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Comparing AWS Glue with Hevo and Weld

You’re comparing AWS Glue vs Hevo vs Weld. Explore how they differ on connectors, pricing, and features. Ed Logo

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Weld vs AWS Glue vs Hevo

WeldAWS GlueHevo
Connectors200+50+150+
Price$99 / 5M Active Rows$0.44 per DPUs-hour (development endpoints) + per-job costs$299 / 5M rows
Free tier
LocationEUAWS 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 ExcelVia JDBC to S3 CSVs
Load to/from Google Sheets
Two-Way Sync
dbt Core Integration
dbt Cloud Integration
OpenAPI / Developer API
G2 rating4.84.14.3

Overview

AWS Glue in Short

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.

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Pros

  • 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).

Cons

  • 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).

Reviews & Quotes

G2 Reviews:

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.

What I dislike about AWS Glue

Does not support xml file formats.

Overview

Hevo in Short

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.

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Pros

  • 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

Cons

  • 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

Reviews & Quotes

As one reviewer on G2 puts it: :

What I like about Hevo

Hevo is really good for normal pipelines, but it has some limitations for more complex use cases.

Overview

Weld in Short

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.

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Pros

  • 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

Cons

  • Requires some technical knowledge around data warehousing and SQL

  • Limited features for advanced data teams

  • Focused on cloud data warehouses

Reviews & Quotes

A reviewer on G2 said:

What I like about Weld

First and foremost, Weld is incredibly user-friendly. The graphical interface is intuitive, which makes it easy to build data workflows quickly and efficiently. Even with little experience in SQL and pipeline management, we found that Weld was straightforward and easy to use. What really impressed me, however, was Weld's flexibility. It was able to handle data from a wide variety of sources, including SQL databases, Google Sheets, and even APIs. The solution also allowed us to customize my data transformations in a way that best suited my needs. Whether I needed to clean data, join tables, or aggregate data, Weld had the necessary tools to accomplish the task. Weld's performance was also exceptional. I was able to run large-scale ETL jobs quickly and efficiently, with minimal downtime via a Snowflake instance and visualization via own-hosted Metabase. The solution's scalability meant that I could process more data without any issues. Another standout feature of Weld was its support. I never felt lost or unsure about how to use a particular feature, as the support team was always quick to respond to any questions or concerns that I had. Overall, I highly recommend Weld as an ETL solution. Its user-friendliness, flexibility, performance, and support make it an excellent choice for anyone looking to streamline their data integration processes. I will definitely be using Weld for all my ETL needs going forward.

What I dislike about Weld

Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.

Feature-by-Feature Comparison

Feature
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AWS Glue

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Hevo

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Weld

Ease of Use & Interface

Side-by-side

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AWS Glue

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.

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Hevo

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

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Weld

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.

Pricing & Affordability

Side-by-side

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AWS Glue

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.

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Hevo

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.

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Weld

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.

Feature Set

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AWS Glue

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.

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Hevo

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.

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Weld

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.

Flexibility & Customization

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AWS Glue

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.

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Hevo

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.

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Weld

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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