Decision comparison
AWS Glue vs Fivetran
AWS Glue and Fivetran serve fundamentally different data pipeline philosophies. AWS Glue delivers a full-featured serverless ETL platform with deep transformation capabilities, ideal for teams that need custom Spark-based processing within the AWS ecosystem. Fivetran provides a managed ELT platform with 700+ connectors and near-zero maintenance, built for teams that want automated data ingestion without writing pipeline code. The right choice depends on whether your priority is transformation power or connector breadth and operational simplicity.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
These are different kinds of product — ETL Platform and ELT Platform.
Quick Comparison
| Decision factor | AWS Glue | Fivetran |
|---|---|---|
| Pricing Model | Free tier covers the first million Data Catalog metadata objects stored and the first million requests per month. Paid usage is billed per DPU-hour rather than per GB scanned: Apache Spark ETL jobs, crawlers, interactive sessions, Iceberg table optimization, statistics generation and materialized-view refresh are each $0.44 per DPU-hour, billed per second with a one-minute minimum. Flex execution is $0.29 per DPU-hour. DataBrew interactive sessions are $1.00 per 30-minute session and DataBrew jobs $0.48 per node-hour. Data Catalog storage beyond a million objects is $1.00 per 100,000 objects per month. Rates are US East (N. Virginia) and vary by region. | Fivetran is consumption-priced on monthly active rows and quoted through its own estimator. The plans are Free, Standard, Enterprise and Business Critical. The Free plan covers up to 500,000 monthly active rows for connections, 3,500 for activations and 5,000 model runs. Fivetran advertises savings of up to 22% on an annual contract. No per-plan price is published. |
| Ease of Setup | Requires AWS expertise with IAM roles, crawlers, and Spark job configuration taking days to production | Point-and-click connector setup with pipelines running in under two minutes and zero coding required |
| Connector Ecosystem | Connects to 100+ AWS-native and JDBC sources including S3, RDS, DynamoDB, Redshift, and Kafka | 700+ fully managed pre-built connectors for SaaS apps, databases, ERPs, files, and event streams |
| Data Transformation | Full ETL engine with Apache Spark, Python Shell jobs, visual editor, 250+ DataBrew transforms, and custom code | ELT-first approach pushing raw data to warehouse with dbt integration and Quickstart data models for transformation |
| User Rating | Rated 8.6/10 across 42 reviews with 90% willing to recommend and strong catalog praise | Rated 8.4/10 across 54 reviews with 4.6/5 on Gartner at 300 ratings and enterprise adoption |
| Scalability | Serverless auto-scaling from gigabytes to petabytes with dynamic DPU allocation and no cluster management | Managed platform syncing 9.1+ petabytes monthly at 500+ GB/hr throughput with 99.97% uptime SLA |
AWS Glue
- Pricing Model:
- Free tier covers the first million Data Catalog metadata objects stored and the first million requests per month. Paid usage is billed per DPU-hour rather than per GB scanned: Apache Spark ETL jobs, crawlers, interactive sessions, Iceberg table optimization, statistics generation and materialized-view refresh are each $0.44 per DPU-hour, billed per second with a one-minute minimum. Flex execution is $0.29 per DPU-hour. DataBrew interactive sessions are $1.00 per 30-minute session and DataBrew jobs $0.48 per node-hour. Data Catalog storage beyond a million objects is $1.00 per 100,000 objects per month. Rates are US East (N. Virginia) and vary by region.
- Ease of Setup:
- Requires AWS expertise with IAM roles, crawlers, and Spark job configuration taking days to production
- Connector Ecosystem:
- Connects to 100+ AWS-native and JDBC sources including S3, RDS, DynamoDB, Redshift, and Kafka
- Data Transformation:
- Full ETL engine with Apache Spark, Python Shell jobs, visual editor, 250+ DataBrew transforms, and custom code
- User Rating:
- Rated 8.6/10 across 42 reviews with 90% willing to recommend and strong catalog praise
- Scalability:
- Serverless auto-scaling from gigabytes to petabytes with dynamic DPU allocation and no cluster management
Fivetran
- Pricing Model:
- Fivetran is consumption-priced on monthly active rows and quoted through its own estimator. The plans are Free, Standard, Enterprise and Business Critical. The Free plan covers up to 500,000 monthly active rows for connections, 3,500 for activations and 5,000 model runs. Fivetran advertises savings of up to 22% on an annual contract. No per-plan price is published.
- Ease of Setup:
- Point-and-click connector setup with pipelines running in under two minutes and zero coding required
- Connector Ecosystem:
- 700+ fully managed pre-built connectors for SaaS apps, databases, ERPs, files, and event streams
- Data Transformation:
- ELT-first approach pushing raw data to warehouse with dbt integration and Quickstart data models for transformation
- User Rating:
- Rated 8.4/10 across 54 reviews with 4.6/5 on Gartner at 300 ratings and enterprise adoption
- Scalability:
- Managed platform syncing 9.1+ petabytes monthly at 500+ GB/hr throughput with 99.97% uptime SLA
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | AWS Glue | Fivetran |
|---|---|---|
| Search interest(Market interest) | 1 | 1 |
| Hacker News mentions, 90d(Community interest) | 0 | 2 |
| npm weekly downloads(Developer adoption) | 323.1k | Not available |
| PyPI weekly downloads(Developer adoption) | 12.9k | 32.3k |
| Stack Overflow questions(Community interest) | 4.2k | 22 |
| GitHub commits, 90d(Developer adoption) | Not available | 23 |
| GitHub stars(Developer adoption) | Not available | 126 |
| Product Hunt comments(Community interest) | Not available | 9 |
| Product Hunt rating(Community interest) | Not available | 5.0/5 |
| Product Hunt reviews(Community interest) | Not available | 1 |
| Product Hunt votes(Community interest) | Not available | 85 |
As of September 14, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
AWS Glue
September 14, 2026Package vulnerabilities
npm · @aws-sdk/client-glue@3.1131.0 · PyPI · aws-glue-sessions@1.0.9
0 vulnerabilities
across 2 packages
Repository security score
Not available
Fivetran
September 14, 2026Package vulnerabilities
PyPI · fivetran-connector-sdk@2.12.1
0 vulnerabilities
across 1 package
Repository security score
Not available
Interface Preview
AWS Glue
Feature Comparison
| Feature | AWS Glue | Fivetran |
|---|---|---|
| Data Ingestion | ||
| Source Connectivity | Connects to 100+ sources via JDBC, S3, DynamoDB, Kafka, and custom classifiers within AWS ecosystem | 700+ pre-built fully managed connectors for SaaS, databases, ERPs, and files across all clouds |
| Change Data Capture | Supports CDC through bookmarks and DynamoDB Streams requiring manual configuration in Spark jobs | Log-based CDC replication built into database connectors with automatic incremental syncs and deduplication |
| Schema Management | Automatic schema discovery via crawlers with Schema Registry for Avro streams and Data Catalog versioning | Automatic schema evolution handling with 22.2M+ schema changes managed monthly across all connectors |
| Data Transformation | ||
| Transformation Engine | Apache Spark-based ETL with Python and Scala support plus DataBrew visual editor with 250+ built-in transforms | ELT approach landing raw data first then transforming via dbt integration with 37.7M+ model runs monthly |
| Code Development | Interactive Sessions in any IDE or notebook with code generation, Git integration, and GenAI-assisted authoring | Connector SDK for custom pipelines with REST API for programmatic control and no-code setup for standard flows |
| Data Quality | Built-in Data Quality rulesets with ML-powered deduplication via FindMatches and sensitive data detection | Idempotent pipelines with automatic data type casting, deduplication on server side, and sync health dashboards |
| Operations & Monitoring | ||
| Pipeline Orchestration | Workflows with triggers, event-based scheduling, and MWAA Airflow integration for multi-step pipelines | Automated scheduling from 1-minute to 24-hour intervals with dependency chaining via application logs |
| Monitoring & Alerts | CloudWatch integration for logs, metrics, and alerting with job duration and resource consumption tracking | Built-in reliability dashboards with sync health logs, schema change alerts, and 99.97% uptime guarantee |
| Deployment Options | Fully serverless on AWS with Flex execution class reducing costs up to 35% for non-urgent workloads | Cloud-hosted SaaS plus Hybrid Deployment option to run pipelines in your own environment across AWS, GCP, Azure |
| Security & Compliance | ||
| Encryption & Access | IAM-based fine-grained access control with encryption at rest and in transit plus Lake Formation integration | SSH tunnels, VPN tunnels, customer-managed encryption keys, and private networking in Business Critical tier |
| Compliance Certifications | Inherits AWS compliance including SOC 2, HIPAA, GDPR, PCI DSS, and FedRAMP through AWS shared responsibility | SOC 1 and SOC 2, HIPAA BAA, GDPR, ISO 27001, PCI DSS Level 1, and HITRUST certifications held directly |
| Data Governance | Centralized Data Catalog with metadata versioning, partition tracking, and Lake Formation policy enforcement | Role-based access control with SCIM provisioning, metadata sharing, and governance catalog integrations |
| Ecosystem & Integration | ||
| Cloud Platform Support | AWS-exclusive service deeply integrated with S3, Redshift, Athena, EMR, SageMaker, and Lambda | Cloud-agnostic platform supporting Snowflake, Databricks, BigQuery, Redshift, and Azure Synapse destinations |
| Analytics Tool Integration | Data Catalog shared across Athena, EMR, and Redshift Spectrum as unified metadata layer for analytics | Direct warehouse loading for Tableau, Looker, Power BI consumption with dbt-powered transformation layer |
| Reverse ETL & Activation | Requires custom Lambda or Step Functions workflows to push processed data back into operational systems | Built-in rELT activation with 200+ destinations pushing enriched warehouse data back into business applications |
Data Ingestion
Source Connectivity
Change Data Capture
Schema Management
Data Transformation
Transformation Engine
Code Development
Data Quality
Operations & Monitoring
Pipeline Orchestration
Monitoring & Alerts
Deployment Options
Security & Compliance
Encryption & Access
Compliance Certifications
Data Governance
Ecosystem & Integration
Cloud Platform Support
Analytics Tool Integration
Reverse ETL & Activation
Which approach fits
AWS Glue and Fivetran serve fundamentally different data pipeline philosophies. AWS Glue delivers a full-featured serverless ETL platform with deep transformation capabilities, ideal for teams that need custom Spark-based processing within the AWS ecosystem. Fivetran provides a managed ELT platform with 700+ connectors and near-zero maintenance, built for teams that want automated data ingestion without writing pipeline code. The right choice depends on whether your priority is transformation power or connector breadth and operational simplicity.
When each approach fits
Choose AWS Glue if:
Choose AWS Glue if your organization is already invested in the AWS ecosystem and needs powerful, customizable data transformation capabilities. AWS Glue excels when your team includes data engineers comfortable with Apache Spark and Python who need to build complex ETL pipelines with custom business logic, ML-powered data cleansing via FindMatches, and sophisticated orchestration through workflows and Airflow. At $0.44 per DPU-hour with auto-scaling, it offers strong cost efficiency for heavy batch processing workloads that require transformation before loading.
Choose Fivetran if:
Choose Fivetran if your primary need is fast, reliable data ingestion from diverse SaaS applications, databases, and event streams with minimal engineering overhead. Fivetran is the stronger option for teams that want pipelines running in minutes instead of days, with 700+ pre-built connectors handling schema evolution, CDC replication, and incremental syncs automatically. Its cloud-agnostic architecture, built-in rELT activation to 200+ destinations, and Hybrid Deployment option make it ideal for multi-cloud organizations that prefer the ELT approach of transforming data after it lands in the warehouse.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What is the main difference between AWS Glue and Fivetran?
AWS Glue is a serverless ETL platform that provides full data transformation capabilities using Apache Spark, a centralized Data Catalog, and visual pipeline tools within the AWS ecosystem. It requires data engineering expertise to configure crawlers, write transformation logic, and manage pipeline orchestration. Fivetran is a managed ELT platform focused on automated data ingestion through 700+ pre-built connectors that handle schema evolution, incremental syncs, and CDC replication without custom code. The core distinction is that Glue gives you transformation control before loading, while Fivetran prioritizes getting raw data into your warehouse fast and delegates transformation to downstream tools like dbt.
Which is more cost-effective, AWS Glue or Fivetran?
AWS Glue charges $0.44 per DPU-hour billed by the second with a free tier for the Data Catalog and offers Flex execution at up to 35% savings for non-urgent jobs. Fivetran uses Monthly Active Rows (MAR) pricing with a free tier at 500,000 MAR and tiered plans for Standard, Enterprise, and Business Critical needs. For organizations with heavy transformation workloads already on AWS, Glue typically costs less because you pay only for compute time consumed. For teams needing dozens of SaaS connectors, Fivetran often proves more cost-effective because building and maintaining equivalent custom connectors in Glue would require significant engineering hours that far exceed Fivetran's subscription cost.
Can AWS Glue and Fivetran be used together?
Yes, combining AWS Glue and Fivetran is a common and effective architecture pattern. Fivetran handles the ingestion layer by pulling data from 700+ SaaS sources, databases, and event streams into your data warehouse or S3 data lake with automated CDC and schema management. AWS Glue then takes over for complex transformation workloads, running Spark-based ETL jobs to clean, deduplicate, enrich, and reshape the ingested data for analytics consumption. This combination gives teams the best of both worlds: Fivetran's connector breadth and operational simplicity for extraction, paired with Glue's powerful transformation engine and Data Catalog for processing and metadata management.
Which tool is better for teams without deep data engineering experience?
Fivetran is significantly more accessible for teams without deep data engineering expertise. Its point-and-click interface lets analysts set up data pipelines in under two minutes, with fully managed connectors that handle schema changes, incremental updates, and error recovery automatically. AWS Glue requires knowledge of Apache Spark, Python or Scala, IAM permissions, and AWS networking concepts. While Glue Studio and DataBrew provide visual interfaces that reduce some complexity, production pipeline development still demands engineering skills. Fivetran's built-in dbt integration and Quickstart data models also lower the barrier for transformation work, making it the clear choice for lean data teams that need to move fast without hiring specialized pipeline engineers.