Modern businesses generate massive data volumes across countless platforms, creating an urgent need for robust data integration solutions. With over 53% of organizations struggling to extract value from their data, choosing the right ETL tool has become business critical for maintaining competitive advantage.
Two platforms dominate the conversation when data engineers evaluate their options: Fivetran and AWS Glue. While both excel at moving and transforming data, they represent fundamentally different approaches to solving data integration challenges. Fivetran offers a fully managed ELT platform with 700+ pre built connectors, while AWS Glue provides a serverless data integration service with deep integration into the AWS ecosystem.
The choice between these platforms can significantly impact your organization’s data strategy, operational costs, and team productivity. It is essential to ensure that your ETL tool selection aligns with your organization's data strategy to support long-term data management goals. This comprehensive comparison will help you navigate the key differences, understand implementation requirements, and make an informed decision based on your specific business needs.
The data integration landscape has evolved dramatically over the past decade. Traditional ETL tools required extensive manual setup and ongoing maintenance, creating bottlenecks that slowed business intelligence initiatives. Today’s leading platforms promise automated data integration with minimal technical overhead, but delivery varies significantly.
Quick Comparison Overview:Here are the key features of Fivetran and AWS Glue:
Fivetran: Fully managed ELT platform with extensive third-party connectivity
AWS Glue: Serverless ETL service optimized for AWS native environments, supporting over 70 data sources primarily within the AWS ecosystem
Key Decision Factors: Technical expertise available, diversity of data sources, transformation complexity requirements, and existing cloud infrastructure
The stakes for getting this decision right are high. Poor data integration choices can lead to project delays, cost overruns, and frustrated teams struggling with complex workflows. Conversely, the right platform choice accelerates time-to-value, reduces manual effort, and enables teams to focus on deriving insights rather than managing infrastructure.
Success depends on understanding how each platform aligns with your organization’s data strategy, technical capabilities, and growth trajectory. The following analysis provides the framework for making this critical decision.
Fivetran revolutionized the data integration space by eliminating the traditional pain points of ETL pipeline management. Built as a fully managed data integration platform, Fivetran promises “set-and-forget” data pipelines that handle everything from initial setup to ongoing maintenance.
Core Capabilities:
Extensive Connector Library: 700+ pre built connectors spanning databases, saas applications, cloud storage, and business systems
Zero-Maintenance Architecture: Self-healing pipelines that automatically handle API changes, schema evolution, and error recovery
Service Level Agreement: 99.9% uptime guarantee with automated monitoring and alerting
ELT Approach: Raw data replication with transformations performed in your data warehouse using computational power
Consumption Based Pricing Model: Monthly Active Rows (MAR) pricing that scales with actual data usage
The platform’s strength lies in Fivetran’s ability to extract data from diverse sources, including databases, SaaS applications, and flat files, without requiring technical expertise. Popular integrations include Salesforce, Google Analytics, NetSuite, HubSpot, and hundreds of other business applications that generate valuable operational data.
Fivetran’s automated schema drift handling represents a significant operational advantage. When source systems change their data structure, Fivetran automatically detects and propagates these changes downstream, preventing pipeline breakage that traditionally required manual intervention.
Fivetran efficiently loads data into leading cloud data warehouses such as Snowflake, BigQuery, and Databricks, streamlining the ELT process with robust automation and integration capabilities.
Built-in dbt Integration enables SQL-based transformations directly within the platform, allowing business analysts and data engineers to collaborate using familiar tools while maintaining data consistency across transformation workflows. Fivetran also helps prepare data for analytics and reporting by supporting diverse transformation methods that ready data for downstream use.
AWS Glue's serverless ETL platform represents Amazon’s vision for serverless data integration within cloud-native architectures. Built on Apache Spark, Glue provides powerful data processing capabilities that scale automatically based on workload demands. The platform supports custom scripts in Python or Scala, offering maximum flexibility for complex data transformations. AWS Glue also provides features for visual and code-based transformations depending on user preferences, catering to a wide range of technical expertise levels.
Core Architecture:
Serverless ETL Service: Eliminates infrastructure management while providing enterprise-scale data processing
Apache Spark Foundation: Distributed computing framework supporting Python and Scala for complex transformations
AWS Glue Data Catalog: Centralized metadata repository that integrates with other AWS services for unified data discovery
Visual Interface: Drag-and-drop ETL job creation with automatic code generation capabilities
Data Processing Units (DPUs): Flexible compute scaling that charges per-second for actual resource consumption; DPUs are a measure of processing power and memory capacity, directly impacting performance and cost
Batch and Streaming Workflows: Supports batch and streaming processing modes with seamless AWS integration
Wide Range of Data Connectors: AWS Glue offers a broad selection of data connectors for integrating various data sources, including databases, SaaS applications, and more
Apache Iceberg Support: Provides native support for managing large-scale analytic tables in AWS environments
AWS Glue supports building custom connectors to integrate unsupported or unique data sources. The platform enables the creation of custom connectors and function connectors, allowing users to extend data integration capabilities for bespoke requirements.
AWS Glue excels in AWS native environments where deep integration with other AWS services creates powerful synergies. The platform seamlessly connects with S3 data lakes, Redshift data warehouses, RDS databases, and dozens of other AWS services through native connectors. AWS Glue can also connect to flat files as well as databases and SaaS applications, providing flexibility in data ingestion.
Complex Transformation Capabilities set AWS Glue apart for organizations requiring sophisticated data processing logic. The platform supports custom Python libraries, advanced Spark operations, and machine learning integrations that enable sophisticated data workflows.
The AWS Glue Data Catalog serves as a unified metadata store, enabling data discovery across your entire AWS ecosystem. This centralized approach simplifies data governance and enables other AWS services like Athena to query cataloged data without additional configuration. Companies building a data lake on Amazon S3 can use the Data Catalog and ETL jobs to manage metadata for querying via services like Amazon Athena.
AWS Glue's pricing is based on the amount of data processed during ETL jobs, making it scalable and workload-dependent.
The breadth and quality of available connectors often determines platform viability for organizations with diverse data sources.
Fivetran’s Connector Advantage:
700+ pre built connectors covering virtually every major business application
Specialized connectors for marketing platforms (Facebook Ads, Google Ads, LinkedIn)
Financial system integrations (NetSuite, QuickBooks, Stripe)
Customer relationship management (Salesforce, HubSpot, Zendesk)
Database connectivity (MySQL, PostgreSQL, MongoDB, Oracle)
These connectors enable seamless extraction and integration of source data from a wide range of platforms, ensuring accurate and efficient data workflows.
Fivetran maintains these connectors proactively, handling API updates, authentication changes, and new feature support automatically. This means less maintenance overhead for your team and more reliable data pipelines.
AWS Glue’s Focused Approach:
70+ connectors primarily targeting AWS services and common database systems
Native integration with S3, Redshift, RDS, DynamoDB, and Aurora
JDBC support for custom database connections
Limited coverage for SaaS applications outside the AWS ecosystem
AWS Glue’s connector strategy prioritizes depth over breadth, providing exceptional integration within AWS environments while requiring more manual setup for external data sources. However, its tight integration with AWS services can lead to vendor lock-in, making it challenging to use with data sources outside the AWS ecosystem.
|
Connector Category |
Fivetran |
AWS Glue |
|---|---|---|
|
SaaS Applications |
500+ |
10+ |
|
Databases |
100+ |
40+ |
|
Cloud Storage |
50+ |
15+ |
|
Business Intelligence |
30+ |
5+ |
|
Maintenance |
Fully Automated |
Manual Updates Required |
The level of technical expertise required varies dramatically between platforms, affecting both implementation speed and ongoing operational requirements.
Fivetran’s No Code Solution:
Point-and-click connector configuration
Minimal setup time (often under 30 minutes per source)
Pre built data models for common transformations
Visual pipeline monitoring and alerting
No programming knowledge required for basic implementation
Fivetran minimizes the need for external tools for data transformation and integration, providing a streamlined, integrated experience within its platform.
AWS Glue, in contrast, requires a higher level of technical expertise in Spark, Python, or Scala compared to Fivetran’s user-friendly approach. Additionally, AWS Glue is capable of supporting on-premises setups through AWS services like AWS Outposts, extending its flexibility for hybrid environments.
Point-and-click connector configuration
Minimal setup time (often under 30 minutes per source)
Pre built data models for common transformations
Visual pipeline monitoring and alerting
No programming knowledge required for basic implementation
Fivetran supports hybrid deployments and allows secure connections to on-premises data sources, further enhancing its flexibility for diverse organizational needs.
This approach enables business analysts and data teams without extensive engineering backgrounds to implement and manage data pipelines effectively.
AWS Glue’s Engineering Focus:
Python or Scala programming required for custom transformations
Apache Spark knowledge essential for performance optimization (for a detailed comparison of Spark and Trino, see Trino vs Spark: A Practical Comparison for Data Processing Needs)
More manual setup and configuration steps
Greater flexibility for complex data processing requirements
Steeper learning curve but more powerful customization options
Organizations with strong data engineering capabilities can leverage AWS Glue’s flexibility to implement sophisticated data workflows that would be impossible with simpler platforms.
How you transform data after extraction significantly impacts your analytics capabilities and operational complexity.
Fivetran’s ELT Philosophy:
Raw data replication preserves complete source information
Transformations performed in your data warehouse using dbt Core integration
Leverages modern cloud data warehouse computational power
SQL-based transformation development through dbt integration
Version control and collaboration through dbt workflows
Raw data replication preserves complete source information
Transformations performed in your data warehouse using dbt Core integration
Leverages modern cloud data warehouse computational power
SQL-based transformation development
Version control and collaboration through dbt workflows
This approach works exceptionally well with modern cloud data warehouses like Snowflake, BigQuery, and Redshift that provide massive computational resources for in-warehouse transformations.
AWS Glue’s ETL Flexibility:
Transform data before loading into target systems
Complex transformations using Apache Spark capabilities
Custom Python libraries and machine learning integrations
Real-time and batch processing options
Advanced data cleansing and enrichment workflows
After transformation, AWS Glue supports loading data into a variety of data warehouses, ensuring secure and efficient data movement from multiple sources through its extensive connector support.
AWS Glue enables preprocessing that can reduce storage costs and improve query performance by loading only clean, transformed data into your data warehouse.
When it comes to data integration, security and compliance are non-negotiable—especially as organizations handle sensitive business and customer information. Fivetran and AWS Glue both take a rigorous approach to protecting your data throughout the integration process.
Fivetran employs robust security protocols, including end-to-end encryption for data in transit and at rest, secure login authorization, and the use of SSH tunnels for added protection. The platform is committed to industry-leading security certifications, such as ISO 27001, SOC 2, and HIPAA BAA, giving organizations confidence that their data integration workflows meet stringent compliance requirements. Additionally, Fivetran offers user-friendly support with 24/7 email and chat options based on the pricing plan, ensuring assistance is readily available when needed.
AWS Glue, as a serverless data integration service, leverages the comprehensive security framework of AWS. This includes fine-grained access control through AWS Identity and Access Management (IAM), encryption options for both data at rest and in motion, and compliance with major standards like PCI DSS, GDPR, and HIPAA. AWS Glue’s integration with other AWS services ensures that security policies are consistently enforced across your entire cloud environment. The platform also provides tiered vendor support with different levels of accessibility based on the plan, catering to varying organizational needs.
When comparing AWS Glue vs Fivetran, both platforms offer strong security postures, but the choice may come down to your organization’s specific compliance needs and whether you require deep integration with AWS’s security ecosystem or a broader set of third-party certifications. Ultimately, both Fivetran and AWS Glue provide the tools and certifications necessary to safeguard your data integration pipelines.
As your organization’s data grows, so does the need for scalable and high-performing data integration solutions. AWS Glue and Fivetran each offer distinct approaches to managing increasing data volumes and processing demands.
AWS Glue’s serverless architecture is designed for elasticity, automatically scaling data processing units (DPUs) to match the size and complexity of your ETL jobs. This means you can handle anything from small, routine data loads to massive, enterprise-scale data processing without manual intervention. The platform’s ability to dynamically allocate resources ensures that performance remains consistent, even as data volumes fluctuate.
Fivetran, meanwhile, provides scalability through its consumption-based pricing model, which is based on monthly active rows (MAR). This approach allows organizations to scale their data integration efforts in line with actual usage, making it easy to accommodate growth without overcommitting resources. Fivetran’s infrastructure is built to support high-throughput data pipelines, ensuring reliable performance as your data needs expand.
When evaluating Fivetran vs AWS Glue, consider not only your current data volumes but also your anticipated growth. AWS Glue’s flexible data processing units (DPUs) are ideal for organizations with unpredictable or rapidly increasing workloads, while Fivetran’s MAR-based model offers straightforward scaling for businesses with steady or moderately growing data integration requirements.
Understanding the true cost of data integration is essential for making a sound investment. AWS Glue and Fivetran use different pricing models, each with its own implications for cost predictability and scalability.
AWS Glue charges based on the number of data processing units (DPUs) consumed per hour, which means your costs are directly tied to the compute resources used during ETL jobs. This model can be highly cost-effective for organizations with large, variable workloads, but it may also lead to unpredictable expenses if data processing spikes unexpectedly.
Fivetran, on the other hand, uses a consumption-based pricing model centered on monthly active rows (MAR). This provides greater cost predictability, as you pay for the actual number of unique rows processed each month. For organizations with consistent or moderate data volumes, this can simplify budgeting and financial planning. Fivetran is considered more predictable for smaller workloads due to its consumption-based pricing. However, as data volumes increase, Fivetran’s costs can rise quickly, so it’s important to monitor usage and project future needs.
When comparing AWS Glue vs Fivetran, factor in not just the headline pricing, but also the total cost of ownership—including data storage, data processing, and any additional support or feature fees. Both platforms have limitations in terms of price transparency for higher usage tiers, which can make it challenging to predict costs as data volumes grow. Carefully aligning your expected data volumes and processing patterns with each platform’s pricing structure will help you choose the most cost-effective solution for your organization.
Real-world feedback from verified users provides valuable insights into how these platforms perform in production environments.
Fivetran User Satisfaction (92% Rating):
Teams consistently praise Fivetran’s ease of implementation and reliability. A verified user from a mid-sized technology company noted: “We reduced our data pipeline maintenance from 40 hours per week to virtually zero after migrating to Fivetran. The automated schema handling alone saved us countless hours of troubleshooting.”
Key Positive Feedback:
Rapid deployment with immediate value
Extensive library of reliable connectors
Predictable costs for smaller data volumes
Exceptional customer support responsiveness
Minimal technical expertise requirements
Common Concerns:
Limited customization for complex transformation requirements
Pricing can become expensive with high data volume growth
Dependency on Fivetran for connector updates and new integrations
AWS Glue User Satisfaction (85% Rating):
Data engineers value AWS Glue’s flexibility and deep AWS ecosystem integration. A senior data engineer from a Fortune 500 financial services company shared: “AWS Glue allows us to implement custom data quality checks and complex transformations that simply aren’t possible with simpler ELT platforms. The serverless architecture scales beautifully with our workload demands.”
Key Positive Feedback:
Exceptional customization capabilities
Cost-effective for large-scale data processing within AWS
Seamless integration with existing AWS infrastructure
Powerful Apache Spark processing capabilities
Fine grained access control through AWS IAM
Common Concerns:
Steep learning curve requiring specialized technical expertise
Limited connector options for non-AWS data sources
Documentation complexity can overwhelm new users
Cost optimization requires careful job tuning
Understanding implementation requirements helps set realistic expectations for timeline, resources, and ongoing operational needs.
Fivetran Implementation:
Infrastructure: Cloud-based deployment requires no on-premises hardware
Technical Skills: Minimal setup expertise needed; SQL knowledge helpful for custom transformations
Timeline: Most connectors operational within hours of setup
Security: SOC 2 Type 2, HIPAA, and GDPR compliance built-in
Ongoing Maintenance: Fully managed service requires minimal operational oversight
Fivetran’s streamlined implementation process enables quick wins and rapid value demonstration, making it ideal for organizations prioritizing speed to insight over technical customization.
AWS Glue Implementation:
Infrastructure: Requires existing AWS environment and proper IAM configuration
Technical Skills: Apache Spark knowledge essential; Python or Scala programming required
Timeline: Complex implementations may require weeks or months for full deployment
Security: AWS-grade security with fine grained access control and encryption
Ongoing Maintenance: Requires data engineering expertise for optimization and troubleshooting
AWS Glue is more complex to set up and manage, requiring technical expertise in AWS infrastructure.
Infrastructure: Requires existing AWS environment and proper IAM configuration
Technical Skills: Apache Spark knowledge essential; Python or Scala programming required
Timeline: Complex implementations may require weeks or months for full deployment
Security: AWS-grade security with fine grained access control and encryption
Ongoing Maintenance: Requires data engineering expertise for optimization and troubleshooting
AWS Glue implementations benefit from careful planning and skilled technical resources, but provide exceptional flexibility for organizations with specific data processing requirements.
Selecting the right ETL tool is a strategic decision that impacts your entire data integration and analytics ecosystem. To make an informed choice, organizations should evaluate several critical criteria.
First, assess the platform’s data integration capabilities—does it support all your required data sources and provide seamless integration with your existing data warehouses? Next, consider the tool’s data transformation features: can it handle the complexity of your business logic and deliver the data in the format your analytics teams need?
Scalability is another key factor. Ensure the tool can manage your current data volume and scale as your organization grows. Security certifications and compliance should also be top of mind, especially if you handle sensitive or regulated data.
Don’t overlook the level of technical expertise required. Some platforms, like Fivetran, are designed for minimal setup and can be managed by business analysts, while others, like AWS Glue, may require specialized engineering skills. Finally, evaluate the quality of support, documentation, and community resources available for each tool.
By weighing these criteria—data integration, data sources, data volume, data transformation, technical expertise, security certifications, and seamless integration with your data warehouses—you can confidently select the ETL solution that best aligns with your organization’s needs, whether that’s Fivetran, AWS Glue, or another platform.
The choice between Fivetran and AWS Glue often comes down to the specific use cases and scenarios your organization faces in its data integration journey.
Fivetran excels in environments where automated data integration from a wide variety of data sources and SaaS applications is essential. Its minimal setup and extensive connector library make it ideal for organizations that need to quickly integrate data from marketing platforms, CRM systems, and other business-critical tools without heavy engineering involvement. This makes Fivetran a strong fit for companies seeking rapid deployment and streamlined data management across diverse sources.
AWS Glue, by contrast, shines in organizations deeply invested in the AWS ecosystem. Its tight integration with AWS services like Amazon S3, Redshift, and other AWS-native tools enables powerful, scalable data management workflows. AWS Glue is particularly well-suited for scenarios requiring complex data transformations, large-scale data processing, and seamless orchestration within the AWS environment.
By understanding these real-world applications, organizations can better match their data integration and data management needs to the strengths of each platform. Whether you prioritize automated data integration with minimal setup (Fivetran) or advanced data workflows within the AWS ecosystem (AWS Glue), aligning your use case with the right tool is key to maximizing value from your data.
Rapid Data Integration Success Organizations prioritizing speed to value and minimal technical overhead find Fivetran’s fully managed approach transformative. The platform excels when you need reliable data pipelines operational quickly without extensive engineering investment. Fivetran also enables hybrid deployments and provides managed data lake services while ensuring efficient automation, scalability, and enterprise-grade security.
Extensive Third-Party Connectivity If your data strategy involves integrating diverse SaaS applications, marketing platforms, and external data sources, Fivetran’s 700+ pre built connectors provide unmatched breadth and reliability.
Predictable Operational Costs Teams with limited data engineering resources benefit from Fivetran’s zero-maintenance model. The Monthly Active Rows (MAR) pricing provides cost predictability for smaller to medium data volumes.
Business Analyst Empowerment Organizations wanting to democratize data access find Fivetran’s no code solution enables business teams to implement and manage data pipelines independently.
Deep AWS Ecosystem Integration Organizations with significant AWS infrastructure investments maximize value through Glue’s native integration with other AWS services. The platform provides seamless connectivity across your entire AWS data ecosystem.
Complex Data Processing at Scale Teams requiring sophisticated data transformations, real-time processing, or custom logic benefit from AWS Glue’s Apache Spark foundation and serverless scalability.
Cost Optimization for Large Workloads Organizations processing massive data volumes within AWS often find Glue’s pay-as-you-go model more cost-effective than volume-based pricing alternatives.
Engineering Team Flexibility Teams with strong Apache Spark and Python expertise can leverage AWS Glue’s customization capabilities to implement specialized data workflows impossible with simpler platforms.
The decision between Fivetran and AWS Glue ultimately depends on balancing your organization’s technical capabilities, data source diversity, transformation complexity, and existing infrastructure investments. Both platforms excel in their respective domains – Fivetran for simplicity and breadth, AWS Glue for flexibility and AWS-native optimization.
Consider starting with proof-of-concept implementations using your most critical data sources to evaluate how each platform performs with your specific requirements. This hands-on approach provides the clearest path to making an informed decision that supports your long-term data strategy goals.
While Fivetran streamlines third-party SaaS data ingestion and AWS Glue powers custom ETL in AWS environments, Factory Thread delivers a tailored solution for manufacturers who need real-time, production-grade data integration without the engineering overhead. For those considering alternatives to Snowflake, Factory Thread represents a compelling option.
Here’s how Factory Thread compares:
Zero-maintenance connectors to PLCs, sensors, MES, ERP, QMS, and machine data—no custom drivers or Spark code required
Low-code visual tools and AI workflows built for industrial users, not just data engineers
Built-in semantic layer to contextualize OT and IT data across production lines, shifts, and SKUs
Federated query access via REST, GraphQL, or OData without data duplication
Industrial-grade reliability with edge processing and offline resilience
Unlike Fivetran, Factory Thread doesn’t just pull SaaS data—it’s built for raw plant-floor systems that power day-to-day operations. And unlike AWS Glue, it requires no Spark clusters, no custom Python jobs, and no AWS lock-in.
Factory Thread enables manufacturers to unify, query, and act on their production data—faster, with less effort, and greater contextual fidelity.
Whether you're syncing quality inspections to the cloud, monitoring machine health in real time, or building predictive maintenance pipelines, Factory Thread eliminates the complexity of OT/IT integration and delivers insights directly to the people who need them.