AWS Glue Best Practices: Optimize Your Data Integration Workflow


AWS Glue is a fully managed, serverless data integration service that simplifies and automates the process of extracting, transforming, and loading (ETL) data from various sources to your data lake or data warehouse. Implementing best practices for AWS Glue can improve efficiency, security, and cost-effectiveness of your data integration workflows. In this article, we will discuss various best practices for AWS Glue, categorized into different sections, and provide actionable steps on how to implement them.

Table of Contents

Why Implement AWS Glue Best Practices

Best practices are tried and tested strategies that have been proven to yield optimal results. In the context of AWS Glue, these practices are guidelines to help Glue users get the most out of this service. They provide a roadmap to navigate the complexities of data integration, making it easier to achieve your objectives.

Impact on Efficiency

Implementing best practices in AWS Glue can significantly streamline your data integration workflows. These practices can guide you in setting up your ETL jobs, managing your data catalog, and optimizing your resources, among other things. This leads to improved efficiency, as tasks are completed faster and with fewer errors, freeing up your team to focus on more strategic initiatives.

Enhancing Security and Compliance

Security and compliance are paramount in today’s data-driven world. AWS Glue best practices provide guidelines on how to secure your data and ensure compliance with various regulations. This includes practices on data encryption, fine-grained access control, and auditing AWS Glue activity. By following these practices, you can protect your sensitive data and avoid potential legal and reputational risks.

Cost-Effectiveness and Optimization

AWS Glue best practices can also lead to significant cost savings. They guide you on how to choose the right pricing model, and optimize and monitor your AWS Glue costs. By following these practices, you can ensure that you’re only paying for what you need and that your resources are used in the most efficient way possible.

Continuous Improvement and Adaptability

Best practices are not set in stone. They evolve with the changing landscape of data integration and the continuous development of AWS Glue. By keeping up with these practices, you can ensure that your data integration workflows are always at the forefront of technology. This promotes a culture of continuous improvement and adaptability, helping your organization stay competitive in the digital age.

Related Reading: AWS Glue vs Lambda: Choosing the Right Tool for Your Data Pipeline

Key Focus Areas for AWS Glue Best Practices

The best practices for AWS Glue can be categorized into five main areas, each focusing on a different aspect of the service. Following these practices can help you optimize your use of AWS Glue and achieve better results. The categories are as follows:

1. Best Practices for AWS Glue Architecture and Design

This category focuses on the architectural and design aspects of AWS Glue. Best practices in this area are:

These practices can help you design a robust and scalable AWS Glue environment.

2. Best Practices for ETL Jobs Powered by AWS Glue

This category covers the practices related to ETL (Extract, Transform, Load) jobs in AWS Glue. Best practices in this area are:

These practices can help you optimize your ETL jobs for better performance and cost-effectiveness.

3. Security and Compliance Best Practices in AWS Glue

This category focuses on the security and compliance aspects of AWS Glue. Best practices in this area are:

These practices can help you enhance the security of your AWS Glue environment and ensure compliance with various regulations.

4. Cost Optimization Best Practices in AWS Glue

This category covers the practices related to cost optimization in AWS Glue. Best practices in this area are:

These practices can help you manage your AWS Glue costs effectively.

5. Best Practices for Monitoring and Troubleshooting in AWS Glue

This category focuses on the monitoring and troubleshooting aspects of AWS Glue. Best practices in this area are:

These practices can help you monitor your AWS Glue environment effectively and troubleshoot issues quickly.

By understanding and implementing these best practices, you can optimize your use of AWS Glue and achieve better results in your data integration workflows.

AWS Glue Best Practices

1. Ensure Scalability with Proper Partitioning

Partitioning your data in AWS Glue can significantly improve query performance and reduce costs by limiting the amount of data scanned during queries. To ensure proper partitioning:

  1. Choose an appropriate partition key based on your query patterns (e.g., date, customer ID).
  2. Use AWS Glue Crawlers to automatically discover and create partitions.
  3. Regularly monitor and optimize partition sizes to maintain balance between the number of partitions and the size of each partition.

2. Use AWS Glue Catalog to Manage Metadata

The AWS Glue Data Catalog provides a centralized metadata repository for all your data assets across multiple AWS services. By using the Data Catalog, you can:

  1. Easily discover and search for datasets using schema versioning and data lineage information.
  2. Automate schema discovery and management using AWS Glue Crawlers.
  3. Integrate with other AWS services like Amazon Athena and Amazon Redshift Spectrum for seamless querying.

Get started with AWS Glue Data Catalog

3. Limit the Number of Concurrent Job Runs to Prevent Throttling

AWS Glue imposes limits on the number of concurrent job runs, which can lead to throttling if not managed properly. To avoid throttling:

  1. Use AWS Glue triggers to manage job dependencies and control the execution order.
  2. Monitor the number of concurrent job runs using Amazon CloudWatch metrics and alarms.
  3. Consider increasing the maximum concurrent runs limit by contacting AWS Support if needed.

Using AWS Glue Triggers to Manage Dependencies

AWS Glue triggers should be used to manage dependencies between jobs and control the execution order.

For example, if you have a job B that depends on the output of job A, you can set up a trigger to start job B only after job A has successfully completed. This way, you can prevent unnecessary concurrent runs and manage your resources more effectively. Here’s a sample code snippet to create such a trigger:

import boto3

client = boto3.client('glue')

response = client.create_trigger(
    Actions=[{'JobName': 'JobB'}],
        'Conditions': [
                'LogicalOperator': 'EQUALS',
                'JobName': 'JobA',
                'State': 'SUCCEEDED'

4. Choose the Right Data Format for Your Use Case

Selecting the appropriate data format can significantly impact query performance and storage costs. Some popular data formats include Parquet, Avro, and JSON. Consider the following factors when choosing a data format:

  1. Compression: Formats like Parquet and Avro provide better compression ratios, reducing storage costs.
  2. Query performance: Columnar formats like Parquet improve query performance by minimizing data scanning.
  3. Schema evolution: Avro supports schema evolution, allowing you to add, remove, or modify fields without breaking compatibility with existing data.

5. Optimize Job Performance with AWS Glue Job Bookmarks

AWS Glue job bookmarks help track processed data and ensure that only new or changed data is processed in subsequent job runs. To use job bookmarks:

  1. Enable job bookmarks in your AWS Glue ETL job settings.
  2. Ensure that your input data source supports job bookmarks (e.g., Amazon S3, JDBC).
  3. Monitor and manage job bookmarks using the AWS Glue console or API.

Related Reading: Learn more about leveraging AWS Glue ETL

6. Utilize Spark Optimizations to Improve Job Performance

AWS Glue uses Apache Spark under the hood to execute ETL jobs. Perform Spark optimizations to significantly improve your AWS Glue job performance. More specifically,

  1. Use the right Spark DataFrame APIs to perform operations like filtering and joining.
  2. Optimize Spark configurations, such as executor memory and parallelism settings, based on your specific use case.
  3. Identify performance bottlenecks by monitoring Spark application metrics using Amazon CloudWatch and the AWS Glue console and then optimize accordingly.

7. Protect Sensitive Data with AWS Glue Data Catalog Encryption

Encrypting sensitive data in the AWS Glue Data Catalog is crucial for maintaining data security and compliance. To enable encryption:

  1. Configure AWS Key Management Service (KMS) to create and manage encryption keys.
  2. Enable encryption at rest for your Data Catalog by specifying a KMS key during creation or modification of a database or table.
  3. Ensure that all users and applications have the necessary permissions to access encrypted data through IAM policies.

Understand AWS Glue Data Catalog encryption

8. Implement Fine-Grained Access Control Using AWS Glue Resource-Level Policies

Resource-level policies in AWS Glue allow you to define granular permissions for specific resources like databases, tables, and jobs. To implement fine-grained access control:

  1. Create custom IAM policies with specific resource ARNs and actions (e.g., glue:GetTableglue:StartJobRun).
  2. Attach these custom policies to IAM users, groups, or roles as needed.
  3. Regularly review and update IAM policies to ensure least privilege access.

Follow AWS IAM best practices

9. Audit and Monitor AWS Glue Activity with AWS CloudTrail

AWS CloudTrail enables auditing and monitoring of API calls made to AWS Glue, providing valuable insights into user activity and potential security incidents. To use CloudTrail with AWS Glue:

  1. Enable CloudTrail logging for your AWS account.
  2. Configure a trail to capture AWS Glue API calls, and specify an S3 bucket for storing logs.
  3. Analyze CloudTrail logs using Amazon Athena or other log analysis tools to identify suspicious activity and investigate security incidents.

10. Choose the Right Pricing Model Based on Your Workload

AWS Glue offers different pricing models to suit various workloads and budget requirements. Consider the following factors when choosing a pricing model:

  1. Pay-as-you-go: Suitable for unpredictable workloads with variable usage patterns.
  2. Savings Plans: Offer long-term commitment options for predictable workloads, providing cost savings over pay-as-you-go pricing.

Explore AWS Glue pricing options

11. Optimize Resources by Using Development Endpoints in AWS Glue

Development endpoints in AWS Glue enable interactive development and testing of ETL scripts, helping you optimize resource usage before deploying jobs. To use development endpoints:

  1. Create a development endpoint with the desired configuration (e.g., number of DPUs, VPC settings).
  2. Connect to the endpoint using a notebook or SSH client to develop and test your ETL script.
  3. Monitor resource usage during development and adjust configurations accordingly to minimize costs.

12. Monitor Your AWS Glue Costs Using AWS Cost Explorer and AWS Budgets

Regularly monitoring and managing your AWS Glue costs can help you stay within budget and optimize resource usage. Use the following tools to monitor your costs:

  1. AWS Cost Explorer: Provides detailed insights into your AWS Glue spending patterns, allowing you to identify cost drivers and potential savings opportunities.
  2. AWS Budgets: Allows you to set custom cost and usage budgets for AWS Glue, and sends alerts when your actual or forecasted costs exceed the budget thresholds.

Learn more about monitoring AWS Glue costs

13. Set up CloudWatch Alarms for Monitoring Job Failures and Performance Metrics

Set up Amazon CloudWatch to gain insights into various metrics for monitoring AWS Glue job performance and failures. To set up CloudWatch alarms:

  1. Identify critical AWS Glue metrics, such as job run status, execution time, and memory usage.
  2. Create CloudWatch alarms with appropriate threshold values for each metric.
  3. Configure alarm actions, such as sending notifications or triggering Lambda functions, to respond to specific events.

14. Use AWS Glue Logs to Identify Issues and Analyze Job Performance

AWS Glue automatically logs job run information and error messages to Amazon CloudWatch Logs. To use AWS Glue logs for troubleshooting and performance analysis:

  1. Enable logging for your AWS Glue jobs by specifying a log group and prefix in the job settings.
  2. Analyze log data using CloudWatch Logs Insights or export logs to other log analysis tools.
  3. Identify common issues, such as data type mismatches, incorrect configurations, or resource constraints, and implement appropriate fixes.

15. Utilize AWS Glue Job Metrics to Track Job Progress and Detect Anomalies

AWS Glue Job Metrics provide real-time visibility into job progress and performance, helping you detect anomalies and optimize your ETL workflows. To use Job Metrics:

  1. Enable Job Metrics in your AWS Glue job settings.
  2. Monitor key metrics like bytesReadrecordsRead, and processingTime using the AWS Glue console or CloudWatch.
  3. Identify trends and anomalies in metric data to optimize job performance and resource usage.

Best Practices Summary

CategoryBest PracticeDescriptionImplementation Difficulty
Architecture and DesignProper Data PartitioningEnsures scalability by dividing large datasets into smaller, more manageable parts.Medium
Architecture and DesignUtilize AWS Glue CatalogEfficiently manages metadata, making it easier to organize, locate, and collaborate on data.Low
Architecture and DesignLimit Concurrent Job RunsPrevents throttling by managing the number of jobs that run at the same time.Medium
ETL JobsChoose the Right Data FormatOptimizes performance and cost by selecting the most suitable data format for your use case.Low
ETL JobsUse AWS Glue Job BookmarksEnhances job performance by tracking data that has already been processed.Medium
ETL JobsLeverage Spark OptimizationsImproves job performance by utilizing Spark’s built-in optimization techniques.High
Security and ComplianceProtect Data with AWS Glue Data Catalog EncryptionEnhances security by encrypting sensitive data in the AWS Glue Data Catalog.Medium
Security and ComplianceImplement AWS Glue Resource-Level PoliciesProvides fine-grained access control to AWS Glue resources.High
Security and ComplianceUse AWS CloudTrail for AuditingMonitors AWS Glue activity for auditing and compliance purposes.Low
Cost OptimizationSelect Appropriate Pricing ModelOptimizes costs by choosing the pricing model that best fits your workload.Low
Cost OptimizationUse Development Endpoints in AWS GlueOptimizes resources by testing scripts before running jobs.Medium
Cost OptimizationMonitor Costs with AWS Cost Explorer and AWS BudgetsHelps manage AWS Glue costs by providing detailed cost insights.Low
Monitoring and TroubleshootingSet Up CloudWatch AlarmsMonitors job failures and performance metrics to quickly identify and resolve issues.Medium
Monitoring and TroubleshootingUse AWS Glue LogsIdentifies issues and analyzes job performance by examining AWS Glue logs.Low
Monitoring and TroubleshootingUtilize AWS Glue Job MetricsTracks job progress and detects anomalies by monitoring AWS Glue job metrics.Low

Do’s and Don’ts for AWS Glue


  • Use schema evolution when possible to accommodate changes in your data sources.
  • Leverage AWS Glue’s built-in connectors for seamless integration with other AWS services.
  • Continuously monitor and optimize your AWS Glue environment using available tools and metrics.


  • Avoid writing custom classifiers unless absolutely necessary.
  • Don’t overlook security best practices when designing your AWS Glue architecture.
  • Don’t forget to clean up unused resources, such as temporary tables and databases, to save costs.

Frequently Asked Questions (FAQ)

Why are best practices important in AWS Glue?

Implementing best practices for AWS Glue can improve the efficiency, security, and cost-effectiveness of your data integration workflows.

How can I prevent throttling in AWS Glue?

You can prevent throttling in AWS Glue by using triggers to manage dependencies between jobs, monitoring the number of concurrent job runs using Amazon CloudWatch, and considering an increase in the maximum concurrent runs limit by contacting AWS Support.

What are some best practices for ETL jobs in AWS Glue?

Some best practices for ETL jobs in AWS Glue include choosing the right data format for your use case, optimizing job performance with AWS Glue Job Bookmarks, and utilizing Spark optimizations to improve job performance.

How can I enhance security in AWS Glue?

You can enhance security in AWS Glue by protecting sensitive data with AWS Glue Data Catalog encryption, implementing fine-grained access control using AWS Glue resource-level policies, and auditing and monitoring AWS Glue activity with AWS CloudTrail.

What are some cost optimization best practices in AWS Glue?

Some cost optimization best practices in AWS Glue include choosing the right pricing model based on your workload, optimizing resources by using development endpoints in AWS Glue, and monitoring your AWS Glue costs using AWS Cost Explorer and AWS Budgets.

How can I monitor and troubleshoot issues in AWS Glue?

You can monitor and troubleshoot issues in AWS Glue by setting up CloudWatch Alarms for job failures and performance metrics, using AWS Glue Logs for issue identification and job performance analysis, and utilizing AWS Glue Job Metrics for tracking job progress and detecting anomalies.

What are some do’s and don’ts for AWS Glue?

Some do’s for AWS Glue include using schema evolution when possible, leveraging AWS Glue’s built-in connectors, and continuously monitoring and optimizing your AWS Glue environment. Some don’ts include avoiding writing custom classifiers unless necessary, not overlooking security best practices, and not forgetting to clean up unused resources.

What are the benefits of implementing AWS Glue best practices?

Implementing AWS Glue best practices can lead to efficient, secure, and cost-effective data integration workflows, ensuring high-performance ETL processes and robust data management.


Implementing best practices for AWS Glue is essential for achieving efficient, secure, and cost-effective data integration workflows. By following the recommendations discussed in this article, you can optimize your AWS Glue environment, ensuring high-performance ETL processes and robust data management. Remember to continuously monitor and adjust your strategies as your data integration needs evolve, leveraging the rich set of tools and features provided by AWS Glue and related services.

Discover more about AWS Glue and related topics