Comparing Dataddo with Google Cloud Dataflow and Weld
Carolina Russ6 min read
What is Dataddo
Dataddo is a fully managed, no-code data integration platform designed for business users, marketers, and analysts who need quick access to data without engineering support. It supports over 300 prebuilt connectors to cloud apps, databases, and BI tools, and offers flexible data flows for ETL, ELT, reverse ETL, and dashboard integrations. If a specific connector isn’t available, Dataddo will build it on request. The platform emphasizes ease of use, with automated handling of API changes, built-in pipeline monitoring, and a “SmartCache” feature that temporarily stores historical data, allowing teams to run reports without immediately setting up a data warehouse. With support for secure data practices (SOC 2, ISO 27001, GDPR compliance) and simple, transparent pricing plans, including a free tier, Dataddo is ideal for organizations looking to centralize data quickly and with minimal overhead.
Pros
- No-code interface makes setup simple for non-technical users.
- Integrates with 300+ platforms, including many marketing and CRM tools.
- Onboarding and connector requests are generally well-handled.
- Offers competitive pricing, especially for small teams.
Cons
- Some users report delays for complex issues.
- New or niche sources may not be instantly available.
- Cancelling or modifying plans can be frustrating.
G2 Review:
What I like about Dataddo
It is so user friendly and doesnt have any learning curve. Any user can really understand and create their own custom flows without any external support
What I dislike about Dataddo
If a flow is created, Dataddo needs to introduce how to add more features in the flow (maybe edit columns or add/remove them instead of creating and replacing with a net new flow).
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.
G2 Reviews:
What I like about Google Cloud Dataflow
Google cloud dataflow is automatically optimize and manages resources for you this platform supports multiple programming languages including Python, java and SQL and makes it easy for developers to focus on writing codes
What I dislike about Google Cloud Dataflow
It is costly as compared to other solutions
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
- Lineage, orchestration, and workflow features
- Ability to handle large datasets and near real-time data sync
- ETL + reverse ETL in one
- User-friendly and easy to set up
- Flat monthly pricing model
- 200+ connectors (Shopify, HubSpot, etc.)
- AI assistant
Cons
- Requires some technical knowledge around data warehousing and SQL
- Limited features for advanced data teams
- Focused on cloud data warehouses
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.
Feature-by-Feature Comparison
Ease of Use & Interface
Dataddo
Dataddo offers a clean, intuitive no-code interface that allows users to set up data flows quickly. The drag-and-drop flow builder and prebuilt connectors minimize the learning curve, making it accessible for non-technical 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.
Pricing & Affordability
Dataddo
Pricing is straightforward and competitive, with plans starting at $99/month for three data flows. The free tier allows users to test the platform with limited functionality before committing to a paid plan.
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.
Feature Set
Dataddo
Dataddo supports over 300 connectors, ETL/ELT workflows, reverse ETL capabilities, data transformations, and built-in monitoring.
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.
Flexibility & Customization
Dataddo
While Dataddo is primarily designed for ease of use, it still offers flexibility through its wide range of connectors and the ability to create custom data flows. However, it may not provide the same level of customization as more technical platforms.
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 Dataddo vs Google Cloud Dataflow vs Weld
| Weld | Dataddo | Google Cloud Dataflow | |
|---|---|---|---|
| Connectors | 200+ | 398+ | 30+ |
| Price | $79 / 5M Active Rows | $99.00 / mo for 3 data flows to sync data between any source and destination | Per vCPU-second ($0.0106/vCPU-minute) + RAM and storage; streaming pipelines incur additional costs |
| Free tier | No | Yes | No |
| Location | EU | US/EU | GCP Global (multi-region) |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | Yes | 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 | No | Yes |
| Load data to and from Google Sheets | Yes | Yes | No |
| Two-Way Sync | Yes | Yes | No |
| dbt Core Integration | Yes | No | No |
| dbt Cloud Integration | Yes | No | No |
| OpenAPI / Developer API | Yes | No | No |
| G2 Rating | 4.8 | 4.7 | 4.5 |
Conclusion
You’re comparing Dataddo, Google Cloud Dataflow, Weld. Each of these tools has its own strengths:
- Dataddo: dataddo supports over 300 connectors, etl/elt workflows, reverse etl capabilities, data transformations, and built-in monitoring. . pricing is straightforward and competitive, with plans starting at $99/month for three data flows. the free tier allows users to test the platform with limited functionality before committing to a paid plan..
- 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 $79 for 5 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises..
