into an Amazon S3 bucket or load them into Amazon Redshift for analysis. As for AWS Lambda, it can be used to trigger AWS Glue ETL jobs based on specific events or conditions, thus automating data workflows. This synergy between AWS services allows for efficient and automated data processing and analysis pipelines.

11. What Are Some Common Use Cases For AWS Glue?

Tips to Answer:

  • Start by explaining what AWS Glue is briefly, then proceed to cite some instances where AWS Glue is commonly used. This helps give context to your answer.
  • Use specific examples wherever possible. This helps to demonstrate your practical experience with AWS Glue.

Sample Answer: AWS Glue, a fully-managed extract, transform, and load (ETL) service, is used in a variety of scenarios. One common use case is data warehousing, where AWS Glue extracts data from various sources, transforms it into a useful format, and loads it into a data warehouse like Amazon Redshift. AWS Glue is also often used for data lake analytics. It can catalog data from different sources and store it in a central repository known as a data lake, making it easier for analysis. Another use case is for data migration. AWS Glue can extract data from on-premises databases, transform it as necessary, and load it into cloud-based databases. It’s also used for log analytics, where AWS Glue can transform and enrich logs to create a consistent format for easier analysis.

12. How Does AWS Glue Handle Data Security And Encryption?

Tips to Answer:

  • Understand the data security and encryption mechanisms provided by AWS Glue. This includes knowing how AWS Glue uses AWS Key Management Service (KMS) for encryption, the role of AWS Identity and Access Management (IAM) in controlling access to resources, and how AWS Glue manages data privacy.
  • Be prepared to explain how these security mechanisms can be implemented in real-world scenarios. Providing examples can help to demonstrate your knowledge and understanding of the topic.

Sample Answer: In AWS Glue, data security and encryption are handled through several mechanisms. First, AWS Glue uses the AWS Key Management Service (KMS) to encrypt data at rest. This means that all data stored by AWS Glue is automatically encrypted using keys managed by KMS.

Second, AWS Glue leverages AWS Identity and Access Management (IAM) to control access to resources. IAM allows you to create and manage AWS users and groups, and use permissions to allow and deny their access to AWS resources, including AWS Glue resources.

In terms of data privacy, AWS Glue adheres to AWS’s shared responsibility model. This means that while AWS is responsible for the security of the cloud, customers are responsible for security in the cloud. Therefore, customers need to ensure that they’re using the security features and best practices provided by AWS Glue to protect their data.

In terms of implementation, for example, you can use KMS to create custom encryption keys and implement fine-grained access control using IAM policies. This way, you can ensure that only authorized users have access to your AWS Glue resources and the data they contain.

13. What Are Some Best Practices For Designing Etl Workflows In Aws Glue?

Tips to Answer:

  • Be sure to mention the importance of data partitioning and how it can optimize performance in AWS Glue.
  • Highlight the use of AWS Glue features like custom connectors and transforms which can improve the efficiency of ETL workflows.

Sample Answer: In my experience of designing ETL workflows with AWS Glue, I have found several best practices that I always adhere to. First, I ensure that I implement data partitioning. This is incredibly beneficial as it optimizes performance by reducing the amount of data that needs to be processed. Second, I make efficient use of AWS Glue’s features such as custom connectors and transforms. Custom connectors allow me to connect to various data sources, while transforms allow me to perform complex data manipulations. This not only enhances the efficiency of my ETL workflows but also enables me to handle a wider range of use cases. I’ve also found that monitoring and troubleshooting AWS Glue jobs is crucial for maintaining optimal performance and swiftly addressing any issues that may arise.

14. How Can You Monitor And Troubleshoot AWS Glue Jobs?

Tips to Answer:

  • Understand the tools and services provided by AWS for monitoring and troubleshooting. For instance, AWS CloudWatch provides insights on AWS resources and applications, which can be used to monitor AWS Glue Jobs.
  • Be familiar with common issues that may arise during AWS Glue jobs and how to resolve them. Knowledge of the AWS Glue service documentation and user guides will be useful.

Sample Answer: In order to monitor AWS Glue jobs, I leverage AWS CloudWatch. AWS CloudWatch allows me to track metrics, collect and monitor log files, and set alarms. I can view operational data and concurrent executions or trigger metrics, which can help me understand the performance of my AWS Glue jobs.

When it comes to troubleshooting, I will first check the error messages in the AWS Glue console. Most errors come with Error Codes that provide insights into the nature of the problem. If the problem persists, I may increase the logging level to DEBUG or TRACE for more detailed information. In some cases, I might have to modify the ETL code or adjust the timeout settings. AWS Glue service documentation also provides a comprehensive list of common errors and ways to troubleshoot them, which is a handy reference.

15. What Are Some Common Challenges When Working With AWS Glue, and How Do You Overcome Them?

Tips to Answer:

  • Understand the common challenges that may arise when using AWS Glue such as handling large datasets, dealing with complex ETL workflows, and managing Glue Crawlers.
  • Share practical solutions or strategies on how you would overcome these challenges. This could include a good understanding of Glue components, familiarity with AWS services, and the best practices for designing ETL workflows.

Sample Answer: In my experience, some of the challenges I faced when working with AWS Glue included managing large datasets and dealing with complex ETL workflows. To handle large datasets efficiently, I ensured that the data was partitioned optimally in AWS Glue. This helped in reducing the time taken for data processing. For dealing with complex ETL workflows, I made sure to follow the best practices such as avoiding transformation of unnecessary data and using AWS Glue’s built-in transforms as much as possible. Another challenge was managing Glue Crawlers, as it is essential to configure them correctly to ensure the correct metadata is extracted. To overcome this, I familiarized myself with AWS Glue components and worked closely with the AWS documentation and support team.

16. What Are Some Common Challenges When Working With AWS Glue, And How Do You Overcome Them?

Tips to Answer:

  • Highlight your problem-solving skills by explaining how you identify and approach common challenges in AWS Glue.
  • Provide specific examples of issues you have encountered and how you resolved them. This could include technical issues, such as handling large data volumes, or project management challenges, such as coordinating with other team members.

Sample Answer: In my experience with AWS Glue, one common challenge has been handling large data volumes. To solve this, I used Glue’s dynamic data partitioning feature, which significantly improved the efficiency of my ETL jobs. Another issue was coordinating ETL processes with other team components. I overcame this by setting up notifications and failure retries in my Glue jobs, ensuring that any issues were quickly identified and addressed.

17. What Are Some Advanced Features of AWS Glue Like Custom Connectors and Transforms?

Tips to Answer:

  • Be sure to highlight your knowledge about the advanced features of AWS Glue. This includes custom connectors, which are used to connect to non-native data sources, and transforms, which are used to modify data.
  • Mention any experience you have in using these features in your past projects or work. This will help to show your practical knowledge and hands-on experience.

Sample Answer: In AWS Glue, there are a number of advanced features that provide flexibility and enhance data processing tasks. For instance, custom connectors are a significant feature that allows AWS Glue to connect to data sources that are not natively supported. I have used this feature in a recent project to connect to a third-party CRM system. This provided a seamless integration of data for our ETL workflows.

Transforms, on the other hand, are used to modify the data as it is being processed in AWS Glue. Commonly used transforms include ApplyMapping, Relationalize, and Unnest. I often use the ApplyMapping transform to change the names, types, and formats of my data fields. This comes in handy when preparing data for analysis or reporting tasks. Through my experience, I find that these features greatly enhance the capability of AWS Glue in handling diverse data sources and complex data processing tasks.

18. How Can You Use AWS Glue To Handle Streaming Data?

Tips to Answer:

  • First, explain what streaming data is and why it’s important in the context of AWS Glue.
  • Discuss the specific functionalities and tools provided by AWS Glue for handling streaming data.
  • If you have experience using AWS Glue with streaming data, share a specific example to demonstrate your understanding and expertise.

Sample Answer: In the context of AWS Glue, streaming data refers to the data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes. Handling streaming data is a significant aspect of AWS Glue as it provides real-time analytics and insights.

AWS Glue can handle streaming data through its capabilities to run continuous jobs that consume data from streaming sources. For example, AWS Glue can connect to Amazon Kinesis Data Streams or Kafka topics as a source in a Glue ETL job. These AWS Glue jobs continuously consume and process the data, transforming it and loading it into a data store.

During my tenure at a previous company, I used AWS Glue to handle streaming data from various sources. We had an e-commerce platform with millions of users, and we needed to process the real-time data generated by these users for real-time analytics. I set up AWS Glue to continuously consume this data, transform it according to our needs, and load it into Amazon Redshift for analysis. It was a critical component of our data analytics pipeline.

19. What is the AWS Glue Data Catalog and How Does it Work?

Tips to Answer:

  • Understand the concept of AWS Glue Data Catalog and how it functions.
  • Discuss the specific benefits of using the AWS Glue Data Catalog in managing and organizing data.

Sample Answer: The AWS Glue Data Catalog serves as a centralized metadata repository for diverse data sources. It offers a unified view of data spread across various AWS services. When you use AWS Glue to run ETL jobs, you can use the Data Catalog as your external Apache Hive metastore, which provides a comprehensive, search-friendly perspective of your data.

In terms of functionality, the Data Catalog automatically collects and collates metadata from your data sources, storing it securely in a search-friendly manner. It makes it easier to discover, manage, and search for data. You can also use it to maintain schema versioning and history.

By using the AWS Glue Data Catalog, you can eliminate the need for manual cataloging and reduce the time spent on ETL jobs. It also provides a robust foundation for data discovery and data governance.

20. How Do You Create And Manage AWS Glue Jobs Using The AWS Management Console?

Tips to Answer:

  • Be specific and detailed in your explanation. Describe the step-by-step process of creating and managing AWS Glue Jobs using the AWS Management Console, this shows your hands-on experience with the tool.
  • Discuss the importance of each step and the possible implications if not done correctly. This demonstrates your understanding of why each step is important and that you are careful when performing tasks.

Sample Answer: Based on my experience, creating and managing AWS Glue Jobs using the AWS Management Console involves a series of steps. First, you would navigate to the AWS Glue service from the AWS Management Console. From there, you would select ‘Jobs’ from the left-hand panel and then ‘Add job’. This will open a dialog where you can specify details about the job, including its name, IAM role, and type. After you’ve filled out the necessary information, you click ‘Next’.

Here is where you would configure the ETL properties of the job. You can select an existing data source and target, or create new ones. You also have the option to use a proposed script generated by AWS Glue or write your own.

Once the job is created, you can manage it from the ‘Jobs’ panel. This involves monitoring its status, editing its properties, or deleting it if necessary. It’s crucial to monitor the status of your jobs to ensure they are running as expected and to troubleshoot any issues that may arise.

21. What Are Some Common Error Messages In AWS Glue And How Do You Troubleshoot Them?

Tips to Answer:

  • Understand and familiarize yourself with the AWS Glue service. Knowing how it works will help you understand the common errors that may occur and how to troubleshoot them.
  • Share specific examples of common errors in AWS Glue and how you resolved them. This shows your practical knowledge and problem-solving skills.

Sample Answer: In my experience with AWS Glue, I have encountered several common error messages. For example, I’ve often seen the “Unable to retrieve output for this job” error. This usually happens if the job doesn’t produce any output, so my first step is to check the job script and the data source to ensure everything is set up correctly.

Another common error is “Timeout while fetching the log.” This can occur if the job takes longer than the maximum timeout limit. To resolve this, I would first check the job’s runtime metrics to see if it’s taking longer than usual and then optimize the job accordingly.

These are just a couple of examples, but I would approach any error in AWS Glue by first understanding the error message, then investigating the possible causes, and finally implementing a solution based on my findings.

22. How Do You Create An IAM Role Using AWS Glue?

Tips to Answer:

  • Understand the key concepts of IAM and AWS Glue.
  • Explain the procedure in a step-by-step manner.

Sample Answer: In order to create an IAM role using AWS Glue, I follow a few simple steps. Firstly, I open the IAM console in AWS and navigate to roles, then select ‘Create role’. I then choose AWS Glue as the service that will use this role and proceed to the permissions page. Here, I attach the necessary policies that the role requires to function effectively. Once I have assigned the policies, I set a name and description for the role and review the details. If everything is correct, I create the role. Now, AWS Glue can assume this role to perform certain tasks on my behalf.

23. What Is The Next Step After Creating A Metadata Table In AWS Glue?

Tips to Answer:

  • Be specific with the process and steps after creating a metadata table. You can mention the role of crawlers in scanning the data in an AWS data store and classifying it.
  • Discuss how you can use AWS Glue jobs to make the data available for querying and analysis in data warehouses and data lakes.

Sample Answer: After creating a metadata table in AWS Glue, the next step involves creating and running a Crawler. The Crawler scans the data in an AWS data store and classifies it. It infers the schema for each data source and creates table definitions in the AWS Glue Data Catalog. Then, you can use AWS Glue ETL jobs to transform, clean, and normalize the data. With the use of AWS Glue, you can make the data available for querying and analysis in data warehouses and data lakes. It’s also possible to schedule these ETL jobs based on triggers or on-demand, depending on the requirements. This way, AWS Glue simplifies and automates data discovery, conversion, and job scheduling tasks.

24. How Do You Customize A Job In AWS Glue?

Tips to Answer:

  • Before answering this question, it’s important to clarify what the interviewer means by “customizing a job”. This could refer to changing the script generated by AWS Glue, adjusting the ETL flow, or modifying the job’s properties such as its time-out period or memory usage.
  • When explaining how to customize a job, provide a step-by-step process. For instance, you might explain how to navigate to the AWS Glue console, select a specific job, and then make changes to its script or properties. Remember to highlight the need for testing any changes to ensure the job still functions as expected.

Sample Answer: To customize a job in AWS Glue, you first need to navigate to the AWS Glue console and select the job that you want to customize. If you want to customize the script generated by AWS Glue, you can do so by selecting the ‘Script’ option within the job. Here, you can modify the Python or Scala code as needed. If you want to modify the properties of the job, such as its time-out period or the amount of memory it uses, you can do so by selecting the ‘Job parameters’ option. After making any changes, it’s crucial to test the job to ensure that it still functions as expected.

25. What Is the Security Configuration in AWS Glue?

Tips to Answer:

  • Be sure to demonstrate a clear understanding of what security configuration in AWS Glue involves, including its importance and how it’s utilized.
  • Discuss the key features of AWS Glue’s security configuration, such as encryption options and security groups.

Sample Answer: In AWS Glue, the security configuration is a crucial aspect, ensuring the safety and integrity of data. It sets up encryption options for security artifacts and protects data in transit and at rest. Data in transit is secured using Secure Sockets Layer (SSL) while data at rest is protected using AWS Key Management Service (AWS KMS) keys. In addition to this, security configurations also involve setting up Virtual Private Cloud (VPC) security groups and subnets for resources launched by AWS Glue. A security group acts as a virtual firewall, controlling inbound and outbound traffic for AWS Glue resources. Meanwhile, subnets allow the regulation of IP address ranges within VPCs. By effectively utilizing these security measures, AWS Glue can securely manage and process data.

26. How Do You Change the Schema in AWS Glue?

Tips to Answer:

  • Understand the process of changing the schema in AWS Glue, including the steps involved and the implications of doing so.
  • Explain the use cases for wanting to change the schema, giving real-world examples if possible.

Sample Answer: When using AWS Glue, there might be instances where I’d need to modify the schema. The process of changing the schema in AWS Glue involves modifying the table definition in the AWS Glue Data Catalog. I can use the AWS Glue console, the AWS CLI, or the AWS Glue API operations to do this. Before any changes, it’s crucial to consider the impact on any jobs or ETL processes that depend on the existing schema. For example, if I add new columns to the schema, I’d need to make sure that my ETL jobs are also updated to handle the new data. Similarly, if I remove columns, I would need to confirm that no existing processes rely on the data in those columns. This careful consideration ensures that the data remains consistent and reliable, and that all processes run as expected.

27. What Does AWS Glue Generate for The Proposed Script?

Tips to Answer:

  • Familiarize yourself with what AWS Glue does when a script is proposed. It’s essential to understand that AWS Glue generates Python or Scala code, which is editable and customizable to align it with your specific ETL (Extract, Transform, Load) needs.
  • Be prepared to explain the importance of this feature. The autogenerated code can be a significant timesaver, and it enables less technical users to create, run, and manage ETL jobs.

Sample Answer: When AWS Glue proposes a script, it automatically generates Python or Scala code. This function is particularly useful as it eliminates the need for manual code writing, saving time and making the process more efficient. This autogenerated code is based on the data source, target, and the transformations required to convert and move the data. Another critical aspect of this feature is that the generated code is not fixed or final; it’s editable. So if I need to modify the script to cater to specific ETL requirements, I can easily do so. This flexibility allows me to handle complex ETL jobs without starting from scratch, making AWS Glue a powerful tool for data operations.

28. What Are The Components Of The AWS Glue Data Catalog?

Tips to Answer:

  • Familiarize yourself with all the components of the AWS Glue Data Catalog. It’s important to understand what each component does and how they collectively contribute to the functionality of AWS Glue.
  • Use a real-world scenario or use case where possible to illustrate your descriptions. This shows that you can apply theoretical knowledge into practical situations, which is a quality that interviewers often look for.

Sample Answer: The AWS Glue Data Catalog is composed of several key components. Firstly, there are Tables that store metadata related to data stored in Amazon S3 or other data stores. Then, there are Databases, which are collections of Tables.

Another essential component is the Crawler. Crawlers are used to gather metadata from data sources and store it in the Data Catalog. They can be scheduled to run at specific times or triggered by events, ensuring the metadata is always up-to-date.

There are also Classifiers, which determine the schema of the data. They run on a data sample and generate a schema, which is stored in the Data Catalog.

Lastly, we have the Connections. Connections define where data is stored and how AWS Glue can access it. They are used by Crawlers and ETL jobs to read from or write to the data stores.

By understanding these components, we can effectively utilize AWS Glue Data Catalog to manage, access, and prepare data for ETL jobs.

29. What Format Is The Output Generated In AWS Glue?

Tips to Answer:

  • Firstly, be sure to have a clear understanding of AWS Glue and its capabilities. This includes knowing what kind of output formats it supports. AWS Glue supports various output formats like JSON, CSV, Avro, and Parquet.
  • Secondly, it would be beneficial to explain why a particular format might be preferred over another in certain scenarios. For example, Parquet is often used when dealing with big data due to its columnar storage format, which allows for quick querying of large datasets.

Sample Answer: As a user of AWS Glue, I can say that it supports a variety of output formats. These include popular formats like JSON, CSV, Avro, and Parquet. The choice of format depends on the specific use case and requirements. For instance, JSON is often used for its flexibility and human-readability, making it ideal for data interchange between web services. On the other hand, Parquet, a columnar storage file format, is preferred when dealing with big data. This is because it allows for efficient querying and analysis, especially when dealing with enormous datasets.

30. What Is The Importance Of AWS Glue In The Context Of Big Data And Analytics?

Tips to Answer:

  • Understand the fundamental purpose of AWS Glue. AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it easy to move data between data stores. It’s crucial in big data and analytics because it allows for the preparation and loading of data for analysis quickly and efficiently.
  • Highlight the unique features of AWS Glue that make it suitable for big data and analytics. These include its serverless architecture, flexibility, and integration with other AWS services.

Sample Answer: As a professional using AWS Glue, I’ve found it particularly significant in the context of big data and analytics. AWS Glue stands out due to its serverless architecture, meaning there’s no infrastructure to manage, which saves time and resources. This feature is critical when dealing with large volumes of data, as it allows for the automatic scaling of resources to match the workload.

Additionally, AWS Glue provides a lot of flexibility. For instance, it supports both Python and Scala, allowing analysts and data scientists to use the language they prefer. AWS Glue also integrates seamlessly with other AWS services. For example, it can easily extract data from services such as Amazon S3, Amazon RDS, and Amazon Redshift, transform it, and then load it into another AWS service for analysis. This integration makes the data preparation process more efficient and straightforward. Therefore, AWS Glue’s capabilities and features play a vital role in big data and analytics by simplifying and accelerating data extraction, transformation, and loading.


Mastering AWS Glue through thoughtful preparation and understanding of its key concepts is crucial for navigating interviews successfully. This collection of questions and answers has provided insights into various aspects of AWS Glue, from its architecture and capabilities to practical tips for implementation and optimization. By leveraging this knowledge, aspiring candidates can confidently tackle AWS Glue interviews, demonstrating their proficiency and readiness to contribute effectively in data integration and ETL processes within the AWS ecosystem. Continuous learning and hands-on experience will further solidify this foundation, ensuring professionals remain adept in harnessing AWS Glue’s power for transforming and managing data at scale.

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