Comparing Rivery with Stitch and Weld

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Rated 5 stars on G2

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

Read full review

What is Stitch

Acquired by Talend in 2018, Stitch Data is a cloud open-source and developer-focused ELT platform that offers no-code connectors to bring data into your warehouse. Stitch offers compatibility with a broad range of data sources and destinations. It also enables customers to construct new sources based on Singer's standards, an open-source toolkit designed for scriptwriting.

Pros

Easy to setup and use
A cost-friendly pricing model that's easy to understand and based on usage
Integrates with Talend
Includes transformations with JSON
Fully managed no-code ELT data pipelines

Cons

Require deep technical knowledge to get full value out of the platform
The quality can vary a lot between different connectors as they are not maintained by Stitch
Depends on the Singer open source framework which can break without notice

A reviewer on Gartner said::

What I like about Stitch

Stitch is an affordable tool for bringing data "as is" from the various data sources (Google Ads, Salesforce or even MySQL) to a data warehouse (Redshift, Snowflake, BigQuery, etc.)

What I dislike about Stitch

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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.

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Rivery vs Stitch: Ease of Use and User 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.

Stitch

Stitch is relatively easy to set up and use, offering no-code connectors and an affordable pricing model, but users may need deep technical knowledge to leverage its full potential.

Rivery vs Stitch: Pricing Transparency and 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.

Stitch

Stitch's pricing model is cost-friendly and based on usage, making it accessible for many users. However, costs can increase if data volumes grow significantly.

Rivery vs Stitch: Comprehensive 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.

Stitch

Stitch offers fully managed no-code ELT data pipelines and integrates with Talend, but relies heavily on the Singer open-source framework, which can be less stable.

Rivery vs Stitch: Flexibility and 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.

Stitch

Stitch is designed for simplicity and ease of use, but customization is limited and dependent on the capabilities of the open-source Singer framework.

Summary of Rivery vs Stitch vs Weld

	Weld	Rivery	Stitch
Connectors	150+	200+	140+
Price	$99 / 2 connectors	$0.75 per credit *100MB of data replication	$100 / 5M rows
Free tier	No	Yes	Yes
Extract data (ETL)	Yes	Yes	Yes
Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL)	Yes	Yes	No
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	4.5

Why choose Weld?

Gather all your data in one place

Tired of scattered data? Weld consolidates all your data in one location with our no-code data pipelines.

Spend less time on manual reporting

Suffering from data headaches? Spend less time manually juggling data in Excel and Google Sheets and more time getting real insights.

Reliability and quality

All our connectors are built in-house to ensure the highest quality and reliability with 99.9% uptime across more than 150,000 daily syncs.

Combine and transform your data with AI

Easily transform your data into meaningful metrics with our SQL AI Assistant, Ed.

Award winning ETL Platform

Across industries, data-driven companies choose Weld to sync their data seamlessly

"Before Weld, our data was very simple. We didn’t have a clear data strategy and relied heavily on Excel files and Power BI for dashboarding our reports."

Temur Makhsudov, Head of BI and Operations