Comparing AWS Glue with Google Cloud Dataflow 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 Google Cloud Dataflow
Google Cloud Dataflow is a fully managed stream and batch processing service based on Apache Beam. It enables users to write ETL pipelines in Java or Python, which Dataflow executes on Google’s serverless infrastructure with autoscaling. It integrates natively with Pub/Sub, BigQuery, Cloud Storage, and other GCP services for end-to-end data processing.
Pros
- Unified batch + streaming model via Apache Beam SDK (Java/Python).
- Serverless autoscaling with dynamic work rebalancing for cost and performance optimization.
- First-class integration with GCP services: Pub/Sub, BigQuery I/O connectors, Cloud Storage, Spanner, etc.
- Built-in exactly-once processing semantics and windowing capabilities for streaming ETL.
Cons
- Steep learning curve if unfamiliar with Apache Beam’s abstractions (PCollections, DoFns, pipelines).
- Monitoring and debugging streaming pipelines can be complex—metrics and logs often require cross-referencing.
- Cost can rise quickly for large-scale streaming (billed per vCPU-second and memory). Efficient pipeline tuning is critical.
Cloud Dataflow Documentation:
What I like about Google Cloud Dataflow
Dataflow’s unified model for batch and streaming simplifies pipeline development—write once and choose your execution mode. Autoscaling and dynamic work rebalancing ensure efficient resource use.
What I dislike about Google Cloud Dataflow
Debugging streaming jobs can be challenging; understanding Apache Beam semantics is essential. Costs can spike if pipelines aren’t carefully tuned.
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 Google Cloud Dataflow: 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.
Google Cloud Dataflow
Dataflow pipelines are defined programmatically in Java or Python (Apache Beam). There is no drag-and-drop UI; developers use the Cloud Console or CLI to monitor, but pipeline creation and debugging happen in code and SDKs.
AWS Glue vs Google Cloud Dataflow: 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.
Google Cloud Dataflow
Charges for each pipeline based on vCPU-second, memory, and persistent disk usage. Streaming jobs are billed continuously. Without careful optimization (autoscaling, batching), costs can escalate. However, for high-throughput workloads, serverless autoscaling can be cost-effective versus self-managed clusters.
AWS Glue vs Google Cloud Dataflow: 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.
Google Cloud Dataflow
Features include: Batch & streaming unified model, windowing & triggers, exactly-once semantics, dynamic work rebalancing, and data-driven autoscaling. Supports FlexRS (spot pricing for batch) and integration with Dataflow SQL for SQL-based pipelines.
AWS Glue vs Google Cloud Dataflow: 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.
Google Cloud Dataflow
Users write custom transforms (ParDo, Map, GroupBy), can integrate UDFs, and use side inputs. Complex workloads requiring custom logic (stateful processing, custom connectors) are fully supported via Beam SDK. Cloud features like VPC, IAM, and KMS integrate security.
Summary of AWS Glue vs Google Cloud Dataflow vs Weld
| Weld | AWS Glue | Google Cloud Dataflow | |
|---|---|---|---|
| Connectors | 200+ | 50+ | 30+ |
| Price | €99 / 2 connectors | $0.44 per DPUs-hour (development endpoints) + per-job costs | Per vCPU-second ($0.0106/vCPU-minute) + RAM and storage; streaming pipelines incur additional costs |
| Free tier | No | Yes | No |
| Location | EU | AWS Global (multi-region) | GCP Global (multi-region) |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | No | No |
| Transformations | Yes | Yes | Yes |
| 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 | Yes |
| 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 | 4.5 |
Conclusion
You’re comparing AWS Glue, Google Cloud Dataflow, 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. .
- Google Cloud Dataflow: features include: batch & streaming unified model, windowing & triggers, exactly-once semantics, dynamic work rebalancing, and data-driven autoscaling. supports flexrs (spot pricing for batch) and integration with dataflow sql for sql-based pipelines. . charges for each pipeline based on vcpu-second, memory, and persistent disk usage. streaming jobs are billed continuously. without careful optimization (autoscaling, batching), costs can escalate. however, for high-throughput workloads, serverless autoscaling can be cost-effective versus self-managed clusters. .
- 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..
