Comparing AWS Glue with Matia 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 Matia
Matia is a unified DataOps platform combining ETL/ELT, reverse ETL, data observability, and data catalog features in a single cloud-native solution. It aims to reduce “tool sprawl” by offering ingestion, monitoring, and activation in one interface, with rapid connector development based on customer requests.
Pros
- Unified platform: ETL/ELT, reverse ETL, observability, and catalog in one solution
- Hundreds of connectors available, with rapid on-demand connector development
- Built-in data observability to detect anomalies, schema changes, and pipeline health
- Data catalog for metadata management and discovery integrated natively
- Strong, responsive customer support and quick feature rollout
Cons
- Newer startup—features still maturing compared to incumbents
- Cloud-only SaaS (no on-prem option)
- Limited third-party tutorials or community resources due to early stage
- Pricing not publicly transparent; requires custom negotiation
- All-in-one approach may lack depth of specialized tools in certain areas (advanced catalog features, for example)
Matia Homepage:
What I like about Matia
Matia unifies ETL, observability, catalog, and reverse ETL so teams can focus on driving actionable insights and accelerating innovation.
What I dislike about Matia
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 Matia: 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.
Matia
Matia’s UI integrates ingestion, observability, and cataloging in a cohesive web interface, making setup straightforward for small teams. Users praise its modern design and low learning curve.
AWS Glue vs Matia: 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.
Matia
Pricing is by custom quote, but early users report good value for replacing multiple point tools. A free trial is available for evaluation.
AWS Glue vs Matia: 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.
Matia
Comprehensive feature set: ETL/ELT, real-time CDC ingestion, reverse ETL, data observability (anomaly detection, schema drift), data catalog with lineage, and orchestration. It covers end-to-end data ops from ingestion to activation.
AWS Glue vs Matia: 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.
Matia
While offering rich built-in modules, Matia allows custom connectors on demand and configurable data quality rules. It abstracts infrastructure management, trading some low-level control for rapid deployment and ease of use.
Summary of AWS Glue vs Matia vs Weld
| Weld | AWS Glue | Matia | |
|---|---|---|---|
| Connectors | 200++ | 50+ | 200+ |
| Price | $99 / Unlimited usage | $0.44 per DPUs-hour (development endpoints) + per-job costs | Custom, unified platform license |
| Free tier | No | Yes | No |
| Location | EU | AWS Global (multi-region) | US |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | No | Yes |
| Transformations | Yes | Yes | Yes |
| AI Assistant | Yes | No | No |
| On-Premise | No | No | No |
| Orchestration | Yes | Yes | Yes |
| Lineage | Yes | Yes | Yes |
| 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 | Yes |
| dbt Core Integration | Yes | Yes | No |
| dbt Cloud Integration | Yes | No | No |
| OpenAPI / Developer API | Yes | No | No |
| G2 Rating | 4.8 | 4.1 | 4.9 |
Conclusion
You’re comparing AWS Glue, Matia, 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. .
- Matia: comprehensive feature set: etl/elt, real-time cdc ingestion, reverse etl, data observability (anomaly detection, schema drift), data catalog with lineage, and orchestration. it covers end-to-end data ops from ingestion to activation.. pricing is by custom quote, but early users report good value for replacing multiple point tools. a free trial is available for evaluation..
- 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..
