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 Hevo vs Weld. Explore how they differ on connectors, pricing, and features.


Loved by data teams from around the world
| Weld | AWS Glue | Hevo | |
|---|---|---|---|
| Connectors | 200+ | 50+ | 150+ |
| Price | $99 / 5M Active Rows | $0.44 per DPUs-hour (development endpoints) + per-job costs | $299 / 5M rows |
| Free tier | |||
| Location | EU | AWS Global (multi-region) | INDIA |
| 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
Hevo is a cloud-based platform for data integration and pipeline automation. It enables organizations to seamlessly gather data from a wide range of sources, such as SaaS applications, databases, cloud storage, and streaming platforms, and transfer it into data warehouses or other destinations. From there, the data can be prepared for use in analytics, business intelligence, or even operational workflows through reverse data flows. Hevo's core mission is to streamline and automate the entire process, eliminating the need for companies to build and maintain complex custom infrastructure.
That said, experienced data specialists may wish for some more technical depth in the platform, giving them room to do more complex work.

Supports both ELT, ELT, and reverse ELT
Plenty of fully maintained connectors
Great for non-technical users
Simple UI that's easy to work with
Affordable pricing
Limited features for more advanced use cases
Limited custom scheduling features
Only 50 connectors are available on the Free plan
Lack of flexibility when wanting to edit pipelines
Error messages and status codes could be better
As one reviewer on G2 puts it: :
“Hevo is really good for normal pipelines, but it has some limitations for more complex use cases.”
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.

Hevo is designed with non-technical users in mind, offering a simple UI and code-free capabilities, making it easy for beginners to use.
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.
Hevo is designed with non-technical users in mind, offering a simple UI and code-free capabilities, making it easy for beginners to use.
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.

Hevo offers affordable pricing and a free plan with limited connectors, making it accessible for startups and small businesses. However, the costs can rise with data volume and feature needs.
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.
Hevo offers affordable pricing and a free plan with limited connectors, making it accessible for startups and small businesses. However, the costs can rise with data volume and feature needs.
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.

Hevo supports ETL, ELT, and reverse-ETL, making it versatile for different data needs. It also has plenty of fully maintained connectors, although it may lack some advanced features required by seasoned data professionals.
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.
Hevo supports ETL, ELT, and reverse-ETL, making it versatile for different data needs. It also has plenty of fully maintained connectors, although it may lack some advanced features required by seasoned data professionals.
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.

While Hevo is great for non-technical users, it lacks flexibility and customization options for more complex data tasks, making it less suitable for advanced data teams.
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.
While Hevo is great for non-technical users, it lacks flexibility and customization options for more complex data tasks, making it less suitable for advanced data teams.
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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