Comparing AWS Glue with Supermetrics and Weld
Carolina Russ6 min read
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 Supermetrics
Supermetrics is a powerful data integration tool that's particularly popular among marketers. It allows you to fetch data from various marketing platforms, such as Google Ads, Facebook Ads, and Google Analytics, and then transfer it into a data warehouse, spreadsheet, or BI tools like Looker, Google Data Studio, or PowerBI. The automation it provides saves time and reduces the chance of errors that come with manual data transfers.
Pros
- Easy to use
- Pre-built reports
- Google Sheets destination
- Flexibility in data storage and visualization options
Cons
- Complex pricing model
- Expensive
- Limited data transformation capabilities
- Slow data refresh rates
- Can be glitchy
- Not suitable for very large datasets
A reviewer on G2:
What I like about Supermetrics
Supermetrics is so easy to use, and the user interface for Google Sheets is intuitive and fast. There is a huge number of connectors available. I liked the scheduling options in my legacy plan, with hourly refreshes available. The customer support is premium and fast.
What I dislike about Supermetrics
There is a limited set of connectors available in the less expensive pricing plans, and the pricing has increased significantly over the years.
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.
AWS Glue vs Supermetrics: Ease of Use and User Interface
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.
Supermetrics
Supermetrics is easy to use, especially for marketers who need to fetch data quickly from various platforms and transfer it to spreadsheets or BI tools.
AWS Glue vs Supermetrics: Pricing Transparency and Affordability
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.
Supermetrics
The pricing model for Supermetrics can be complex and expensive, which might be a concern for small businesses or those with limited budgets.
AWS Glue vs Supermetrics: Comprehensive Feature Set
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.
Supermetrics
Supermetrics offers pre-built reports and flexibility in data storage and visualization, but has limited data transformation capabilities and may have slow data refresh rates.
AWS Glue vs Supermetrics: Flexibility and Customization
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.
Supermetrics
Supermetrics provides flexibility in where data can be stored and visualized, but may lack advanced customization options for more technical users.
Summary of AWS Glue vs Supermetrics vs Weld
| Weld | AWS Glue | Supermetrics | |
|---|---|---|---|
| Connectors | 200++ | 50+ | 128+ |
| Price | $99 / Unlimited usage | $0.44 per DPUs-hour (development endpoints) + per-job costs | starts at $29 / month - 3 data sources - 1 user |
| Free tier | No | Yes | No |
| Location | EU | AWS Global (multi-region) | FI |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | No | No |
| Transformations | Yes | Yes | No |
| AI Assistant | Yes | No | No |
| On-Premise | No | No | No |
| Orchestration | Yes | Yes | No |
| Lineage | Yes | Yes | No |
| Version control | Yes | No | No |
| Load data to and from Excel | Yes | Yes | No |
| Load data to and from Google Sheets | Yes | No | No |
| Two-Way Sync | Yes | No | No |
| dbt Core Integration | Yes | Yes | No |
| dbt Cloud Integration | Yes | No | No |
| OpenAPI / Developer API | Yes | No | No |
| G2 Rating | 4.8 | 4.1 | 4.4 |
Conclusion
You’re comparing AWS Glue, Supermetrics, Weld. Each of these tools has its own strengths:
- 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. .
- Supermetrics: supermetrics offers pre-built reports and flexibility in data storage and visualization, but has limited data transformation capabilities and may have slow data refresh rates.. the pricing model for supermetrics can be complex and expensive, which might be a concern for small businesses or those with limited budgets..
- 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..
