Comparing Rivery with StreamSets Data Collector and Weld
Carolina Russ6 min read
What is Rivery
Rivery provides a SaaS ELT-based Data Integration platform that enables the seamless loading of data from diverse sources and custom API-based platforms into your preferred data warehouse. While River lacks on-the-fly transformations, it offers Post-load Transformations as compensation.
Pros
- Supports custom integrations though native GUI
- Has reverse ETL option
- Supports Python
- Has data transformation capabilities
- Great customer support
Cons
- Lack of advanced error handling features
- Cannot transform data on the fly (ETL)
- Complex pricing model
- UI is lacking when working with larger complex pipelines
- Product documentation is lacking
As a user on G2 puts it::
What I like about Rivery
As a data analyst, I find the tool really easy to use; it's intuitive how you connect to the different data sources and create your data pipelines.
What I dislike about Rivery
What is StreamSets Data Collector
StreamSets Data Collector is an open-source data integration engine built for continuous ingestion, transformation, and delivery—often referred to as a DataOps platform. It supports both streaming (Kafka, Kinesis) and batch (JDBC, files) data sources, with a drag-and-drop canvas to design pipelines. The standout feature is Schema Drift Detection: pipelines automatically adapt to changes in incoming data schemas. Commercial editions add operational monitoring, metadata management, and lineage.
Pros
- Schema Drift Detection automatically adjusts to incoming data changes, preventing many pipeline breaks.
- Supports both streaming (Kafka, Kinesis, JMS) and batch (JDBC, files) in the same pipeline.
- Drag-and-drop pipeline builder with over 200 connectors and transformation processors.
- Open-source core (Data Collector); enterprise edition adds operational monitoring, lineage, and governance.
Cons
- Open-source lacks robust monitoring and lineage features; must pay for the Data Ops Platform for full enterprise functionality.
- UI performance can degrade for very large pipelines; memory usage can be significant.
- Steep learning curve for advanced pipeline patterns, especially around custom scripting in Groovy or Java.
StreamSets Data Operations Platform:
What I like about StreamSets Data Collector
StreamSets’ ability to automatically detect and adapt to schema changes (drift) in streaming sources greatly reduces pipeline failures.
What I dislike about StreamSets Data Collector
The open-source feature set is limited—monitoring, lineage, and enterprise support require the paid Data Ops Platform. Debugging complex pipelines can be tricky if not familiar with the UI.
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.
Feature-by-Feature Comparison
Ease of Use & Interface
Rivery
Rivery is known for its ease of use, especially for data analysts who need to connect different data sources and create pipelines quickly. Its intuitive GUI makes setup straightforward.
StreamSets Data Collector
The Data Collector UI is a canvas where users drag origin, processor, and destination stages. Schema drift is highlighted automatically. While basic pipelines are easy to build, complex transformations may require custom scripting in Groovy/Java.
Pricing & Affordability
Rivery
Rivery's pricing is complex and based on credits, which may not be straightforward for all users. Costs can rise significantly with increased data usage.
StreamSets Data Collector
Data Collector is free, but enterprise features (monitoring, lineage, role-based access) require paid Data Ops Platform licenses. Pricing is custom based on number of nodes and connectors.
Feature Set
Rivery
The platform supports custom integrations, Python scripting, and reverse ETL, making it versatile for various data integration needs, but lacks on-the-fly transformation capabilities.
StreamSets Data Collector
Features: streaming & batch pipelines, schema drift detection, transformation processors (masking, joins, lookups), origin/destination connectors (Kafka, S3, HDFS, JDBC), and enterprise ops (alerting, lineage, governance) in paid edition.
Flexibility & Customization
Rivery
Rivery offers flexibility in custom integrations and supports post-load transformations, but its user interface may lack robustness for managing larger, more complex pipelines.
StreamSets Data Collector
Supports custom processors in Groovy/Java for bespoke logic. Pipelines can be parameterized and deployed in containers or VMs. Integration with external schedulers (Airflow) and monitoring tools (Prometheus, Grafana).
Summary of Rivery vs StreamSets Data Collector vs Weld
| Weld | Rivery | StreamSets Data Collector | |
|---|---|---|---|
| Connectors | 200+ | 200+ | 200+ |
| Price | $79 / No data volume limits | $0.75 per credit *100MB of data replication | Data Collector: Free (OSS); Data Ops Platform: Custom enterprise pricing |
| Free tier | No | Yes | Yes |
| Location | EU | US | San Francisco, CA, USA |
| 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 | Yes |
| Orchestration | Yes | No | Yes |
| Lineage | Yes | No | Yes |
| Version control | Yes | No | Yes |
| Load data to and from Excel | Yes | No | Yes |
| Load data to and from Google Sheets | Yes | No | No |
| Two-Way Sync | Yes | No | No |
| dbt Core Integration | Yes | No | No |
| dbt Cloud Integration | Yes | No | No |
| OpenAPI / Developer API | Yes | Yes | No |
| G2 Rating | 4.8 | 4.7 | 4.5 |
Conclusion
You’re comparing Rivery, StreamSets Data Collector, Weld. Each of these tools has its own strengths:
- Rivery: the platform supports custom integrations, python scripting, and reverse etl, making it versatile for various data integration needs, but lacks on-the-fly transformation capabilities.. rivery's pricing is complex and based on credits, which may not be straightforward for all users. costs can rise significantly with increased data usage..
- StreamSets Data Collector: features: streaming & batch pipelines, schema drift detection, transformation processors (masking, joins, lookups), origin/destination connectors (kafka, s3, hdfs, jdbc), and enterprise ops (alerting, lineage, governance) in paid edition. . data collector is free, but enterprise features (monitoring, lineage, role-based access) require paid data ops platform licenses. pricing is custom based on number of nodes and connectors. .
- 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..
