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.”
Comparing AWS Glue with Jitterbit and Weld
You’re comparing AWS Glue vs Jitterbit vs Weld. Explore how they differ on connectors, pricing, and features. 
Loved by data teams from around the world
Weld vs AWS Glue vs Jitterbit
| Weld | AWS Glue | Jitterbit | |
|---|---|---|---|
| Connectors | 200+ | 50+ | 100+ |
| Price | $99 / 5M Active Rows | $0.44 per DPUs-hour (development endpoints) + per-job costs | Subscription-based (custom quotes; starts ~$25k/year) |
| Free tier | |||
| Location | EU | AWS Global (multi-region) | Oakland, CA, USA |
| 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 | Yes (via Excel connector) | |
| 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 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.
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 dislike about AWS Glue
“Does not support xml file formats.”
Overview
Jitterbit in Short
Jitterbit Harmony is a cloud-based integration platform that supports ETL, API integration, and application connectivity. Its core concept is “Harmony Cloud Integration,” where users design “Jitterpaks” (integration packages) via a visual interface. Jitterbit offers pre-built connectors to major SaaS apps (Salesforce, NetSuite), on-prem databases, and flat files. It also supports real-time API creation from data sources and transformation using SQL or scripting.
Pros
Pre-built connectors for CRM, ERP, databases, and flat files; plus the ability to build custom connectors via SDK.
API creation feature: turn data flows into REST or SOAP endpoints on the fly.
Visual Studio for designing Jitterpaks (pipelines), with drag-and-drop mapping and transformation steps.
Real-time and batch modes supported; can deploy on Jitterbit’s cloud or your own servers (hybrid).
Cons
Complex licensing (based on endpoints, environments, and usage) can be expensive for heavy data volumes.
Studio interface can feel less modern compared to newer iPaaS; large, complex flows can become unwieldy.
Some advanced transformations require writing custom code rather than purely using GUI.
Reviews & Quotes
Jitterbit Harmony Overview:
What I like about Jitterbit
“Jitterbit’s Studio UI makes building integrations straightforward, and the API creation feature lets us expose data to external apps quickly.”
What I dislike about Jitterbit
“Pricing is tiered and can be high as you add more endpoints or data volume. Complex transformations sometimes require scripting, despite the low-code interface.”
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.
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
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
AWS Glue
Jitterbit
Weld
Feature
AWS Glue
Jitterbit
Weld
Ease of Use & Interface
Side-by-side
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.
Jitterbit
Jitterbit’s Studio is a Java-based desktop application (also has a web version) with a canvas for designing operations. It supports mapping between schemas, scripting for complex logic, and testing within the UI. Some users find it less responsive for very large flows.
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.
Ease of Use & Interface
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.
Jitterbit’s Studio is a Java-based desktop application (also has a web version) with a canvas for designing operations. It supports mapping between schemas, scripting for complex logic, and testing within the UI. Some users find it less responsive for very large flows.
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
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.
Jitterbit
Pricing depends on number of endpoints, environments (dev/test/prod), and data volume. Smaller teams might start around $25k/year, but enterprise usage can cost significantly more.
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.
Pricing & Affordability
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 depends on number of endpoints, environments (dev/test/prod), and data volume. Smaller teams might start around $25k/year, but enterprise usage can cost significantly more.
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
Side-by-side
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.
Jitterbit
Features include: ETL/ELT pipelines, API generation, cloud & on-prem deployment, real-time event triggers, pre-built templates (“Jitterpaks”), and monitoring dashboards. Also supports multi-environment promotion and CI/CD.
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.
Feature Set
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.
Features include: ETL/ELT pipelines, API generation, cloud & on-prem deployment, real-time event triggers, pre-built templates (“Jitterpaks”), and monitoring dashboards. Also supports multi-environment promotion and CI/CD.
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
Side-by-side
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.
Jitterbit
Users can embed JavaScript or VBScript for transformations. Jitterbit’s SDK allows building custom connectors. While hybrid deployment is possible, full feature access often requires cloud usage.
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.
Flexibility & Customization
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.
Users can embed JavaScript or VBScript for transformations. Jitterbit’s SDK allows building custom connectors. While hybrid deployment is possible, full feature access often requires cloud usage.
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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