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Asad S., AWS Data Engineer

Last Updated: 4 Jul 2023

- More than 8 years of Data Engineering experience in the Banking and Health sector. - Worked on Datawarehousing and ETL pipeline projects using AWS Glue, Databrew, Lambda, Fivetran, Kinesis, Snowflake, Redshift, and Quicksight. - Recent project involves loading data into Snowflake using Fivetran connector and automation of pipeline using Lambda and Eventbridge. - Performed Cloud Data Migrations and automation of ETL pipeline design and implementations. - Fluent English - Available from 18.08.2022

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Amazon Web Services (AWS)

Amazon Web Services (AWS)

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Yaroslav M., Scala Software Engineer with Cloud & Data Engineering skills

Ternopil, Ukraine
Last Updated: 4 Jul 2023

- Professional engineer with proven ability to develop efficient solutions for complex problems, including cloud and Data projects; - Microservice architecture expertise Lightbend Reactive Architecture, Infrastructure as Code expertise in AWS CloudFormation, CI/CD (Gitlab, AWS CodePipeline), Cloud expertise - AWS; -Engineer with the ability to develop efficient solutions for complex problems, including cloud projects, AWS Services (Amazon Quicksight, EC2, S3, Glue), Databricks, Kinesis; - API development RESTful, Swagger, GraphQL, API Gateway, Microservice architecture expertise - Commercial experience in IT since 2013; - Lightbend Reactive Architecture, Infrastructure as Code expertise in AWS CloudFormation, CI/CD (Gitlab, AWS CodePipeline); - System level programming, OOP and OOD, functional programming; Stress on profiling and optimizing code, writing reliable code; - System-level programming, OOP and OOD, functional programming; - Profiling and optimizing JVM code; - Experience with product documentation and supporting products; - Upper-intermediate English; - Available ASAP.

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Amazon Web Services (AWS)

Amazon Web Services (AWS)

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Oleg B., ML Engineer/Big Data Architect

United Arab Emirates
Last Updated: 5 Aug 2023

- Over 15 years experience in leading the design, developing, and delivery of complex IT projects and high-performance solutions, +10 years in business intelligence and in the data analytics field - Advanced hands-on experience in reactive, microservices-based, distributed system design and development including stream application platforms for advanced analytics including machine learning and data science - Proficient Data Engineer-researcher focused on the immediate benefits for the business using Big Data tools (AWS Glue, AWS Greengrass, AWS EMR, AWS Data Lake) with advanced analytical and visualization APIs (graph DB – Titan, Neo4J, Tinkerpop, software development – Scala, Python) with CI/CD pipelines – Jenkins, Circle CI, GitLab actions - Generative AI - Q&A with multiple choices, pre-trained models (Hugging Faces ecosystem, T5, BERT, GPT), ChatBot for online gambling platform (LangChain, Pinecone, Cohere, Faiss, Hugging Face Hub) - Generative AI in NLP - information retrieval for 1) generate personalized recommendations for products or services based on a user's preferences and past behavior 2) summarize legal documents and contracts, making it easier for lawyers and legal professionals to review and analyze large volumes of legal documents. 3) create content such as product descriptions, blog posts, and social media posts - Recommendations platforms - mobile games platform (generate game recommendations based on player history, promo-offers, AWS Personalize ), self-learning algorithms for data-based risk management in agriculture (Monte-Carlo tree and Markov chains) - Upper-intermediate English. - Availability starting from ASAP

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Alex K., Data Engineer

Oradea, Romania
Last Updated: 13 Nov 2023

- Senior Data Engineer with a strong technology core background in companies focused on data collection, management, and analysis. - Proficient in SQL, NoSQL, Python, Pyspark, Oracle PL/SQL, Microsoft T-SQL, and Perl/Bash. - Experienced in working with AWS stack (Redshift, Aurora, PostgreSQL, Lambda, S3, Glue, Terraform, CodePipeline) and GCP stack (BigQuery, Dataflow, Dataproc, Pub/Sub, Data Studio, Terraform, Cloud Build). - Skilled in working with RDBMS such as Oracle, MySQL, PostgreSQL, MsSQL, and DB2. - Familiar with Big Data technologies like AWS Redshift, GCP BigQuery, MongoDB, Apache Hadoop, AWS DynamoDB, and Neo4j. - Proficient in ETL tools such as Talend Data Integration, Informatica, Oracle Data Integrator (ODI), IBM Datastage, and Apache Airflow. - Experienced in using Git, Bitbucket, SVN, and Terraform for version control and infrastructure management. - Holds a Master's degree in Environmental Engineering and has several years of experience in the field. - Has worked on various projects as a data engineer, including operational data warehousing, data integration for crypto wallets/De-Fi, cloud data hub architecture, data lake migration, GDPR reporting, CRM migration, and legacy data warehouse migration. - Strong expertise in designing and developing ETL processes, performance tuning, troubleshooting, and providing technical consulting to business users. - Familiar with agile methodologies and has experience working in agile environments. - Has experience with Oracle, Microsoft SQL Server, and MongoDB databases. - Has worked in various industries including financial services, automotive, marketing, and gaming. - Advanced English - Available in 4 weeks after approval for the project

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Amazon Web Services (AWS)

Amazon Web Services (AWS)

Google Cloud Platform (GCP)

Google Cloud Platform (GCP)

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Natig, Data Engineer

Last Updated: 14 Jul 2023

- 12+ years experience working in the IT industry; - 12+ years experience in Data Engineering with Oracle Databases, Data Warehouse, Big Data, and Batch/Real time streaming systems; - Good skills working with Microsoft Azure, AWS, and GCP; - Deep abilities working with Big Data/Cloudera/Hadoop, Ecosystem/Data Warehouse, ETL, CI/CD; - Good experience working with Power BI, and Tableau; - 4+ years experience working with Python; - Strong skills with SQL, NoSQL, Spark SQL; - Good abilities working with Snowflake and DBT; - Strong abilities with Apache Kafka, Apache Spark/PySpark, and Apache Airflow; - Upper-Intermediate English.

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Python   4 yr.

Microsoft Azure

Microsoft Azure   5 yr.

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Table of Contents

Hard skills of a AWS Glue Developer

As an AWS Glue Developer, having the right hard skills is essential to excel in this role. Here are the hard skills required for Junior, Middle, Senior, and Expert/Team Lead positions:


  • AWS Glue: Proficiency in using AWS Glue for ETL (Extract, Transform, Load) processes.
  • Python: Strong knowledge of Python programming language for scripting and automation tasks.
  • SQL: Familiarity with SQL for querying and manipulating data.
  • Data Integration: Understanding of data integration concepts and experience with integrating data from multiple sources.
  • Data Modeling: Knowledge of data modeling techniques to design efficient and scalable data structures.


  • Data Transformation: Experience in transforming and cleaning data using AWS Glue’s built-in transformations and custom scripts.
  • AWS Services: Familiarity with other AWS services like S3, Redshift, Athena, and EMR for building end-to-end data solutions.
  • Data Governance: Understanding of data governance principles and practices for maintaining data quality and compliance.
  • Performance Optimization: Ability to optimize AWS Glue jobs for better performance and cost efficiency.
  • Debugging and Troubleshooting: Proficiency in debugging and troubleshooting issues related to AWS Glue jobs and data pipelines.
  • Data Security: Knowledge of data security best practices and experience in implementing data encryption and access controls.
  • Version Control: Experience with version control systems like Git for managing code changes and collaboration.


  • Advanced ETL Techniques: Expertise in advanced ETL techniques like incremental data loading, change data capture, and real-time data processing.
  • Data Warehousing: In-depth understanding of data warehousing concepts and experience in designing and implementing data warehouse solutions using AWS services.
  • Big Data Technologies: Knowledge of big data technologies like Apache Spark, Hadoop, and Hive for processing and analyzing large-scale datasets.
  • Data Pipeline Orchestration: Proficiency in orchestrating complex data pipelines using AWS Step Functions or Apache Airflow.
  • Monitoring and Logging: Experience in setting up monitoring and logging mechanisms to track the health and performance of AWS Glue jobs and data pipelines.
  • Optimization Strategies: Ability to develop optimization strategies for improving the efficiency and scalability of AWS Glue jobs.
  • Data Governance Frameworks: Familiarity with data governance frameworks and experience in implementing data governance processes and policies.
  • Leadership: Strong leadership skills to guide and mentor junior team members, and collaborate effectively with cross-functional teams.

Expert/Team Lead

  • Architecture Design: Proficiency in designing scalable and fault-tolerant data architectures using AWS services.
  • Performance Tuning: Expertise in fine-tuning AWS Glue jobs and data pipelines for optimal performance and cost optimization.
  • Advanced Data Manipulation: In-depth knowledge of advanced data manipulation techniques like window functions, complex aggregations, and data partitioning.
  • Data Lake Solutions: Experience in building and managing data lake solutions using AWS services like AWS Glue, S3, Athena, and Glue Catalog.
  • DevOps: Familiarity with DevOps practices and experience in implementing CI/CD pipelines for deploying and managing AWS Glue jobs.
  • Machine Learning Integration: Understanding of machine learning concepts and experience in integrating machine learning models into AWS Glue workflows.
  • Performance Monitoring and Optimization: Ability to monitor and optimize the performance of complex data pipelines, and implement automated monitoring solutions.
  • Data Security and Compliance: Expertise in implementing data security and compliance controls, and ensuring adherence to industry regulations.
  • Project Management: Strong project management skills to lead and deliver complex data projects, and effectively manage resources and timelines.
  • Team Collaboration: Proven ability to collaborate effectively with cross-functional teams, provide technical guidance, and drive successful project outcomes.
  • Continuous Improvement: Commitment to continuous learning and improvement, staying updated with the latest AWS Glue features and industry trends.

Pros & cons of AWS Glue

9 Pros of AWS Glue

  • 1. Serverless Data Integration: AWS Glue is a fully managed serverless data integration service, which means you don’t have to provision or manage any infrastructure. This allows you to focus on your data rather than the underlying infrastructure.
  • 2. Data Catalog: AWS Glue provides a central metadata repository called the Glue Data Catalog. It allows you to store, discover, and search metadata about your data assets. This makes it easier to understand and analyze your data.
  • 3. ETL Automation: AWS Glue simplifies the process of Extract, Transform, and Load (ETL) by providing a visual interface for creating ETL jobs. It automatically generates the code required to transform your data, saving you time and effort.
  • 4. Scalability: With AWS Glue, you can easily scale your data processing capabilities to handle large volumes of data. It automatically scales resources based on the size of your data and the complexity of your transformations.
  • 5. Data Lake Integration: AWS Glue seamlessly integrates with Amazon S3, allowing you to build a data lake where you can store and analyze all your structured and unstructured data.
  • 6. Data Quality Checks: AWS Glue provides built-in data quality checks that help you identify and fix issues in your data. It can automatically detect missing values, data inconsistencies, and other common data quality problems.
  • 7. Data Transformation: AWS Glue supports a wide range of data transformation capabilities, including data filtering, aggregation, joining, and more. It provides a powerful set of built-in transformation functions, as well as the ability to write custom transformations using Python or Scala.
  • 8. Integration with Other AWS Services: AWS Glue integrates seamlessly with other AWS services, such as Amazon Redshift, Amazon Athena, and Amazon EMR. This allows you to easily move and transform data between different services in your data pipeline.
  • 9. Cost-effective: AWS Glue offers a pay-as-you-go pricing model, which means you only pay for the resources you use. It eliminates the need for upfront investments in hardware or software, making it a cost-effective solution for data integration.

9 Cons of AWS Glue

  • 1. Learning Curve: While AWS Glue provides a user-friendly interface for creating ETL jobs, there is still a learning curve involved in understanding its features and capabilities. Users with limited technical knowledge may require additional training or support.
  • 2. Limited Customization: Although AWS Glue offers a wide range of built-in transformations and functions, there may be cases where you require more customization. In such cases, you may need to write custom code using Python or Scala, which requires additional development effort.
  • 3. Dependency on AWS Services: AWS Glue is tightly integrated with other AWS services, which means you need to have an existing AWS infrastructure to fully leverage its capabilities. If you are not already using AWS services, you may need to invest time and resources in setting up and configuring your AWS environment.
  • 4. Performance Considerations: While AWS Glue is designed to handle large volumes of data, performance can be affected by factors such as the complexity of your transformations, the size of your data, and the configuration of your AWS environment. It is important to consider these factors when designing your data pipeline.
  • 5. Limited Support for Non-AWS Data Sources: While AWS Glue provides seamless integration with Amazon S3 and other AWS services, it may have limited support for non-AWS data sources. If you have data stored in on-premises systems or other cloud platforms, you may need to explore alternative solutions or use additional tools for data integration.
  • 6. Debugging and Troubleshooting: Like any software service, AWS Glue may encounter occasional issues or errors. Debugging and troubleshooting these issues can be challenging, especially for users who are not familiar with the inner workings of the service.
  • 7. Data Security: While AWS Glue provides built-in security features, such as encryption at rest and in transit, it is important to ensure that your data is properly secured throughout the data integration process. This may involve additional configuration and setup to meet your organization’s security requirements.
  • 8. Limited Flexibility for Complex Workflows: AWS Glue is primarily designed for ETL jobs and may have limitations when it comes to handling complex data workflows or real-time data processing. If your use case requires advanced data processing capabilities, you may need to consider other services or tools.
  • 9. Vendor Lock-in: As AWS Glue is a proprietary service offered by Amazon Web Services, using it may result in vendor lock-in. It is important to consider the long-term implications and potential migration challenges if you decide to switch to a different data integration solution in the future.

Soft skills of a AWS Glue Developer

Soft skills are essential for AWS Glue Developers to effectively communicate and collaborate with team members, stakeholders, and clients. These skills are crucial for their success in delivering high-quality solutions and driving successful outcomes.


  • Effective Communication: Ability to clearly and concisely convey information and ideas to team members and stakeholders.
  • Teamwork: Willingness to collaborate with others, share knowledge, and contribute to the success of the team.
  • Problem-Solving: Capacity to analyze and resolve issues, finding innovative solutions to challenges.
  • Adaptability: Flexibility in adapting to changing requirements and priorities in a dynamic work environment.
  • Attention to Detail: Ability to pay close attention to details, ensuring accuracy and completeness in tasks and deliverables.


  • Leadership: Ability to take ownership of tasks, guide junior team members, and provide mentorship.
  • Time Management: Skill in prioritizing tasks, managing deadlines, and delivering work on schedule.
  • Client Relationship Management: Capacity to build and maintain strong relationships with clients, understanding their needs and providing excellent customer service.
  • Conflict Resolution: Ability to identify and address conflicts within the team or with stakeholders, finding mutually beneficial resolutions.
  • Critical Thinking: Capacity to analyze complex problems, evaluate options, and make informed decisions.
  • Innovation: Ability to think creatively and propose innovative ideas to improve processes and solutions.
  • Continuous Learning: Willingness to stay updated with the latest technologies and industry trends, continuously improving skills and knowledge.


  • Strategic Thinking: Capacity to align technical solutions with business objectives, considering long-term goals and scalability.
  • Project Management: Skill in planning, organizing, and executing projects, ensuring successful delivery within scope, time, and budget.
  • Client Engagement: Ability to engage with clients at a strategic level, understanding their business needs and providing strategic guidance.
  • Negotiation: Proficiency in negotiating contracts, agreements, and partnerships, achieving favorable outcomes for the organization.
  • Change Management: Skill in managing and leading teams through organizational changes and transitions.
  • Empathy: Ability to understand and empathize with team members, stakeholders, and clients, fostering positive relationships and collaboration.
  • Presentation Skills: Capacity to effectively present technical concepts and solutions to non-technical audiences.

Expert/Team Lead

  • Strategic Leadership: Capacity to provide strategic direction, mentor team members, and drive the success of the team.
  • Business Development: Ability to identify and pursue business opportunities, contributing to the growth and profitability of the organization.
  • Thought Leadership: Skill in establishing oneself as a thought leader in the field, sharing knowledge and insights through publications, presentations, and industry engagement.
  • Team Management: Proficiency in managing and developing a high-performing team, fostering a positive and productive work environment.
  • Stakeholder Management: Ability to build and maintain strong relationships with key stakeholders, influencing and aligning their expectations.
  • Strategic Partnerships: Skill in forming strategic partnerships with other organizations, driving innovation and collaboration.
  • Decision-Making: Capacity to make complex decisions, considering multiple factors and balancing short-term and long-term goals.
  • Conflict Resolution: Expertise in resolving complex conflicts and managing challenging situations with stakeholders and clients.
  • Communication Skills: Excellent verbal and written communication skills, including the ability to effectively communicate complex technical concepts to various audiences.
  • Executive Presence: Ability to confidently interact with senior executives and present ideas and solutions at an executive level.
  • Continuous Improvement: Commitment to driving continuous improvement initiatives within the team and organization, enhancing processes and outcomes.

TOP 10 AWS Glue Related Technologies

  • Python

    Python is one of the most popular programming languages for AWS Glue software development. It offers a simple syntax and extensive libraries, making it ideal for data processing and transformations. With its rich ecosystem and strong community support, Python enables developers to efficiently write and maintain Glue jobs.

  • Apache Spark

    Apache Spark is a powerful open-source framework widely used with AWS Glue. It provides a distributed computing environment for processing large datasets. Spark’s in-memory processing capabilities and support for various programming languages make it a go-to choice for big data processing and analytics in Glue.

  • AWS Glue DataBrew

    AWS Glue DataBrew is a visual data preparation tool that simplifies the process of cleaning and transforming data. It offers an intuitive interface for data wrangling tasks, allowing developers to easily explore, clean, and normalize data before further processing in Glue.

  • Apache Hive

    Apache Hive is a data warehouse infrastructure built on top of Hadoop. It enables developers to query and analyze large datasets using a SQL-like language called HiveQL. Hive seamlessly integrates with AWS Glue, allowing developers to leverage its powerful querying capabilities for data processing and analysis.

  • AWS Glue Studio

    AWS Glue Studio is a visual interface for building, running, and monitoring Glue ETL (Extract, Transform, Load) jobs. It provides a low-code environment with pre-built templates and connectors, enabling developers to quickly create and deploy data processing workflows without writing extensive code.

  • Apache Parquet

    Apache Parquet is a columnar storage file format widely used for efficient data storage and processing in AWS Glue. It offers compression, schema evolution, and predicate pushdown capabilities, making it an optimal choice for handling large datasets with improved query performance.

  • AWS Glue Data Catalog

    AWS Glue Data Catalog is a fully managed metadata repository that stores and organizes metadata about data sources, transformations, and targets used in Glue jobs. It provides a centralized location for managing and discovering data assets, simplifying data governance and ensuring data consistency across Glue workflows.

TOP 15 Tech facts and history of creation and versions about AWS Glue Development

  • AWS Glue Development was launched in 2017 as part of Amazon Web Services’ portfolio of data integration and ETL (Extract, Transform, Load) tools.
  • It was created by Amazon and designed to simplify the process of preparing and transforming data for analytics and data-driven applications.
  • Glue Development is based on the Apache Spark and Apache Hive open-source frameworks, leveraging their capabilities for large-scale data processing and querying.
  • With Glue Development, developers can define data transformation scripts using Python or Scala, allowing for flexibility and ease of use.
  • One of the key features of AWS Glue Development is its ability to automatically generate ETL code based on data schema and metadata analysis, reducing manual effort and speeding up development time.
  • Glue Development supports data integration from various sources, including Amazon S3, Amazon RDS, Amazon Redshift, and on-premises databases, making it a versatile tool for handling diverse data types.
  • In 2019, AWS Glue Development introduced the concept of Glue DataBrew, a visual data preparation tool that allows users to clean and normalize data without writing any code.
  • Glue DataBrew provides a user-friendly interface for data wrangling tasks, such as data cleansing, normalization, and data type conversion, making it accessible to non-technical users as well.
  • AWS Glue Development offers a serverless architecture, where developers do not have to provision or manage the underlying infrastructure, allowing them to focus solely on data transformation logic.
  • Glue Development provides automatic scaling capabilities, enabling it to handle large volumes of data and adapt to changing workloads without manual intervention.
  • It offers integration with other AWS services, such as AWS Lambda, Amazon CloudWatch, and Amazon S3, allowing for seamless data processing and monitoring within the AWS ecosystem.
  • AWS Glue Development supports both batch and streaming data processing, enabling real-time data transformations and analysis.
  • Glue Development provides built-in data cataloging and discovery features, making it easy to search and explore data assets across various sources.
  • AWS Glue Development has multiple pricing options, including pay-as-you-go and reserved capacity, allowing users to choose the most cost-effective model based on their usage patterns.
  • Glue Development has regular updates and new features added, ensuring that it keeps up with the evolving needs of data integration and ETL processes.

How and where is AWS Glue used?

Case NameCase Description
Data Lake CreationWith AWS Glue, you can easily create and manage a data lake by organizing, cataloging, and transforming your data from various sources into a consistent format. It enables you to extract, transform, and load (ETL) your data into Amazon S3, making it accessible for analytics and processing.
Data TransformationAWS Glue provides a powerful set of tools for transforming data. It allows you to define and execute ETL jobs to clean, enrich, and transform your data. You can apply various transformations, such as filtering, aggregating, joining, and sorting data, to prepare it for analysis or consumption by downstream applications.
Data CatalogingWith AWS Glue’s data cataloging capabilities, you can automatically discover, classify, and catalog your data assets. It creates a centralized metadata repository that enables easy search, exploration, and understanding of your data. This helps in improving data governance, data lineage tracking, and compliance with regulations.
Data IntegrationAWS Glue simplifies the process of integrating data from different sources. It supports a wide range of data formats and data sources, including relational databases, data warehouses, and cloud storage services. You can use Glue to extract data from these sources, transform it, and load it into a target data store for further analysis.
Real-time Data StreamingBy leveraging AWS Glue streaming capabilities, you can process and analyze streaming data in real-time. Glue supports integration with Amazon Kinesis Data Streams, allowing you to capture, transform, and load streaming data for immediate analysis or storage. This is particularly useful for applications that require low-latency processing of real-time data.
Data Pipeline AutomationAWS Glue simplifies the process of building and managing data pipelines. It provides a visual interface to create and schedule ETL workflows, making it easy to orchestrate complex data transformations. Glue also integrates with other AWS services, such as AWS Step Functions and AWS Lambda, to enable serverless and event-driven data processing.
Data Quality AssuranceWith AWS Glue’s built-in data quality features, you can ensure the integrity and accuracy of your data. Glue allows you to define and enforce data quality rules, perform data profiling, and monitor data quality metrics. This helps in detecting and resolving data anomalies, improving data reliability, and maintaining high data quality standards.
Machine Learning Data PreparationAWS Glue provides capabilities for preparing data for machine learning (ML) workflows. You can use Glue to clean, transform, and enrich your data, making it suitable for ML model training. Glue integrates with Amazon SageMaker, allowing you to seamlessly prepare and provision data for ML experiments and model deployment.
Serverless Data ProcessingBy leveraging AWS Glue’s serverless architecture, you can eliminate the need for managing infrastructure and scale seamlessly based on demand. Glue automatically provisions and scales compute resources as required to process your data. This enables cost optimization, improved agility, and reduced operational overhead in data processing tasks.
Data MigrationAWS Glue simplifies the process of migrating data from on-premises data sources to the cloud. It provides tools and services to extract data from legacy systems, transform it into a cloud-compatible format, and load it into AWS services. Glue’s data migration capabilities help organizations accelerate their cloud adoption journey while minimizing disruption to ongoing operations.

What are top AWS Glue instruments and tools?

  • AWS Glue DataBrew: AWS Glue DataBrew is a visual data preparation tool that enables you to clean and normalize data without writing any code. It was launched in 2020 and is designed to simplify the process of data preparation by providing a user-friendly interface and a wide range of built-in transformations. DataBrew is widely used by data analysts and data scientists to explore, clean, and transform data before loading it into AWS Glue for further processing.
  • AWS Glue Data Catalog: The AWS Glue Data Catalog is a fully managed metadata repository that stores and organizes metadata about data sources, transformations, and targets. It provides a central location to store and manage metadata, making it easier to discover, understand, and govern your data assets. The AWS Glue Data Catalog has been available since the launch of AWS Glue in 2017 and is a critical component of the AWS Glue service.
  • AWS Glue Crawlers: AWS Glue Crawlers are automated tools that scan and analyze your data sources to infer schema and generate metadata. They can discover and catalog data from various sources such as Amazon S3, Amazon RDS, and Amazon Redshift. AWS Glue Crawlers save time and effort by automatically understanding the structure and format of your data, which is essential for data integration and data transformation tasks.
  • AWS Glue ETL: AWS Glue ETL (Extract, Transform, Load) is a serverless data integration service that allows you to prepare and load your data for analytics. It offers a fully managed environment for running ETL jobs at scale, without the need to provision or manage infrastructure. AWS Glue ETL supports various data sources and provides a rich set of transformations, making it easier to transform and cleanse your data before loading it into a data warehouse or data lake.
  • AWS Glue Job: AWS Glue Job is a serverless compute resource that runs your ETL code or data transformation scripts. It allows you to define and schedule ETL jobs that transform data from one format to another or perform complex data transformations. AWS Glue Jobs can be written in Python or Scala and can be executed on a schedule or triggered by events. They provide a flexible and scalable way to process data in AWS Glue.
  • AWS Glue Studio: AWS Glue Studio is a visual interface for building and running AWS Glue ETL jobs. It simplifies the process of creating ETL workflows by providing a drag-and-drop interface to design data transformations. AWS Glue Studio automatically generates the underlying code for the ETL job, allowing users to focus on the logic and business rules. It was introduced in 2021 and has gained popularity among data engineers and developers for its ease of use and productivity.
  • AWS Glue DataBrew: AWS Glue DataBrew is a visual data preparation tool that enables you to clean and normalize data without writing any code. It was launched in 2020 and is designed to simplify the process of data preparation by providing a user-friendly interface and a wide range of built-in transformations. DataBrew is widely used by data analysts and data scientists to explore, clean, and transform data before loading it into AWS Glue for further processing.

Cases when AWS Glue does not work

  1. When the input data is not in a supported format: AWS Glue supports various file formats such as CSV, JSON, Parquet, and Avro. If the input data is in an unsupported format, AWS Glue may not be able to process it effectively.
  2. When the input data is too large: AWS Glue has certain limitations on the size of input data it can handle. If the data exceeds these limits, AWS Glue may fail to process it or result in slow performance.
  3. When there are connectivity issues: AWS Glue relies on network connectivity to access the required resources such as data sources, data targets, and AWS services. If there are network issues or restrictions, AWS Glue may not be able to function properly.
  4. When there are insufficient compute resources: AWS Glue uses a distributed processing model to handle large-scale data transformations. If the allocated compute resources are insufficient for the workload, AWS Glue may experience performance degradation or failure.
  5. When there are incompatible schema changes: AWS Glue relies on a defined schema to process and transform data. If there are incompatible schema changes in the input data or the target schema, AWS Glue may not be able to perform the required operations.
  6. When there are authorization or permission issues: AWS Glue needs appropriate permissions to access data sources, write to data targets, and interact with other AWS services. If the required permissions are not set correctly, AWS Glue may encounter authorization errors.
  7. When there are software compatibility issues: AWS Glue relies on specific versions of software components, such as Apache Spark and Python. If there are compatibility issues between the software versions or conflicts with other installed software, AWS Glue may not work as expected.
  8. When there are resource limitations: AWS Glue operates within the resource limits defined by AWS. If the workload exceeds these limits, AWS Glue may not be able to handle it efficiently or may fail to execute certain operations.

It is important to note that while AWS Glue is a powerful and flexible data processing service, it may encounter limitations or issues in certain scenarios. It is recommended to review the AWS Glue documentation and best practices to ensure optimal usage and to address any specific requirements or challenges you may encounter.

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