What I like about AWS Glue
“My team build a framework to fetch data from different platform through AWS Glue and stores them in S3 in the file format mention by us. That make our integration and fetching data a lot easier.”
You’re comparing AWS Glue vs Integrate.io vs Weld. Explore how they differ on connectors, pricing, and features.

Loved by data teams from around the world
| Weld | AWS Glue | Integrate.io | |
|---|---|---|---|
| Connectors | 200+ | 50+ | 100+ |
| Price | $99 / 5M Active Rows | $0.44 per DPUs-hour (development endpoints) + per-job costs | Custom, based on connectors & volume |
| Free tier | |||
| Location | EU | AWS Global (multi-region) | US |
| Extract data (ETL) | |||
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | |||
| Transformations | |||
| AI Assistant | |||
| On-Premise | |||
| Orchestration | |||
| Lineage | |||
| Version control | |||
| Load to/from Excel | Via JDBC to S3 CSVs | ||
| Load to/from Google Sheets | |||
| Two-Way Sync | |||
| dbt Core Integration | |||
| dbt Cloud Integration | |||
| OpenAPI / Developer API | |||
| G2 rating | 4.8 | 4.1 | 4.3 |
Overview
AWS Glue is a fully managed, serverless ETL service from AWS that automates data discovery, cataloging, and transformation using the Glue Data Catalog and PySpark. It integrates natively with AWS services like S3, Redshift, RDS, and DynamoDB, and supports third-party sources via JDBC. Glue offers both batch and streaming ETL, along with visual tools like Glue Studio and low-code options like DataBrew. It automatically scales based on workload, supports job scheduling and orchestration, and provides monitoring through CloudWatch. Ideal for AWS-centric teams, Glue simplifies large-scale data integration with minimal infrastructure management.

Serverless, no infrastructure to manage; Glue provisions compute as needed (Apache Spark under the hood).
Built-in Data Catalog for schema discovery, versioning, and integration with Athena and Redshift Spectrum.
Supports Python (PySpark) and Scala ETL scripts with mapping and transformation APIs for complex logic.
Deep integration with AWS ecosystem (CloudWatch monitoring, IAM for security, S3 triggers).
Cost can be unpredictable for long-running or high-concurrency jobs (billed per Data Processing Unit-hour).
Debugging PySpark jobs in Glue requires jumping between AWS console logs and code; local testing is limited compared to local Spark.
On-premises or multi-cloud data sources require additional setup (Glue has JDBC connectors but network config can be complex).
G2 Reviews:
“My team build a framework to fetch data from different platform through AWS Glue and stores them in S3 in the file format mention by us. That make our integration and fetching data a lot easier.”
“Does not support xml file formats.”
Overview
Integrate.io (formerly Xplenty) is a low-code/no-code data integration platform that unifies ETL, ELT, and reverse ETL in a single solution. It features a drag-and-drop interface for building pipelines and supports over 150 pre-built connectors for databases, SaaS apps, and REST APIs. Designed for both cloud and hybrid environments, it offers on-premise agents for secure local data access. Integrate.io supports complex workflows with scheduling, transformation logic, and change data capture (CDC), while also enabling reverse ETL to push data back into operational systems like CRMs. With built-in monitoring, alerts, and customer support, it’s a strong option for teams seeking a user-friendly yet powerful integration platform.
100+ pre-built connectors covering both operational (reverse ETL) and analytical use cases
Low-code visual pipeline builder with rich transformation expressions
Supports hybrid deployments via secure agent for on-prem sources
Unified platform for ETL, ELT, and reverse ETL
Robust workflow orchestration and scheduling features
Cloud-only SaaS (no fully on-prem option)
UI can feel complex initially due to breadth of features
Less polished transformation debugging compared to dedicated tools like Matillion
Pricing can be high for small teams; custom quotes required
Documentation sometimes lagging on newer features
G2 Review:
“The system was easy to integrate and flexible for all of our systems. The system is reliable with little or no maintenance or intervention needed to maintain consistent data flows. They have excellent support and communication in both timelines and quality of response.”
“There is a learning curve that can take some time to leverage more complex transformations and the support is limited with highly complex setups leaving some aspects of development more difficult.”
Overview
Weld is a powerful ETL platform that seamlessly integrates ELT, data transformations, reverse ETL, and AI-assisted features into one user-friendly solution. With its intuitive interface, Weld makes it easy for anyone, regardless of technical expertise, to build and manage data workflows. Known for its premium quality connectors, all built in-house, Weld ensures the highest quality and reliability for its users. It is designed to handle large datasets with near real-time data synchronization, making it ideal for modern data teams that require robust and efficient data integration solutions. Weld also leverages AI to automate repetitive tasks, optimize workflows, and enhance data transformation capabilities, ensuring maximum efficiency and productivity. Users can combine data from a wide variety of sources, including marketing platforms, CRMs, e-commerce platforms like Shopify, APIs, databases, Excel, Google Sheets, and more, providing a single source of truth for all their data.
Lineage, orchestration, and workflow features
Ability to handle large datasets and near real-time data sync
ETL + reverse ETL in one
User-friendly and easy to set up
Flat monthly pricing model
200+ connectors (Shopify, HubSpot, etc.)
AI assistant
Requires some technical knowledge around data warehousing and SQL
Limited features for advanced data teams
Focused on cloud data warehouses
A reviewer on G2 said:
“Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.”


Side-by-side

AWS Glue Studio provides a visual job authoring interface where you can drag-and-drop nodes to transform data, but deeper customizations still require PySpark code. The console UI can be intimidating for new users.
Integrate.io offers a drag-and-drop canvas for building pipelines, which is intuitive for new users but requires some ramp-up to master its full capabilities.
Weld is highly praised for its user-friendly interface and intuitive design, which allows even users with minimal SQL experience to manage data workflows efficiently. This makes it an excellent choice for smaller data teams or businesses without extensive technical resources.
Side-by-side
AWS Glue Studio provides a visual job authoring interface where you can drag-and-drop nodes to transform data, but deeper customizations still require PySpark code. The console UI can be intimidating for new users.
Integrate.io offers a drag-and-drop canvas for building pipelines, which is intuitive for new users but requires some ramp-up to master its full capabilities.
Weld is highly praised for its user-friendly interface and intuitive design, which allows even users with minimal SQL experience to manage data workflows efficiently. This makes it an excellent choice for smaller data teams or businesses without extensive technical resources.
Side-by-side

Glue charges per Data Processing Unit (DPU)-hour; for example, running a small job for one hour costs ~$0.44 * number of DPUs used. While serverless, large or long-running jobs can become costly if not optimized.
Pricing is customized and generally pitched as more affordable than enterprise competitors, but small businesses may find entry costs high without a free tier.
Weld offers a straightforward and competitive pricing model, starting at $79 for 5 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises.
Side-by-side
Glue charges per Data Processing Unit (DPU)-hour; for example, running a small job for one hour costs ~$0.44 * number of DPUs used. While serverless, large or long-running jobs can become costly if not optimized.
Pricing is customized and generally pitched as more affordable than enterprise competitors, but small businesses may find entry costs high without a free tier.
Weld offers a straightforward and competitive pricing model, starting at $79 for 5 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises.
Side-by-side

Features include automated schema discovery (Glue Data Catalog), PySpark/Scala job generation, job scheduling & triggers, DataBrew for visual data prep, and Glue Workflows for orchestration. Also supports streaming ETL via Glue streaming jobs.
A breadth of features: ETL/ELT connectors, reverse ETL for operational syncs, built-in transformation expressions, API generation, and workflow automation. It also provides data governance tools like environment promotion.
Weld integrates ELT, data transformations, and reverse ETL all within one platform. It also provides advanced features such as data lineage, orchestration, workflow management, and an AI assistant, which helps in automating repetitive tasks and optimizing workflows.
Side-by-side
Features include automated schema discovery (Glue Data Catalog), PySpark/Scala job generation, job scheduling & triggers, DataBrew for visual data prep, and Glue Workflows for orchestration. Also supports streaming ETL via Glue streaming jobs.
A breadth of features: ETL/ELT connectors, reverse ETL for operational syncs, built-in transformation expressions, API generation, and workflow automation. It also provides data governance tools like environment promotion.
Weld integrates ELT, data transformations, and reverse ETL all within one platform. It also provides advanced features such as data lineage, orchestration, workflow management, and an AI assistant, which helps in automating repetitive tasks and optimizing workflows.
Side-by-side

Glue allows custom PySpark scripts, supports Python libraries via wheel files, and you can integrate with AWS Lambda for custom triggers. However, debugging and local runs can be challenging compared to self-managed Spark.
Integrate.io allows custom scripting (JavaScript or SQL) within pipelines and secure agent deployment for on-prem sources. Its API and SDK enable integration into CI/CD, but ultimate customization is bounded by the platform’s framework.
Weld offers advanced SQL modeling and transformations directly within its platform with the help of AI, providing users with unparalleled control and flexibility over their data. Leveraging its powerful AI capabilities, Weld automates repetitive tasks and optimizes data workflows, allowing teams to focus on getting value and insights. Additionally, Weld's custom connector framework enables users to build connectors to any API, making it easy to integrate new data sources and tailor data pipelines to meet specific business needs. This flexibility is particularly beneficial for teams looking to customize their data integration processes extensively and maximize the utility of their data without needing external tools.
Side-by-side
Glue allows custom PySpark scripts, supports Python libraries via wheel files, and you can integrate with AWS Lambda for custom triggers. However, debugging and local runs can be challenging compared to self-managed Spark.
Integrate.io allows custom scripting (JavaScript or SQL) within pipelines and secure agent deployment for on-prem sources. Its API and SDK enable integration into CI/CD, but ultimate customization is bounded by the platform’s framework.
Weld offers advanced SQL modeling and transformations directly within its platform with the help of AI, providing users with unparalleled control and flexibility over their data. Leveraging its powerful AI capabilities, Weld automates repetitive tasks and optimizes data workflows, allowing teams to focus on getting value and insights. Additionally, Weld's custom connector framework enables users to build connectors to any API, making it easy to integrate new data sources and tailor data pipelines to meet specific business needs. This flexibility is particularly beneficial for teams looking to customize their data integration processes extensively and maximize the utility of their data without needing external tools.
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