Minimizing AWS Glue Costs: A Comprehensive Guide for 2024

AWS Glue is a powerful ETL service widely used for data integration and transformation. However, its pricing structure can sometimes be complex and costly, posing budgeting and cost management challenges. In this blog, we will dive deep into AWS Glue costs and offer practical strategies to optimize the expenses. Additionally, we will explore Hevo, a potential alternative that offers more transparent pricing and ease of use. This blog will guide you in making informed decisions to manage and optimize your ETL costs effectively.

What is AWS Glue, and how does it help in ETL?

AWS Glue is a fully managed service offered by Amazon Web Services that simplifies ETL tasks by automating data extraction, transformation, and loading. It is designed to handle the complexities of data integration, making it easier to prepare data for analysis.

How does it work?

AWS Glue automates the extraction of data from various sources, whether it is databases or data lakes. Once the data is extracted, AWS Glue provides tools to transform it—this could involve cleaning, enriching, or reformatting the data to meet specific requirements. Finally, the transformed data is loaded into a target system, such as a data warehouse, where it can be easily analyzed.      

The following diagram shows how data is transformed with AWS Glue in ETL workflows:

Importance of cost optimization in cloud data services.

Imagine an e-commerce company running its online store on a cloud platform. During peak shopping seasons, for example, the Black Friday sale, the platform experiences a massive increase in traffic. The company might over-provision resources to handle the load. However, once the sale ends, these additional resources are no longer needed, but they continue to run, leading to higher costs even during off-peak times.
This is where cloud cost optimization comes in. It involves carefully selecting and allocating the most appropriate cloud resources for each workload, balancing performance, cost, scalability, and security factors. The company can dynamically allocate resources based on real-time demand by implementing cloud cost optimization. For example, it can scale up resources during high-traffic periods to maintain performance and automatically scale down during low-traffic periods to minimize costs. In this way, the company maximizes its return on investment (ROI) by aligning resource allocation with actual usage, thereby increasing overall business value.

Understanding AWS Glue Pricing

Breakdown of AWS Glue Costs

The AWS Glue pricing model uses a unit called the M-DPU-Hour (which stands for “Million Data Processing Unit Hours”). The M-DPU-Hour breakdown:

• Data Processing Unit (DPU): A DPU in AWS Glue represents a certain amount of processing capacity, including CPU, memory, and network resources. Specifically, one DPU has 4 vCPUs (virtual CPUs) and 16 GB of memory.
• Per Hour: AWS Glue charges you based on the number of DPUs you use per hour. If your ETL job uses 1 DPU for one hour, that’s 1 DPU hour. 
• For instance, if your ETL job uses 2 DPUs and runs for 5 hours, you would use: 
• 2 DPUs x 5 Hours = 10 DPU-Hours
• M-DPU-Hour: AWS Glue pricing is measured per Million DPU-hours. So, when you see a cost listed for M-DPU-Hour, it refers to the price for using one million DPU-Hours. You’ll only be charged for a fraction of this unit for most small to medium-scale jobs.

Crawlers and Data Catalog Costs: AWS Glue uses crawlers to automatically scan data sources and discover schemas, storing this metadata in the Data Catalog. Costs depend on the data scanned, the time taken, and the number of objects stored. Additionally, there are charges for storage in AWS services like S3 and data transfers between AWS services or to external destinations, with costs varying by data volume and service.

Common Cost Drivers

Building on the previous explanation of AWS Glue costs, several key factors drive the overall expenses. Understanding these cost drivers is essential for managing and optimizing the AWS Glue expenditures effectively:

1. Data volume and processing time: The amount of data processed and the duration of ETL jobs significantly impact costs. Larger datasets and longer processing times will increase the number of DPU hours required, leading to higher expenses.
2. Number of Crawlers and Frequency of Use: The cost of the crawlers depends on how many are deployed and how often they run. More crawlers and frequent scans result in more data being processed and cataloged, which results in higher costs.
3. Complexity of ETL jobs and transformations: The cost of ETL jobs increases with their complexity. Jobs that involve more transformations and computations need more processing power and time, which raises the overall expense.

Strategies to Minimize AWS Glue Costs

Optimizing DPU Usage

Efficient Job Scheduling and Automation

AWS Glue offers a workflow feature allowing you to define and organize ETL steps sequentially, triggering each step by specific events. Scheduled triggers enable you to set precise times for initiating events, such as running crawlers or jobs, according to the required schedule. This ensures that the data processes are automated and executed at the right time.
Consider a scenario where you need to update the data warehouse with daily sales data. You can set up an AWS Glue workflow that includes steps such as:

1. Daily data extraction: Create a trigger to start a crawler every day at 2:00 AM to scan and catalog new sales data from the source database.
2. ETL processing: After the daily data extraction is complete, the workflow can automatically start an ETL job to clean, transform, and aggregate the sales data.
3. Loading the data: Finally, configure another step to load the transformed data into the data warehouse.

In this setup, the scheduled trigger initiates the crawler at the specified time each day, followed by the automatic execution of the ETL job and data loading process. This ensures that the sales data is consistently updated in the data warehouse without manual intervention, streamlining data operations and minimizing the risk of errors.
To avoid unnecessary costs in AWS Glue, automate job termination after completion. For instance, after the daily ETL job transforms and loads the data, configure the workflow to automatically stop the job and clean up any temporary resources used during the process. This automation helps manage costs efficiently by ensuring that you’re not paying for resources beyond their needed duration.

Managing Crawler Usage

Following are some of the best practices for scheduling crawlers:

• Frequency consideration: Adjust the frequency of crawls based on data changes, avoiding overly frequent runs to save costs.
• Batch processing: Batch similar data sources together in a single crawl to reduce the number of crawler runs.
• Use Cron scheduling: AWS Glue allows you to schedule crawlers using Cron, a job scheduler for Unix-like systems that automates repetitive tasks. When setting up Cron-based schedules, specify constraints like frequency, day of the week, and time. Be mindful of cron limitations, such as handling months with fewer than 31 days.
• Crawl validity: Remember that crawls for each crawler are valid for up to 12 months, so plan accordingly.
• Monitor and Adjust: Regularly review crawler performance and data changes, adjusting the schedule as needed for efficiency and cost-effectiveness.
• Use notifications: Set up alerts for crawler completion or issues to promptly make necessary adjustments.

Partitioning with incremental crawls can be used to reduce costs in AWS Glue. For example, consider the Black Friday sale scenario where sales data is partitioned by day. The crawler offers an option to add new partitions, making crawls faster for incremental datasets with a stable table schema. Initially, the crawler performs a complete dataset scan to capture the schema and partition structure. In subsequent scheduled crawls, it only adds new partitions to the existing tables if the schemas are consistent, without making any schema changes or adding new tables to the data catalog. This approach speeds up the crawling process and reduces costs by focusing only on new data.

Data Catalog Optimization

A data catalog is a central repository that stores metadata about datasets, helping organizations manage, search, and utilize data efficiently. To optimize the data catalog, it is essential to manage and organize it effectively. Here are a few ways to do that:

• Regularly update and organize table schemas and partitions to ensure the metadata accurately reflects the evolving data.
• Enhance query performance by managing the column statistics.
• Focus on managing only the necessary metadata to keep the data catalog lean and relevant.
• Enhance the security by using AWS Key Management Service (KMS) to encrypt sensitive metadata.

Leveraging Spot Instances and Reserved Instances

Leveraging Spot Instances and Reserved Instances can significantly reduce cloud costs when used strategically. By combining Spot and Reserved Instances, businesses can balance cost and performance, leveraging Spot Instances for unpredictable spikes and Reserved Instances for steady, predictable workloads.

1. Spot Instances offer cost savings of up to 90% by utilizing unused AWS resources, making them ideal for non-critical workloads. These instances are like renting a storage unit at a heavily discounted rate because the facility has excess space. However, if the owner finds someone willing to pay full price, you might be asked to vacate. This makes them unsuitable for mission-critical tasks but perfect for workloads that can handle interruptions, like CI/CD pipelines or containerized workloads that require occasional extra capacity.
2. Reserved Instances, on the other hand, are designed for predictable workloads that need consistent performance. By reserving resources for 1 to 3 years, customers can save up to 72% compared to On-Demand pricing. Reserved Instances are flexible and can be applied to various instances, making them a solid choice for long-term, steady-state applications. However, they require upfront commitment, and any unused capacity can lead to wasted costs. To mitigate this, AWS offers the Reserved Instances Marketplace, where organizations can sell their unused Reserved Instances.

How Can Hevo Help Reduce Overall Costs?

Simplified ETL and Data Integration: Hevo vs. AWS Glue

AWS can present financial challenges due to its complex pricing structures. Businesses often struggle with understanding and managing these costs, which can lead to unexpected expenses. Hevo offers a straightforward and cost-effective approach to data integration, making it an appealing alternative for users seeking simplicity and efficiency.
Hevo’s no-code platform addresses some of these challenges by offering a user-friendly alternative to traditional ETL solutions. With Hevo, you can seamlessly integrate and transform data without extensive technical expertise. Its intuitive interface simplifies the process, allowing you to quickly connect various data sources and destinations, accelerating data workflows.
In contrast, AWS Glue provides a powerful but more complex ETL solution. While it automates many aspects of the ETL process, such as schema discovery and job scheduling, it requires more setup and management. AWS Glue has advanced features that are well-suited for large-scale data environments but come with a steeper learning curve and potentially higher costs.

Cost-Effective Data Pipelines

SelectHub offered an in-depth analysis and comparison between AWS Glue and Hevo based on data from a comprehensive 400+ point analysis of ETL tools, user reviews, and crowdsourced data. Based on the detailed analysis of SelectHub and user feedback, here is a comparison between AWS Glue and Hevo based on a few features:

Feature	AWS Glue	Hevo
Analyst Rating	84	84
User Sentiment	Great (165 User Reviews)	Great (83 Use Reviews)
Pricing Model	Per M-DPU-Hour	Free, Starter, Professional, Business, Quote-Based
Free Trial	No	Yes
Deployement Options	Cloud Only	Cloud and on-premise
In-Person Training	Not Available	Available
24/7 Live Support	Not Available	Available
User Satisfaction Rating	85% (165 Reviews from 3 Platforms)	94% (83 Reviews from 3 Platforms)
User Recommendation	85% of users recommend AWS Glue	94% of users recommend Hevo Data

• Predictable and affordable pricing:
  Hevo has flexible pricing plans—ranging from Free to Quote-based—offering more predictable costs than AWS Glue, which charges per M-DPU-hour. This allows businesses to choose a plan that fits their budget, making Hevo a more affordable option.
• Reduced costs through efficiency:
  Hevo’s no-code platform and comprehensive support, including in-person training and 24/7 live assistance, help organizations efficiently manage and transform data. This reduces the need for costly setups or external help, leading to overall cost savings.

Refer to Hevo Documentation for Features.

Choosing the Right Tool: AWS Glue vs. Hevo

Conclusion

Choosing between AWS Glue and Hevo involves more than just technical specifications—it’s about aligning with your organization’s needs and goals.
AWS Glue excels in complex, cloud-native ETL tasks with deep AWS integration and customization, making it ideal for those heavily invested in AWS. Hevo, on the other hand, offers a cost-effective, user-friendly platform with both cloud and on-premise support and predictable pricing. It’s a smart choice for budget-conscious users seeking simplicity and efficiency in data management.
So, are you ready to streamline your data processes and choose the tool that best fits your unique needs? Will you opt for the robust capabilities of AWS Glue or the intuitive and economical solution provided by Hevo? The right choice can redefine your data strategy.

FAQ on AWS Glue Costs