Buying snapshot
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| Decision point | What AWS states |
|---|---|
Operating model | Serverless. Auto Scaling assigns workers only when a job needs them; there are no servers to provision or maintain. |
Building jobs | Code in Python or Scala on Apache Spark, the Glue Studio drag-and-drop editor, notebooks, or natural-language job creation with Amazon Q Data Integration. |
Sources and AWS services | More than 100 data sources. Integrates with Amazon Athena, Amazon EMR, Amazon Redshift and Amazon SageMaker. |
Zero-ETL replication | Replicates DynamoDB, Salesforce, SAP, ServiceNow and self-managed Oracle, MySQL, SQL Server and PostgreSQL into Amazon Redshift or the SageMaker lakehouse within minutes of source changes. |
Streaming | Streaming ETL jobs read Amazon Kinesis and Amazon MSK. AWS says Real-Time Mode reaches single-digit millisecond latency without separate streaming infrastructure. |
Table formats and access | Native Apache Iceberg, Apache Hudi and Delta Lake. Glue 5.0 and later apply table-, column- and row-level permissions to Spark jobs on those tables. |
Running pipelines | Jobs run on a schedule, on demand or on an event, with dependencies, retries and logs in Amazon CloudWatch. Git integration covers GitHub and AWS CodeCommit. |
Our take
Choose AWS Glue when your data already sits in AWS and you want Spark ETL, cataloging and data quality without running clusters. Its strongest case is the shared Data Catalog that Athena, Redshift and EMR query directly. If you need one fixed monthly price, model each meter before committing: jobs, crawlers, catalog requests and DataBrew sessions bill separately.
Evaluate AWS Glue
Comparisons
Where it stands out
One metadata layer for AWS analytics. The Data Catalog keeps table definitions, schemas and schema version history, and Redshift, Athena and EMR query from it. Lake Formation adds database- to cell-level permissions with no separate charge.
Paths beyond hand-written Spark. Glue Studio generates Python or Scala code from a visual editor, and DataBrew gives analysts a point-and-click tool with over 250 built-in transformations. Generative AI features produce Spark upgrade plans and root-cause analysis for failed jobs.
Quality checks inside the pipeline. Glue Data Quality recommends rules, monitors data and alerts when quality drops. Sensitive Data Detection finds and redacts personal data such as names, addresses and Social Security numbers.
Product screenshot
Trade-offs to weigh
Costs spread across meters and services. Compute, catalog storage and requests, DataBrew and data-quality tasks bill separately, and Glue's temporary and shuffle files in Amazon S3 are charged at standard S3 rates.
The lower Flex rate trades away start-time certainty. AWS recommends the standard class for time-sensitive jobs that need fast startup, and Flex for jobs whose start and completion times may vary.
AWS-centered by design. AWS says Glue job code runs anywhere Spark is supported, but the Data Catalog, Lake Formation permissions and zero-ETL targets are AWS services. Leaving AWS means replacing more than job code.
AWS Glue for Ray is closed to new customers. AWS's notice says it stopped accepting new customers on April 30, 2026, told interested users to sign up before that date, and points to Amazon EKS for similar capabilities.
Pricing and billing conditions
AWS prices Glue per component rather than by plan, and rates can vary by AWS Region. Compute is billed per DPU-hour, by the second; one DPU provides 4 vCPU and 16 GB of memory. Tables you store in Amazon S3 or Amazon Redshift are charged at those services' standard rates.
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| Component | Published rate | Billing condition |
|---|---|---|
ETL jobs and interactive sessions | $0.44 per DPU-hour | Billed by the second. AWS's example: a Spark job using 6 DPUs for 15 minutes costs 6 DPU × 0.25 hour × $0.44 = $0.66. Interactive sessions default to 5 DPUs. |
Flex execution class | $0.29 per DPU-hour | Rate shown in AWS's ETL data-quality example: 6 DPUs × 1/3 hour × $0.29 = $0.58, against $0.88 at the standard rate. |
Crawlers | $0.44 per DPU-hour | Billed by the second, so frequent crawler schedules add to the bill. |
Data Catalog storage | First 1 million objects free; then $1.00 per 100,000 objects per month | Objects include tables, table versions, partitions, partition indexes, statistics, databases and catalogs. |
Data Catalog requests | First 1 million requests per month free | AWS's example bills 1 million requests above the free tier at $1. |
DataBrew interactive sessions | $1.00 per 30-minute session | Billed per session, including time the console is left open. |
DataBrew jobs | $0.48 per node-hour | Billed per minute. |
Show full plan details
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| Component | Rate and conditions |
|---|---|
Iceberg optimization, statistics, materialized-view refresh | $0.44 per DPU-hour, billed per second with a 1-minute minimum per run. |
Data Quality | Data Catalog tasks need at least 2 DPUs with a 1-minute minimum; AWS's examples use $0.44 per DPU-hour. Anomaly detection there adds 1 DPU per statistic. Rules inside ETL jobs add job runtime instead, and anomaly detection in ETL jobs has no additional charge. |
Zero-ETL | No additional integration fee; you pay for source and target resources. Application ingestion is charged by data volume received, with a 1 MB minimum per request. Writes to Amazon S3 use Glue DPU-hours, billed per second with a 1-minute minimum. |
Development endpoints | Hourly rate, billed per second. |
Schema Registry | No additional charge. |
- Vendor source · Observed · Stored official pricing capture for aws-glue.
- Vendor source · Observed · Stored official pricing capture for aws-glue.
- Vendor source · Observed · Stored official pricing capture for aws-glue.
- Vendor source · Observed · Stored official pricing capture for aws-glue.
- Vendor source · Observed · Stored official pricing capture for aws-glue.
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
15.0k PyPI weekly downloads656.5k npm weekly downloads0 vulnerabilities across 2 packages
See all signals from 6 sources
npm · @aws-sdk/client-glue@3.1164.0 · PyPI · aws-glue-sessions@1.0.9
Alternatives to AWS Glue
The reviewed substitutes for AWS Glue among the ETL platforms, and what would make each one the better answer.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Fivetran
- Fivetran and AWS Glue both prepare and load data for analytics in the cloud. Fivetran is a managed service whose ready-made connectors pull from SaaS apps with no code, while AWS Glue is a serverless AWS service where engineers write and run their own ETL jobs, which suits teams that build on AWS.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
- Airbyte
- Airbyte and AWS Glue can each extract data from sources and load it for analytics. AWS Glue is a serverless AWS service with a data catalog and Spark-based transform jobs, so it fits teams whose data already lives on AWS, while Airbyte is open source with a wide catalogue of SaaS connectors and runs on any cloud.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
- Apache Airflow
- Apache Airflow and AWS Glue can both schedule ETL jobs and manage the order they run in. Glue does this with its own triggers and workflows inside a serverless AWS service, while Airflow is an open-source orchestrator whose Python DAGs coordinate jobs in any cloud or tool.Applies to: Whether pipelines are scheduled inside the ETL suite or coordinated by a separate orchestrator.
- Azure Data Factory
- The two hyperscaler serverless ETL services; the choice follows the cloud the data already sits in.Applies to: Serverless ETL and ELT pipelines inside one hyperscaler's data estate.
Head to head
See detailed alternatives analysis
AWS Glue alternatives should be evaluated by product role, architecture, pricing, public adoption signals, and operational trade-offs—not category proximity alone. AWS Glue is a serverless AWS data-integration service built around crawlers, the AWS Glue Data Catalog, and Spark-based ETL jobs. It is a strong fit when data, governance, and execution already live in AWS, but its AWS-centric design, job startup time, and development complexity can change the equation. Below, we assess three adjacent options for data engineers, analytics engineers, and data leaders choosing where pipeline responsibility should sit.
Top Alternatives Overview
Confluent
Confluent is a data streaming platform built on Apache Kafka and Apache Flink, with Confluent Cloud, an enterprise Kafka distribution, and more than 120 pre-built connectors. Its key distinction from AWS Glue is that it centers continuous data streams rather than scheduled, Spark-oriented ETL workloads and catalog-driven discovery. This is the better architectural fit when teams need to connect, process, and govern event data as it moves between systems, rather than primarily prepare batch data for a lake, warehouse, or lakehouse.
Confluent is usage-based, with Basic at $0 per month, Standard at $385 per month, Enterprise at $895 per month, and Freight at $2,300 per month; usage-based rates start at $0.01. Its 9.2/10 user rating across 27 reviews is a useful signal, but the decision should turn on streaming requirements and connector needs rather than ratings alone. Choose this if continuous event processing and a Kafka/Flink-based streaming platform are the core requirement; do not select it merely to recreate AWS Glue’s batch ETL and catalog workflow.
Fivetran
Fivetran is a managed ELT platform for automated ingestion from SaaS applications, databases, and event streams into cloud warehouses and lakes. It provides more than 600 automated connectors and handles schema evolution, incremental updates, and connector maintenance. Compared with AWS Glue, Fivetran shifts the work from building and operating transformations in Spark toward standardized, managed ingestion and downstream analytics modeling.
The trade-off is control versus operational simplicity. AWS Glue gives teams AWS-native data catalog capabilities, crawlers, and custom ETL-job execution; Fivetran is purpose-built for repeatable managed ingestion. Fivetran prices consumption by monthly active rows and has Free, Standard, Enterprise, and Business Critical plans, but its pricing page does not include plan dollar amounts. Its user rating is 8.4/10 from 54 reviews. Choose this if your priority is maintaining reliable source-to-warehouse or source-to-lake pipelines with minimal connector engineering, especially where schema changes and incremental loading would otherwise consume team time.
Azure Data Factory
Azure Data Factory is a cloud-scale data integration service for ETL and ELT pipelines across Azure and hybrid environments, with more than 100 built-in connectors. It supports code-free pipeline construction, Git integration, continuous integration and delivery, and rehosting of SSIS workloads. Its primary differentiator from AWS Glue is ecosystem alignment: it is designed for Azure and hybrid environments, whereas Glue is designed around AWS services, including AWS data stores and the AWS Glue Data Catalog.
Like AWS Glue, Azure Data Factory is a managed, pay-as-you-go service that scales on demand. The practical distinction is not that one eliminates pipeline operations, but that each reduces friction in its native cloud environment. Azure Data Factory is the clearer choice for teams operating Azure-based or hybrid data estates and those that need SSIS rehosting. Choose this if Azure integration, hybrid movement, or code-free orchestration with Git-backed delivery matters more than AWS-native catalog and Spark-job workflows.
Architecture and Approach Comparison
AWS Glue uses a serverless AWS architecture for discovering, preparing, integrating, and loading data. Its operating model combines crawlers, a centralized Data Catalog, visual pipeline creation and monitoring, and Apache Spark ETL jobs. AWS states that Glue can discover and connect to more than 100 diverse data sources, and its product materials emphasize loading data into data lakes, warehouses, and lakehouses. It also supports built-in generative AI capabilities for modernizing Apache Spark jobs.
Confluent takes a different approach: it is a unified data streaming platform based on Apache Kafka and Apache Flink. Its technical center is continuous stream connection, processing, and governance, supported by 120+ connectors. We recommend Confluent over AWS Glue when the architecture needs an always-moving stream of events; Glue is the better fit when scheduled Spark ETL, AWS metadata management, and AWS data destinations are the main workload.
Fivetran is managed ELT ingestion, with automated connectors, incremental updates, schema evolution, and connector maintenance. It is best when teams want standardized ingestion rather than a broad Spark transformation environment. Azure Data Factory is a managed pipeline service oriented around Azure and hybrid integration, with code-free pipeline building, Git, CI/CD, and SSIS rehosting. For AWS-centered processing, Glue remains coherent; for Azure or hybrid integration, Azure Data Factory has the more natural operating model.
Pricing Comparison
AWS Glue uses a freemium, usage-based, and open-source pricing model. AWS bills crawlers and ETL jobs at an hourly rate billed by the second, rather than by gigabytes scanned. Data Catalog storage and access carry a monthly fee after the free allowance, while development endpoints are billed hourly by the second. The first million Data Catalog objects and first million monthly requests are free; pricing varies by AWS Region.
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| Tool | Pricing model | Published pricing from the public documentation |
|---|---|---|
| AWS Glue | Freemium, usage-based, open-source | $0.44, $0.66, $0.88, $1.00, $0.07, $3.00, $0.40, $0.48, $0.37, $0.73, $0.29, and $0.58 listed across pricing components; first 1 million catalog objects and accesses are free |
| Confluent | Usage-based | Basic: $0/month; Standard: $385/month; Enterprise: $895/month; Freight: $2,300/month; usage starts at $0.01 |
| Fivetran | Usage-based | Monthly-active-row consumption; Free, Standard, Enterprise, and Business Critical plans; contact for pricing details |
| Azure Data Factory | Usage-based | Pipeline orchestration: $1 per 1,000 activity runs; data movement: $0.25 per DIU-hour; data flow execution: $0.273 per vCore-hour (General Purpose, Central US) |
The key decision is cost predictability at the workload level. Glue costs follow crawler, ETL, catalog, development, and related service use; Confluent combines named plan levels with consumption; Fivetran ties costs to active rows; and Azure Data Factory meters orchestration and execution. We recommend modeling real pipeline volumes before treating any of these as lower-cost by default.
When to Consider Switching
Consider moving away from AWS Glue when AWS-native Spark ETL is no longer the best match for the work. Third-party user feedback praises its AWS service integration, S3 integration, Data Catalog, triggers, scalability, serverless model, schema updates, Jupyter Notebook compatibility, and code generation. However, the same feedback identifies material limitations: job startup time is described as five to eight minutes, setup can be complex, technical support can be challenging, and the service is AWS-exclusive rather than multi-cloud. Users also cite expense and limited language support to Python and Scala.
For teams whose principal problem is real-time data movement, we recommend Confluent over AWS Glue. For teams overwhelmed by SaaS, database, or event-source connector upkeep, Fivetran is the stronger operational choice. For Azure or hybrid estates, Azure Data Factory should be evaluated before expanding Glue usage. AWS Glue also has a user rating of 8.6/10 across 42 reviews, with reported strengths including its command-line interface, online availability, resources, data handling, and ease of use. Yet user-reported weaknesses include challenges for non-developers and development tools—signals that implementation experience can vary significantly by team skills.
Migration Considerations
A move from AWS Glue begins with inventorying what is actually coupled to AWS: crawlers, Data Catalog definitions, job triggers, Spark ETL code, schemas, source and destination formats, and monitoring practices. AWS Glue’s workflow combines metadata discovery with processing, so replacing it with Fivetran requires separating managed ingestion from transformations that currently run in Glue jobs. Moving to Confluent requires a different design mindset as well: identify the event streams, connector mappings, processing responsibilities, and governance requirements that will replace batch-oriented flows.
For Azure Data Factory, assess pipeline definitions, hybrid connectivity, Git and CI/CD practices, and any SSIS rehosting needs. SQL compatibility deserves explicit attention wherever transformations are expressed outside Glue’s Spark jobs; data-format compatibility and schema-evolution behavior also affect complexity. Teams should plan for a learning curve based on the target model: streaming concepts for Confluent, managed connector and ELT operating practices for Fivetran, or Azure and hybrid pipeline administration for Azure Data Factory. The safest migration is workload-by-workload, preserving clear validation of data quality, schedules, incremental behavior, and operational ownership before retiring Glue jobs.