AWS Glue Features and Benefits

In today’s competitive world, organizations are trying to fetch maximum value out of their data to stay ahead in the market. Designing robust data pipelines for efficient management and processing of huge amounts of data is an important part of any data strategy. Since most of the workloads today run on the cloud, choosing the right cloud service for ETL needs becomes crucial.  AWS Glue is one such service offered by AWS (Amazon web services) for ETL needs.

AWS Glue is a serverless, fully managed Data Integration tool. With AWS Glue, engineering teams can extract data from various sources, perform transformations, and prepare it for end data consumers. They can build data workflows, monitor operational efficiency for their pipelines.

In this blog post, we discuss AWS Glue features, benefits, and limitations.

Components of AWS Glue

AWS Glue offers a range of components to help users manage, transform, and prepare data. Here are the key components of AWS Glue:

• AWS Data Catalog: The AWS Data Catalog is a central repository that holds information on the schema and metadata. It makes it easy to search, query, and manage your data across various sources.
• Amazon Glue Crawlers: Crawlers scan defined data sources to identify the data structure and format. After scanning, crawlers register metadata and table definitions in the data catalog. AWS Glue crawler comes with various classifiers, but you can also define custom classifiers to handle the unique format of their data.
• AWS Glue ETL Jobs: AWS Glue Jobs process ETL tasks. These jobs can be written in Python or Scala and use Apache Spark as the underlying processing engine. These jobs can be invoked with triggers based on an event or schedule. AWS Glue supports Spark UI and has tools for managing and monitoring ETL jobs to debug potential issues.
• AWS Glue Studio: AWS Glue Studio is a visual interface provided by Glue that creates, manages, and runs ETL jobs. ETL jobs can simply be built by drag-and-drop transformations, data sources, and other operations. This feature helps you develop ETL without having deep knowledge of ETL pipelines.
• AWS Glue Workflows: AWS Glue Workflows help you create and manage ETLs and interdependent jobs. They also help you chain multiple jobs, crawlers, and triggers to achieve the desired workflow. With built-in monitoring tools, you can see the execution and performance of end-to-end pipelines.
• AWS Glue DataBrew: DataBrew is a visual data preparation tool for cleaning and normalizing data. It provides more than 250 pre-built transformations, making it easy for both technical and non-technical users to use.
• Development endpoint: Allows developers to use their preferred integrated development environment (IDE) to develop, test, and debug their ETL code.

Key Features of AWS Glue

Fully Managed ETL Service

AWS Glue is a serverless data integration service. It automatically provisions the necessary resources to required to run your ETL Jobs.  As the data volume changes, AWS Glue is capable of upscaling and downscaling resources. This allows you to focus more on developing your processing logic without worrying about the underlying infrastructure. 

AWS Glue Data Catalog

• Centralized Metadata Repository: The AWS Glue Data Catalog is a centralized metadata repository. Data crawlers help it automatically discover structures and catalogs and update metadata, making it easy for users to find data.
• Schema Versioning: AWS Glue Data Catalog supports schema versioning. It helps track and manage changes to data schemas over time. This is especially important in a dynamic multi-user environment where the structure of data may evolve.

Integrated Development Environment

• AWS Glue Studio: AWS Glue provides an integrated development environment called Glue Studio for writing custom ETL scripts in Python or Scala using Apache Spark. It allows users to create, run, and monitor ETL jobs visually. offers drag-and-drop capabilities and a user-friendly interface for development.

Job Scheduling and Automation

• Automated Job Scheduling: AWS Glue supports scheduling jobs for batch workloads. You can define cron expressions to define jobs to run at scheduled times. You can also define event-based triggers. This helps run pipelines without any manual interventions.
• Workflow Orchestration: AWS Glue enables the orchestration of complex ETL workflows by chaining together multiple jobs, crawlers, and triggers. This allows users to define end-to-end data processing pipeline flow.

Broad Data Source and Destination Support

• Extensive Connectivity: AWS Glue supports a wide range of data sources and destinations, including Amazon S3, Amazon RDS, Amazon Redshift, Amazon Sagemaker, etc. It also supports connection to various third-party databases and data stores hosted on AWS. 
• Data Lake Integration: AWS Lake Formation can be integrated with AWS Glue to manage and access data lakes. AWS Glue catalog and AWS Lake Formation can work together to manage access to lake tables.

Transformations and Data Processing

• Built-in Transformations: AWS Glue Databrew comes with a broad range of built-in transformations that make it easy to clean, format, and enrich your data without the need to write any code. These transformations can be applied directly within the ETL job, simplifying the process of preparing data for analysis.
• Custom Transformations: In addition to built-in transformations, AWS Glue allows users to write custom transformations in Python or Scala. It uses Apache Spark as an underlying processing engine to give flexibility in performing advanced data manipulations.

Advanced Features and Capabilities

AWS Glue also provides some out-of-the-box features. Below we take a look at some of the advanced features and capabilities of Glue.

AWS Glue Crawlers

• Automated Data Discovery: AWS Glue Crawlers can automatically scan data sources to infer the schema and structure of your data. After scanning crawler registers or updating table definitions, the AWS Glue Data Catalog ensures that metadata always stays up to date.
• Custom Classifiers: While AWS Glue ships with a wide range of pre-built classifiers. It also allows you to define custom classifiers. This gives you complete freedom and control to define your data strategy.
• Continuous Metadata Updates: Crawlers can also be scheduled at regular intervals to keep the data catalog updated. On each run, the crawler identifies new data or changes in existing sources and creates a version of the schema.

Machine Learning Integration

• Integration with Amazon SageMaker: AWS Glue integrates seamlessly with Amazon SageMaker. It can process and prepare data before feeding it into SageMaker for training and testing models.
• ML Transforms: Machine learning transforms can be used to automatically clean and deduplicate data to improve accuracy with predictive analytics models.

Security and Compliance

• Fine-Grained Access Control: AWS Glue integrates with AWS Identity and Access Management (IAM) to provide fine-grained access control. You can define policies restricting access to specific data sources, tables, and ETL jobs, ensuring only authorized users can interact with sensitive data.
• Compliance Certifications: AWS Glue complies with various industry standards and certifications, such as GDPR, HIPAA, and SOC. 
• VPC Integration: AWS Glue jobs can be configured to be deployed in VPC. This gives you full power to have your data processing jobs in your private network. You can control the security of your network using security groups and defining ACLs.

Limitations of AWS Glue 

• Cold start latency: AWS Glue jobs may face latency due to cold starts. This particularly happens when job runs are infrequent or are run for the first time. 
• Near real-time processing: AWS Glue jobs are best suited for batch processing. Though with some configurations near real time processing can be achieved. Achieving Real-time processing is still a challenge with AWS Glue.
• Cost Management: AWS Glue is Serverless and incurs costs only for the time the resources are utilized. But users also need to be well-versed with the knowledge on how to monitor and choose the right resources as per their workload.
• Learning Curve: AWS Glue jobs run on Spark Environment. Hence users need to be technically well-versed with Spark and AWS services usage to work effectively with Glue.
• Limited Access to Spark Environment: AWS Glue manages to spark clusters on behalf of users for their ease. However, it provides limited access to the underlying spark environment. Thus, users have limited access to what they can configure in special cases.

How is Hevo better than AWS Glue?

Hevo is a cloud-based no-code data movement platform. While AWS Glue is a powerful data integration platform, Hevo with its distinct features performs better and caters to the limitations of AWS Glue. Here’s how Hevo might be considered better than AWS Glue :

• No code, Ease to Use: Hevo Data is designed with a strong focus on user-friendliness. Hevo offers a no-code interface that allows users to set up data pipelines with just a few clicks. 
• Extensive Pre-Built Connectors: AWS Glue offers a wide range of connectors support for the AWS ecosystem. But It has limitations and also creates vendor lock-in. Hevo Data comes with a wide range of pre-built connectors for 150+ data sources and destinations, including cloud applications, databases, and data warehouses. 
• Real-Time and Batch Data Processing: AWS Glue is good for batch processing and on certain configurations near real-time processing could be achieved but lacks support for real-time data processing pipelines. Hevo provides native support for real-time data processing, making it ideal for both Real time and batch data processing use case. 
  AWS Glue jobs might also face latency on cold starts. Hevo’s real-time processing capabilities ensure lower latency.
• 24 x 7 Support: Hevo offers dedicated 24×7 customer support. AWS Glue, being part of the larger AWS ecosystem, offers support through AWS Support plans, which can be more expensive and less personalized.
• Data Transformation Flexibility: Hevo provides a range of pre-built data transformations that are easy to apply, making it easier for non-technical users to manipulate data. Hevo allows users to write custom SQL queries for easier data transformations. 
• Transparent Pricing: Hevo’s pricing model is based on number of events processed and the number of active pipelines. This makes it easy for businesses to have clear visibility on their expenses so they can just focus on data integration and fetching maximum value out of it.

Conclusion

In this article, we discussed AWS Glue’s capabilities, purpose, and advantages. It is more suitable for organizations that need a powerful, flexible, and highly scalable ETL service with deep integration into the AWS ecosystem. On the other hand, Hevo provides a no-code, user-friendly interface that simplifies the ETL process. With Hevo, you can easily set up and manage data pipelines without having deep technical expertise, giving you more time to fetch business value.

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FAQ on AWS Glue Features