Comparing Airbyte with AWS Glue and Weld
Carolina Russ6 min read
What is Airbyte
An open-source data integration platform, Airbyte is a popular choice for those building a modern data stack. Airbyte boasts its collection of ELT connectors as well as the ability to build custom ones in the platform, a differentiator from other no-code ELT tools. Because building custom pipelines requires coding knowledge, this special feature will only benefit data specialists with solid coding abilities.
Airbyte is built to work well with DBT and SQL, so this is an important thing to note before deciding to use the tool.
Pros
- Open-source platform
- A really large number of connectors and destinations
- Easy deployment
- Managed cloud option
- Engineering-focused with advanced options to build your own connectors and features
- Has both ELT and Reverse ETL options
- Large community
Cons
- More suited for advanced teams
- Require more maintenance
- The quality of connectors can vary because of open-source
- High dependence on community
In a review from Confessions of a Data Guy, he shares::
What I like about Airbyte
If you don't have workloads that currently use DBT or fit well into that model, this probably isn’t the tool for you.
What I dislike about Airbyte
What is AWS Glue
AWS Glue is a fully managed, serverless ETL service provided by Amazon Web Services. It automatically discovers and catalogs metadata (Glue Data Catalog), generates ETL code in PySpark, and runs jobs on demand or schedules them. Glue integrates natively with AWS data stores (S3, Redshift, RDS, DynamoDB) and third-party sources via JDBC.
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).
AWS Glue Documentation:
What I like about AWS Glue
Glue’s automatic schema discovery and code generation speed up ETL development—once you point it to a data source, it builds tables in the Data Catalog and scaffolds PySpark jobs for you.
What I dislike about AWS Glue
Managing large-scale Glue jobs can be tricky—job concurrency limits and developer debugging in PySpark jobs require more AWS expertise.
What is Weld
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
- Premium quality connectors and reliability
- User-friendly and easy to set up
- AI assistant
- Very competitive and easy-to-understand pricing model
- Reverse ETL option
- Lineage, orchestration, and workflow features
- Advanced transformation and SQL modeling capabilities
- Ability to handle large datasets and near real-time data sync
- Combines data from a wide range of sources for a single source of truth
Cons
- Requires some technical knowledge around data warehousing and SQL
- Limited features for advanced data teams
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.
Airbyte vs AWS Glue: Ease of Use and User Interface
Airbyte
Airbyte is an open-source platform that is easier for teams with strong coding skills but may be challenging for those without technical expertise, especially when building custom connectors.
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.
Airbyte vs AWS Glue: Pricing Transparency and Affordability
Airbyte
Airbyte offers a flexible pricing model based on credits, but costs can add up depending on data volume, making it more suitable for teams that can optimize their usage.
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.
Airbyte vs AWS Glue: Comprehensive Feature Set
Airbyte
The platform supports a large number of connectors and destinations and is designed to work well with DBT and SQL, providing robust options for teams building a modern data stack.
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.
Airbyte vs AWS Glue: Flexibility and Customization
Airbyte
Airbyte's open-source nature and customization options make it highly flexible for teams with the capability to maintain and build on the platform, but this flexibility comes with a higher maintenance burden.
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.
Summary of Airbyte vs AWS Glue vs Weld
| Weld | Airbyte | AWS Glue | |
|---|---|---|---|
| Connectors | 200++ | 350+ | 50+ |
| Price | €99 / Unlimited usage | €2.50/credit (one million rows = 6 credits; 1 GB = 4 credits) | $0.44 per DPUs-hour (development endpoints) + per-job costs |
| Free tier | No | Yes | Yes |
| Location | EU | US | AWS Global (multi-region) |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | Yes | No |
| Transformations | Yes | No | Yes |
| AI Assistant | Yes | No | No |
| On-Premise | No | Yes | No |
| Orchestration | Yes | No | Yes |
| Lineage | Yes | No | Yes |
| Version control | Yes | No | No |
| Load data to and from Excel | Yes | No | Yes |
| Load data to and from Google Sheets | Yes | No | No |
| Two-Way Sync | Yes | No | No |
| dbt Core Integration | Yes | Yes | Yes |
| dbt Cloud Integration | Yes | No | No |
| OpenAPI / Developer API | Yes | Yes | No |
| G2 Rating | 4.8 | 4.2 | 4.1 |
Conclusion
You’re comparing Airbyte, AWS Glue, Weld. Each of these tools has its own strengths:
- Airbyte: the platform supports a large number of connectors and destinations and is designed to work well with dbt and sql, providing robust options for teams building a modern data stack.. airbyte offers a flexible pricing model based on credits, but costs can add up depending on data volume, making it more suitable for teams that can optimize their usage..
- 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. . 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. .
- 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.. weld offers a straightforward and competitive pricing model, starting at €99 for 2 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises..
