Airflow vs AWS Glue: Comparison of Leading Data Integration Tools for 2026

KEY TAKEAWAY

Apache Airflow excels at orchestrating and scheduling multi-system workflows with fine-grained control. However, it demands infrastructure management and Python expertise.

AWS Glue focuses on serverless ETL inside the AWS ecosystem with visual tools and automatic scaling. The downside is that it ties you to the AWS ecosystem and uses DPU-based pricing, which can be unpredictable if not managed.

They serve different purposes and often work better together than alone. You can use Airflow as the orchestrator and Glue as one of many tools Airflow might orchestrate.If you prioritize fast and reliable data movement without infrastructure, orchestration code, or unpredictable costs, Hevo Data offers a simpler, no-code alternative built specifically for scalable data integration.

Choosing between AWS Glue and Airflow often feels more difficult than it should.

Both tools are trusted choices because they solve real problems, but they fit different teams and workflows.

Airflow excels at orchestrating complex workflows across multiple systems, while AWS Glue provides serverless ETL capabilities within the AWS ecosystem.

If you manage data pipelines today, you likely want faster setup, predictable costs, and less maintenance. You also want the confidence that your connectors, scaling, and reliability needs are covered. So, how do you choose a tool that ticks all these boxes?

This guide examines Airflow vs AWS Glue by their features, use cases, pricing, and more, to help you decide.

By the end, you’ll know exactly which tool matches your specific use case and whether you should look for a better alternative to both.

Build your first data pipeline in minutes with Hevo. Start your free 14-day trial today!

Airflow vs AWS Glue vs Hevo: Detailed Comparison Table

	Try Hevo for Free
Core functions	No-code, auto-healing pipelines with transparent pricing	Workflow orchestration and task scheduling	Serverless ETL service
Ease of use	Easy	Technical, code-first	Moderate
Connectors	150+	80+ providers	80+
Architecture	Fully managed SaaS	Modular, distributed	Serverless
Real-time sync	Real-time streaming		Batch and streaming jobs with tools like Kinesis
Deployment model	Cloud SaaS	Self-hosted or managed	AWS only
Scalability	Auto-scales	Configurable auto-scaling	Auto-scales
Transformations	dbt integration, Python, and SQL	Python and external transformations	Advanced Spark ETL
Workflow orchestration	Built-in orchestration	Advanced DAGs	Basic DAG through the Workflow feature
Monitoring	Complete real-time observability	Detailed web UI logs	Amazon CloudWatch integration, built-in Spark UI
Customer support	24/7 expert support through chat or email	Community unless through managed service	Paid plans
Vendor lock-in	Low	Low	High
Security compliance	SOC 2 Type II, GDPR, HIPAA, DORA, and CPRA	Depends on deployment	ISO, SOC, PCI DSS, HIPAA, and more
Free plan			(Limited to Data Catalog)
Free trial		NA
Starting Price	$239/month	Free, infrastructure cost applies	$0.44 per DPU-hour

What is Apache Airflow?

G2 Rating: 4.4 (120)

Capterra Rating: 4.6(11)

Apache Airflow is an open-source workflow orchestration platform that helps you programmatically define, schedule, and monitor data pipelines. It was created by Airbnb and is now a widely used solution for managing complex data workflows.

The platform organizes workflows as Directed Acyclic Graphs (DAGs) to give you clear visibility into dependencies and execution order. Its modular architecture uses more than 80 providers that bundle operators and hooks to interact with external systems. Airflow is ideal for teams that want to coordinate multiple data tools and services rather than just move data.

Key features

• Task mapping: Supports programmatic task creation from external configurations or datasets for dynamic task generation that scales to hundreds of parallel tasks for variable or multi-tenant workloads.
• Flexible execution models: Runs tasks across local environments, distributed workers, or containerized setups through Airflow’s Executor framework to make it adaptable to different infrastructure sizes.
• Granular task control: Allows you to control retries, timeouts, priorities, and conditional execution. Additionally, tasks can exchange data using XComs for SLA-driven production pipelines.
• Task state management: Relies on centralized metadata storage to track task states, execution history, retries, and logs, which simplifies auditing and root-cause analysis.
• Strong monitoring: Includes a web-based interface that surfaces real-time task status, execution timelines, failures, and logs to debug without third-party tools.

Use cases

• Multi-source analytics pipelines: Manage analytics workflows where data must arrive from databases, SaaS tools, and files before transformations or reporting jobs begin.
• Machine learning workflow orchestration: Manage model training, validation, deployment, and monitoring steps that span multiple systems and tools.
• Data platform maintenance automation: Schedule recurring operational jobs such as table cleanups, partition management, backups, and archival processes.

Pricing

Airflow itself is open source and free to use, but operating it requires spending on infrastructure, storage, and maintenance. You can also opt for managed offerings such as Google Cloud Composer or AWS Managed Workflows for Apache Airflow. However, they add service-related costs.

Pros and cons

Pros:

• Code-first approach using Python provides unlimited flexibility for custom logic.
• Active community with thousands of contributors and extensive documentation.
• Works well across hybrid and multi-cloud environments.

Cons:

• Not designed as a processing engine.
• Self-hosted deployment demands extensive infrastructure management.
• Steep learning curve for teams not already proficient in Python and DevOps principles.

Customer review

Apache Airflow offers excellent flexibility in defining, scheduling, and monitoring complex workflows. The DAG-based approach is intuitive for data engineers, and the extensive operator ecosystem allows easy integration with various systems. Its UI makes tracking and debugging workflows straightforward, and its scalability ensures smooth operation even with large pipelines.

Rahul D

Program Analyst

View Review

Want to explore alternatives to Airflow? Check out this detailed Talend vs Airflow comparison.

What is AWS Glue?

G2 Rating: 4.2 (195)

Capterra Rating: 4.1(10)

AWS Glue is a fully managed, serverless data integration platform from Amazon Web Services. It makes it easy for you to discover, prepare, and combine data for analytics and machine learning. Glue’s serverless architecture lets you focus on defining your ETL logic while AWS manages the underlying compute and storage.

It offers over 80 connectors, including AWS services, to speed up extraction. If you are already an AWS user and want a platform that eliminates infrastructure concerns, this is a practical choice.

Key features

• Data catalog: Provides a centralized, persistent metadata store for all your data assets, regardless of their location.
• Job authoring: Enables visual drag-and-drop job creation or fully custom PySpark and Scala development using interactive sessions.
• Managed Spark execution: Executes distributed Apache Spark transformations with auto-scaling optimized for S3, Redshift, and Lake Formation workloads.
• DynamicFrame API: Handles schema changes in semi-structured data on the fly. This helps you clean, nest, or un-nest data without the strict schema requirements of traditional Spark.
• Job bookmarks: Tracks state information between runs to prevent reprocessing and ensure consistent incremental data loads.

Use cases

• Prepare data for analytics: Standardize raw operational data into analytics-ready formats while registering datasets in a central data catalog for downstream teams.
• Support data lake ingestion: Continuously ingest and normalize new datasets into governed data lakes managed with AWS Lake Formation.
• Enable downstream BI workflows: Prepare clean, query-optimized datasets that power dashboards in tools like Amazon QuickSight or third-party BI platforms.

Pricing

AWS Glue’s pricing is determined by how much compute and processing time your workloads consume, rather than fixed subscription fees.

• ETL jobs and interactive sessions: Priced at $0.44 per Data Processing Unit (DPU) hour, billed per second after a one-minute minimum.
• Data Catalog: Includes one million metadata objects and requests each month at no cost. Additional usage is charged at $1.00 per 100,000 objects and per million requests.
• Crawlers: Charged at $0.44 per DPU-hour, with a minimum billing duration of ten minutes for each run.
• Data Brew: Interactive data preparation sessions are billed in 30-minute increments, while batch jobs are charged per node-hour.
• Data Quality: Pricing applies per DPU-hour for rule recommendations, evaluations, anomaly detection, and retraining activities.
• Zero-ETL: No upfront charges. Ingestion compute is billed based on data volume processed per gigabyte.

Pricing differs by region, and select Glue capabilities are eligible for a limited free trial.

Pros and cons

Pros:

• Built-in transformations support reduces development time by limiting the need for custom Spark logic.
• Automatic schema discovery through Crawlers reduces manual metadata management effort.
• You only pay for the resources used during active job execution.

Cons:

• Difficult to migrate pipelines outside of the AWS ecosystem.
• Troubleshooting Spark logs within the AWS Console can be cumbersome.
• Provisioning Spark executors can cause startup delays in jobs with short durations.

Customer review

“As a DevOps engineer supporting data engineering and analytics, I am using Glue to build automated, serverless ETL workflows. It eliminates the necessity to provision and manage infrastructure for data transformation, making it easy to scale data movement across structured and unstructured data sources. It has native integration with Amazon S3, Redshift, RDS, and Athena. It has a Data Catalog that we use as a unified metadata repository.”

Verified Author

View Review

To further explore data integration tools, check out this article on the top 5 AWS Glue alternatives.

AWS Glue vs Airflow: In-Depth Feature & Use Case Comparison

1. Architecture

When you deploy Airflow, you work with a distributed system comprising distinct components. Airflow’s Scheduler monitors your DAGs and triggers tasks when dependencies are met. Executors such as CeleryExecutor and KubernetesExecutor allow you to choose how workloads are distributed across machines or clusters.

Additionally, a web server provides your monitoring dashboard, while a metadata database tracks your workflows. This modular setup gives you control but requires you to manage each step.

AWS Glue takes a different approach with its serverless architecture. You don’t provision servers or configure components. Instead, you submit jobs and AWS handles resource allocation automatically. AWS Glue’s Data Catalog serves as a centralized metadata repository that crawlers populate automatically.

The service provisions Spark clusters on demand, runs your ETL jobs, and shuts everything down when complete. In short, you interact through the console or API without touching infrastructure.

This makes Airflow better for when you need architectural flexibility, and AWS Glue better for when you want to skip infrastructure decisions.

Airflow:

I like Apache Airflow\'s clear DAGs since they make workflows easy to understand and maintain. The scheduling feature ensures pipelines run automatically without manual effort, which is really helpful. I also appreciate the retries and monitoring, as they help quickly detect and recover from failures. Additionally, its scalability is a significant advantage, allowing me to handle growing data workloads reliably, making Airflow dependable for production pipelines. Overall, these features really enhance my experience with Apache Airflow.

Raghavendra R

Data engineer

View Review

AWS Glue:

AWS Glue is an ETL service provided by aws which is easy to use and implement. It can be integrated with many services like CLI or SDK allowing its more frequency in usage. It comes with various features that is provided by the AWS like that of availability etc. The customer support of AWS is always satisfying and helps you with cost reduction and sustainable usage of resources.

Rajat J.

Corporate Trainer

2. Ease of use

Airflow demands an understanding of how workflows work internally. You configure the scheduler, executors, and metadata storage, which gives flexibility but requires time and operational knowledge. Writing DAGs in Python offers various possibilities, but the learning curve is steep for beginners.

In comparison, AWS Glue removes most of that friction if you already work inside AWS. Glue Studio allows you to build jobs visually, while the platform handles provisioning behind the scenes. 

If you value simplicity and speed over customization, Glue feels easier to start with. However, Airflow remains a strong choice for maximum pipeline control.

Airflow:

What I like best about Apache Airflow is how it lets me orchestrate complex data pipelines in a very structured way. In supply chain demand planning, we deal with multiple data sources – sales, inventory, production, and even external signals like holidays or weather. Airflow makes it easier to schedule, monitor, and re-run these workflows without too much manual hassle. I also like the visibility it gives through the UI; it helps to quickly catch when a task is failing and why. For me, this saves a lot of time compared to writing ad hoc scripts and cron jobs.

Abhishek K

Senior Analyst (Retail)

View Review

AWS Glue:

AWS Glue offers a user-friendly interface and a range of tools that make it relatively easy to set up and manage data integration workflows. The graphical interface for creating ETL jobs simplifies the process, allowing users to define data sources, transformations, and targets with little to no coding.

Nausheen A

Big Data Engineer

View Review

3. Scalability

Airflow scales well when you plan for it. Using CeleryExecutor or KubernetesExecutor allows you to expand capacity across workers, but you remain responsible for tuning, monitoring, and cost control. Scaling decisions stay in your hands, which works well for predictable workloads.

AWS Glue, on the other hand, scales automatically based on data size and job complexity, without extra configuration. The tradeoff is less control over exactly how resources are allocated, but you save the operational effort.

Airflow is a good choice for fine-grained control over scaling behavior if you have the expertise to manage it. Glue is ideal when you want scaling to happen without your involvement.

Airflow:

For me, the standout feature is definitely the Web UI. As a data engineer, I often find myself troubleshooting, and the Grid view in Airflow makes it remarkably simple to identify exactly where a pipeline has failed. I can quickly access the logs for any specific task and determine what went wrong within seconds. This level of transparency is something that traditional cron jobs or basic scripts simply don\'t offer. Having a central dashboard for all your workflows truly provides peace of mind.

Aindrila R

Assistant System Engineer, Computer Software

View Review

AWS Glue:

AWS Glue is a fully managed service that automatically scales based on the workload. It can handle large volumes of data and adjust resources accordingly, ensuring efficient and reliable data processing.

APOORV G.

Software Engineer

View Review

4. Cost

Airflow’s costs depend entirely on how you deploy it. Self-managed setups require infrastructure, monitoring, and ongoing maintenance, which adds operational overhead. Managed services like Amazon Managed Workflows for Apache Airflow (MWAA) charge baseline costs that continue even when no jobs are running.

AWS Glue follows a pay-per-use model based on DPU hours, crawlers, and catalog storage. This works well for smaller workloads, but costs can accumulate quickly at scale.

Airflow supports steady workloads where always-on infrastructure makes economic sense. If you have intermittent jobs and want to pay only for actual execution time, AWS Glue is the affordable choice.

Airflow:

Airflow can be a bit challenging to set up and configure initially, especially when deploying in production with multiple workers and schedulers. Resource management and scaling sometimes require additional tuning, and debugging can be tricky for new users.

Aditya R

Sofware Development Engineer

View Review

AWS Glue:

Cost-effective: AWS Glue charges on a per-second basis, which means you only pay for the time and resources you use. This makes it a cost-effective solution for small and large-scale data processing jobs.

Hümeyra

Site Coordinator

View Review

5. Limitations

Every tool has constraints you should understand before committing.

Airflow demands significant upfront investment. You configure multiple components, manage dependencies, handle upgrades, and troubleshoot system issues. The learning curve is steep for teams new to workflow orchestration or Python development.

AWS Glue’s limitations include its tight AWS coupling and Spark-centric design. You can’t easily move Glue jobs to other clouds or on-premises environments. While Glue Studio simplifies basic ETL, complex transformations still require knowledge of PySpark. Additionally, costs can escalate quickly if job configurations aren’t optimized carefully.

Evaluate whether Airflow’s operational complexity or Glue’s AWS dependency better aligns with your team’s capabilities before making a choice.

Why is Hevo a better choice than Airflow and AWS Glue?

If your goal is to move data quickly without managing infrastructure, writing orchestration code, or worrying about unpredictable costs, Hevo Data offers a far more practical path than Apache Airflow or AWS Glue.

Here’s what makes it worth considering:

• Simple to use: The no-code interface lets you build, monitor, and scale pipelines in minutes, without DAGs, Spark jobs, or operational setup.
• Built for reliability: Auto-healing pipelines, intelligent retries, and automatic schema handling ensure data keeps flowing even when sources change or fail.
• Wide connectivity: Connects to 150+ ready-to-use integrations with no maintenance overhead. Hevo focuses purely on data movement rather than workflow engineering.
• Complete visibility: Unified dashboards, detailed logs, data lineage, and real-time alerts give you full clarity into pipeline health and data movement at every stage.
• Effortless scalability: Automatic scaling handles spikes in data volume without the need to tune workers, manage clusters, or re-architect pipelines.
• Predictable pricing: Offers a predictable tier-based pricing model that starts at $239/month.
• Human support: Provides 24/7 expert assistance to users during and after pipeline setup.

While Airflow excels at complex orchestration and AWS Glue fits deeply into AWS-centric ETL workloads, Hevo is specially curated for teams that want speed, reliability, and clarity.

Want to know how Hevo can make a difference to your team?  Talk to a Hevo expert with a free demo.

FAQs

Nitin Birajdar Lead Customer Experience Engineer

Nitin, with 9 years of industry expertise, is a distinguished Customer Experience Lead specializing in ETL, Data Engineering, SAAS, and AI. His profound knowledge and innovative approach in tackling complex data challenges drive excellence and deliver optimal solutions. At Hevo Data, Nitin is instrumental in advancing data strategies and enhancing customer experiences through his deep understanding of cutting-edge technologies and data-driven insights.