SAS Data Management and Matillion Data Productivity Cloud are leading solutions in data management. Users tend to prefer SAS for its robust data standardization and integration, while Matillion is favored for its cloud-native features and user-friendly approach.
Features: SAS Data Management offers comprehensive data quality, integration, and governance solutions, providing a unified view of enterprise data with strong ETL capabilities. Matillion Data Productivity Cloud is easy to use in AWS environments, featuring a straightforward graphical interface and excellent integration with various AWS services for agile operations.
Room for Improvement: SAS Data Management could improve by simplifying its pricing model and installation process. Enhancing its interface and technology integration is also needed. Matillion requires more frequent updates for API changes and better real-time data capture features, with improved documentation and broader data source integration.
Ease of Deployment and Customer Service: SAS Data Management operates in on-premises and hybrid cloud environments with strong technical support, though facing critiques on response times. Matillion excels in public cloud deployment, especially through AWS Marketplace, offering agile support services.
Pricing and ROI: SAS Data Management offers high reliability and significant ROI in data-intensive industries despite its high costs. Matillion's competitive pricing, with pay-as-you-go models especially via AWS Marketplace, offers effective cost structures benefiting various organizational sizes.
Consequently, we adjusted our processes to use Matillion Data Productivity Cloud only for extraction and ingestion, while Snowflake handled all transformations and jobs.
Reliable data plus less human intervention and less error result in a strong return on investment.
They communicate effectively and respond quickly to all inquiries.
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.
Depending on the nature of data sets, volume, and mixture of different data, the scalability could be improved as manual code writing is still required.
The autoscale process works well, allowing the system to start another node automatically if the first machine reaches 80% capacity.
The main areas for improvement are AI features and scalability.
Connections to BigQuery for extracting information are complex.
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.
Matillion Data Productivity Cloud offers discounts and special deals, especially when dealing with high-volume clients or fewer existing clients in specific regions, like Spain.
The pricing is moderate, neither expensive nor cheap.
From my experience, SAS Data Management is an expensive tool.
The predefined connectors eliminate the need to write code for connectivity.
Matillion Data Productivity Cloud is effective for ingest functions, particularly when moving information to Snowflake and performing many transformations.
The metadata management feature of SAS Data Management helps a lot; creating your data marts or data lake with good naming conventions, library conventions, and so on is very important because it allows easy queries to find the whole structure, though I think metadata governance also depends on first definitions, not only on the tool.
SAS Data Management's best feature is first, data reliability because SAS Data Management is a very trusted platform.
SAS Data Management stands out because of its data standardization, transformation, and verification capabilities.
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 10 |
| Large Enterprise | 11 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 2 |
| Large Enterprise | 8 |
Matillion Data Productivity Cloud offers a user-friendly platform for seamless integration and dynamic data handling, favored for simplifying ETL processes with minimal coding and ensuring robust performance in complex data tasks.
Matillion Data Productivity Cloud integrates effortlessly with platforms like AWS, Snowflake, and SQL databases, providing tools for efficient data migration, transformation, and cloud warehousing. It supports large datasets with swift management, making it valued for its graphical interface that eases ETL processes for non-technical users. Automation features ensure scalability and dynamic data handling across diverse sources, while security and cost-effectiveness enhance its appeal. Enhancements in database connectivity, interface design, and multi-environment support would refine user experience, with growing demands for real-time data capture, SAP connectivity, and frequent API updates.
What are the most important features?In industries like finance, healthcare, and retail, Matillion Data Productivity Cloud is implemented for transforming data operations. Companies leverage it for its speed in data processing and integration capability, facilitating rapid adaptation to data-driven insights crucial in these sectors.
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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