

SAS Data Management and Rivery are competing in data management. Rivery appears to have an edge due to its user-friendly interface and innovative features.
Features: SAS Data Management offers comprehensive data quality tools, advanced analytics, and detailed data governance. Rivery provides a no-code platform for seamless data integration, efficient data transformation, and user-friendly ingestion processes. The focus for SAS is on analytics and governance, while Rivery prioritizes ease of integration and user experience.
Ease of Deployment and Customer Service: Rivery enables quick cloud deployment and has responsive customer service, making integration swift. SAS Data Management's complex infrastructure might require more time for deployment but provides detailed support services.
Pricing and ROI: SAS Data Management may have high setup costs but aims for substantial long-term ROI with its analytics capabilities. Rivery offers a more cost-effective entry with a scalable pricing model catering to immediate data integration needs. SAS demands higher initial investment with potential analytics returns, while Rivery is more accessible and scalable.
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.
Reliable data plus less human intervention and less error result in a strong return on investment.
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.
The support for SAS in Brazil is not the best one, but the support in Sweden is really good, as they visit the company and work to solve the issues.
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.
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.
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.
There is significant room for improvement, especially with regard to using a hybrid approach that involves both CAS and persistent storage.
SAS Data Management can be improved in terms of the learning curve.
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.
From my experience, SAS Data Management is an expensive tool.
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.
SAS Data Management stands out because of its data standardization, transformation, and verification capabilities.
The best features I appreciate about SAS Data Management tool are that it's easy to create the flows and schedule data, and the tables are not too big, making it easy to control the ETL process, including user access which is also easy to manage in SAS.
SAS Data Management's best feature is first, data reliability because SAS Data Management is a very trusted platform.
| Product | Mindshare (%) |
|---|---|
| Rivery | 0.7% |
| SAS Data Management | 1.2% |
| Other | 98.1% |

| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 1 |
| Large Enterprise | 3 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 2 |
| Large Enterprise | 8 |
Rivery enhances automation with its built-in pipelines, seamless Snowflake integration, and flexible data management capabilities. It supports extensive connectivity and user-defined functions, aiding efficient data flow management.
Rivery provides a robust platform for automating data ingestion and transformation workflows, integrating effortlessly into data warehouses like Snowflake. Its user-friendly interface and extensive API connectivity simplify data extraction and flow, accommodating diverse needs with custom scripting and user-defined functions. Despite its strengths, improvements are desired in lineage, impact analysis, and advanced visualization, along with better orchestration and logging capabilities. Users also seek price adjustments for smaller organizations and integration with modern AI technologies to elevate analytical capabilities.
What features does Rivery offer?In industries such as retail and finance, Rivery is crucial for managing ETL processes. Retail organizations use it for integrating data from sales channels and customer databases, driving targeted marketing strategies. Finance companies rely on its robust pipelines and Snowflake integration to streamline complex financial data transformations and enhance reporting accuracy.
SAS Data Management provides data integration, governance, and robust reporting tools. It connects to diverse data sources, ensuring quality management and enabling data analysis for technical and non-technical users.
SAS Data Management features flexible data flow creation, scheduling, and ETL control. It enhances data integration and metadata management with tools that support data standardization. Users benefit from its importing and exporting capabilities, connecting to multiple sources. It facilitates improved data quality management and offers a flexible language for diverse needs. Data visualization capabilities further support decision-making across industries, automating reports and data warehouses.
What are the key features of SAS Data Management?SAS Data Management helps industries like finance integrate diverse data sources for analytics and reporting. It is used for tasks such as financial reporting, credit risk analysis, and data cleansing. Through user-driven automation, it aids in aligning data warehouses and generating insightful visual outputs, making it ideal for analyzing structured data from sources like Excel and CSV files.
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