Comparing Keboola with Rivery and Weld
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What is Keboola
Pros
- A very wide range of features
- Option to build custom components withe API first approach
- Transformations that support both ELT and ETL
- Fully managed data platform
- Good academy to learn
Cons
- Complex UI
- Orchestration features are lacking
- Can quickly become expensive with the credit consumption pricing model
A user on G2 said::
What I like about Keboola
Keboola is powerful in extracting data from endless sources. In keboola you can organize your analytics and build your models with ease.
What I dislike about Keboola
What is Rivery
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 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.
Keboola vs Rivery: Ease of Use and User Interface
Keboola
Keboola is powerful for users who need extensive customization and flexibility in their data operations, but its complex UI can be a challenge for beginners.
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.
Keboola vs Rivery: Pricing Transparency and Affordability
Keboola
Keboola's pricing can become expensive quickly due to its credit consumption model, making it a better fit for users who can optimize their usage effectively.
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.
Keboola vs Rivery: Comprehensive Feature Set
Keboola
The platform offers a wide range of features, including support for both ELT and ETL transformations, and is well-suited for advanced users needing comprehensive data management capabilities.
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.
Keboola vs Rivery: Flexibility and Customization
Keboola
Keboola provides extensive flexibility with options to build custom components using an API-first approach, but this flexibility requires a deep understanding of the platform's capabilities and architecture.
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.
Summary of Keboola vs Rivery vs Weld
Weld | Keboola | Rivery | |
---|---|---|---|
Connectors | 150+ | 200+ | 200+ |
Price | $99 / 2 connectors | Custom price | $0.75 per credit *100MB of data replication |
Free tier | No | Yes | Yes |
Extract data (ETL) | Yes | Yes | Yes |
Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | Yes | Yes |
Version control | Yes | No | No |
Orchestration | Yes | No | No |
Lineage | Yes | No | No |
AI Assistant | Yes | No | No |
Load data to and from Excel | Yes | No | No |
Load data to and from Google Sheets | Yes | No | No |
Two-Way Sync | Yes | No | No |
G2 Rating | 4.8 | 4.7 | 4.7 |
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