

Azure Data Factory and Rivery compete in the data integration space, each offering distinct advantages. Azure Data Factory is advantageous for those leveraging Microsoft Azure services heavily, while Rivery focuses on automation and scalability, appealing to businesses managing large data volumes.
Features: Azure Data Factory provides native integration with Azure services, advanced data orchestration, and comprehensive data transformation options. Rivery offers automated data pipelines, outstanding scalability, and easy adaptation to dynamic environments, catering to complex data workflows.
Ease of Deployment and Customer Service: Azure Data Factory may present deployment challenges but benefits from robust support within the Microsoft ecosystem. Rivery features a straightforward, no-code deployment, making it accessible to organizations with limited technical expertise. Both offer extensive customer support, though Rivery's simplified approach can appeal to teams with fewer resources.
Pricing and ROI: Azure Data Factory's flexible pricing aligns with Microsoft's other services, creating potential cost efficiencies for users within the ecosystem. Rivery's pricing structure is more transparent and can seem higher; however, the potential for significant ROI from its advanced capabilities is attractive for data-centric operations.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
It saved my team time and really reduced manual work, so overall, it improved efficiency.
By using Snowflake and Rivery, I was able to set up and complete project goals myself without the necessity to employ additional data engineers or DevOps.
The technical support from Microsoft is rated an eight out of ten.
The technical support is responsive and helpful
The technical support for Azure Data Factory is generally acceptable.
One significant challenge was implementing custom-built Python scripts using Rivery for transformations.
Customer support is great; they are answering really fast.
The customer support for Rivery is excellent.
Azure Data Factory is highly scalable.
It has handled growing data volumes and additional pipelines without major issues.
The focus is on the ability to connect to different sources and to put all the data together.
The solution has a high level of stability, roughly a nine out of ten.
I found the tool very easy to use, allowing me to gain a lot of insights.
The excellent support we received from Rivery team contributes to this perception.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
There is a problem with the integration with third-party solutions, particularly with SAP.
As an end-to-end solution for ETL with Snowflake, Rivery has proven to be reliable and efficient in my day-to-day work.
Agentic AI with open source tools can be used to build all configurations automatically for pipelines.
One feature that stood out in Informatica was the ability to see data flowing through each transformation step while debugging, which I felt was missing in Rivery.
The pricing is cost-effective.
It is considered cost-effective.
I found myself asking my stakeholder to make it only five times a day because it was really expensive.
I found the pricing and licensing to be fair and competitive compared to other solutions I have seen.
It connects to different sources out-of-the-box, making integration much easier.
The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.
Regarding the integration feature in Azure Data Factory, the integration part is excellent; we have major source connectors, so we can integrate the data from different data sources and also perform basic transformation while transforming, which is a great feature in Azure Data Factory.
Rivery saved time and money because everything was handled in one place by only one or two data people instead of using the resources of a development team, which is great, and all the knowledge is handled in one team.
The main benefit Rivery brought to my organization was the time we were able to save on development.
Rivery has positively impacted my organization by reducing the need for a big team of data engineers and speeding up the work when we need to connect to a new data source; this can happen really fast.
| Product | Market Share (%) |
|---|---|
| Azure Data Factory | 3.0% |
| Rivery | 0.6% |
| Other | 96.4% |

| Company Size | Count |
|---|---|
| Small Business | 31 |
| Midsize Enterprise | 19 |
| Large Enterprise | 57 |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 1 |
| Large Enterprise | 3 |
Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.
Rivery is a serverless, SaaS DataOps platform that empowers companies of all sizes around the world to consolidate, orchestrate, and manage internal and external data sources with ease and efficiency.
By offering comprehensive data solutions and partnering with complementary technology providers, including Google, Snowflake, Tableau, and Looker, Rivery enables data-driven companies to build the perfect ecosystems for all their data processes.
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