Comparing Google Cloud Dataflow with Weld
What is Google Cloud Dataflow
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
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.
Google Cloud Dataflow vs Weld: Ease of Use and User Interface
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.
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.
Google Cloud Dataflow vs Weld: Pricing Transparency and Affordability
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.
Weld
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.
Google Cloud Dataflow vs Weld: Comprehensive Feature Set
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.
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.
Google Cloud Dataflow vs Weld: Flexibility and Customization
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.
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.
Summary of Google Cloud Dataflow vs Weld
| Weld | Google Cloud Dataflow | |
|---|---|---|
| Connectors | 200++ | 30+ |
| Price | $99 / Unlimited usage | Per vCPU-second ($0.0106/vCPU-minute) + RAM and storage; streaming pipelines incur additional costs |
| Free tier | No | No |
| Location | EU | GCP Global (multi-region) |
| Extract data (ETL) | Yes | Yes |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | No |
| Transformations | Yes | Yes |
| AI Assistant | Yes | No |
| On-Premise | No | No |
| Orchestration | Yes | No |
| Lineage | Yes | No |
| Version control | Yes | No |
| Load data to and from Excel | Yes | Yes |
| Load data to and from Google Sheets | Yes | No |
| Two-Way Sync | Yes | No |
| dbt Core Integration | Yes | No |
| dbt Cloud Integration | Yes | No |
| OpenAPI / Developer API | Yes | No |
| G2 Rating | 4.8 | 4.5 |
Conclusion
When comparing Google Cloud Dataflow and Weld, consider your primary requirements and budget:
- Google Cloud Dataflow excels at 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. . If you need robust transformations and a wide connector catalogue with a strong focus on 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. , this may be your best choice.
- Weld offers competitive pricing and 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.. If you prioritize ease of setup, real-time syncing, or specific integrations, this tool could be a better fit.
