Rivery vs. Fivetran

Rivery vs. Fivetran – Overview

About Rivery

Rivery was founded in 2018 from an internal tool at one of the world’s largest data consulting firms. The company is headquartered in New York City with offices in Tel Aviv, London, and Austin. Rivery is a fully-managed SaaS platform, which provides a unified solution for data ingestion, transformation, and workflow orchestration. The biggest differentiator is the completeness of Rivery’s offering, combining the ease-of-use of a no-code SaaS with the power and flexibility of a custom-coded solution.

About Fivetran

Fivetran is a managed extract and load tool that was created in 2012. Fivetran is headquartered in Oakland, California with offices around the world. They have a large customer base and are considered an easy-to-use choice for their ease of connection. Fivetran charges users on monthly active rows (MAR), which is the number of unique primary keys moved from source to target within one calendar month.

Rivery vs. Fivetran – Features

ETL / ELT Tool
Pricing	Based on executions and data size	Based on rows
Cloud native and fully managed	Yes	Yes
Native connectors	200+	150+
Connect to any API source	Yes	Not natively, only with a Cloud Function
Built-in workflow orchestration	Yes	No
Python support	Yes	No
Reverse ETL	Yes	No
Built-in data transformation	Yes	No
Data Solutions Accelerators	Yes	No
Support SLAs	Yes	Yes
Developer Friendly	Yes	No
G2 Rank	4.8 / 5.0	4.2 / 5.0

Rivery vs. Fivetran – Product

Rivery

Rivery’s SaaS offering provides several data stack products in a single platform. This includes ELT (data ingestion) with over 200+ fully-managed connectors, advanced workflow orchestration, data operations (full support for the development lifecycle), reverse ETL (data activation), and full Python support.

Fivetran

Fivetran is an EL (extraction and load) tool, where the transformation component of the ETL/ELT process is done by an embedded version of dbt Core.

Fivetran provides 150+ native connectors with support for all main databases and data warehouses as targets. Custom connectors are possible with Fivetran, but they must be built via Cloud Functions, which are then called by Fivetran. These Cloud Functions come at an additional cost to run and require maintenance.

Building and maintaining custom connectors in-house and outside of Fivetran can be done to handle custom data needs outside of Fivetran’s capabilities. 

Rivery vs. Fivetran – Pricing

Rivery

Rivery provides both, on-demand pay-as-you-go pricing, and annual contracts. Both pay-as-you-go and annual pricing are available for all plans. 

Pricing is based on a credit system, which starts at $0.75 per credit. Database source pricing is based on total volume of data transferred. App and API-source pricing is based on the number of pipeline executions. 

Rivery doesn’t charge by the number of databases or tables you’re loading into your data warehouse, nor does it charge by the number of rows or sync frequency.

All Rivery pricing plans include: 

• Access to all 200+ fully-managed connectors 
• Unlimited users 
• Runtime environments 
• Reverse ETL 
• Change Data Capture 

Learn more about Rivery pricing on Rivery’s pricing page or click here to learn more about Rivery’s approach to pricing.

Fivetran

Fivetran’s pricing is consumption based. Their pricing model is controlled by monthly active rows (MAR), which is the number of unique primary keys moved from source to target within one calendar month. For some use cases this can make Fivetran’s pricing difficult to estimate.

Fivetran offers a 14-day free trial for new users. 

Rivery vs. Fivetran – Support

Rivery

Rivery offers 24/7 global support as well as onboarding services and dedicated customer success services, which vary by plan. Support is engineering led and year over year, Rivery has been awarded “Easiest to do Business with” by G2 in the category of ETL tools. 

Rivery’s G2 user rating for quality of support is 9.9/10, the highest in its category with an industry average of 8.5/10 for ETL tools on G2. 

Fivetran

Fivetran offers support through in-platform support tickets. They do not provide training services as part of their support offerings. 

Fivetran’s G2 user rating for quality of support is 7.8/10 with an industry average of 8.5/10 for ETL tools on G2.

Rivery vs. Fivetran – Summary

Fivetran

Fivetran is a great option for teams looking for an easy-to-use data integration tool. If you are looking to implement an end-to-end ETL/ELT solution you’ll have to pair Fivetran with other tools. For example, you’ll need to leverage different tools like, dbt for transformation, Airflow for orchestration, and Hightouch or Census for reverse ETL.

Key benefits when evaluating Fivetran include: 

• Ease of connection
• ERD/normalized schemas for each SaaS solution
• Embedded dbt Core with custom dbt models

Limitations of Fivetran as an ETL/ELT tool include: 

• No proprietary transformation capabilities
• Custom connectors need to be built and maintained via Cloud Functions
• No developer capability to allow you to extend Fivetran capabilities
• No reverse ETL capabilities
• No orchestration capabilities

Rivery

Rivery is an end-to-end platform rather than just an extract and load tool, like Fivetran. Rivery offers data orchestration, data transformation, and reverse ETL capabilities for every plan out of the box. 

In regards to data transformation, Rivery offers ETLT processes, where you can complete in-flight transformations of data using Rivery’s industry-first Python integration as well as push-down transformations. 

For users looking for enterprise-ready solutions, Rivery comes with a number of developer capabilities, including: 

• Built-in Rivery version history management
• Context-sensitive variables
• Multiple environments
• Metadata collection
• Audit logging

When evaluating connectors and customizability, Rivery offers a simple custom connector to connect to any API source without the need for an external cloud function. Fivetran connections are tied to their best practices as it pertains to datasets, which in certain use cases could create connection inflexibility.