

SAS Data Management and StreamSets both fall within the data management category, specializing in ETL and data integration solutions. While SAS Data Management offers a robust suite of tools suitable for complex enterprise needs, StreamSets provides a simpler, more accessible platform for non-technical users.
Features: SAS Data Management provides powerful data quality, integration, and governance features, along with strong ETL and data analysis capabilities designed for complex data environments across diverse platforms. StreamSets offers a user-friendly no-code platform, focusing on real-time data processing, connectivity options, and effectively managing data drift and transformations.
Room for Improvement: SAS Data Management could enhance its documentation, simplify database connectivity and integration with non-SAS tools, and address its expensive licensing model. StreamSets needs improvements in logging mechanisms, UI enhancements, real-time processing capabilities, and addressing high licensing costs and technical support issues.
Ease of Deployment and Customer Service: SAS Data Management operates primarily on-premises, offering moderate support, with some reports of complex technical problem-solving difficulties. StreamSets supports diverse deployment options in public, hybrid, and on-premises clouds, praised for simple deployments but noted for expensive support and limited knowledge base resources.
Pricing and ROI: SAS Data Management is known for its high cost, justified by comprehensive features leading to significant ROI, especially in industries with stringent data needs. StreamSets provides an open-source version, but its paid versions are costly, offering a flexible pricing model and delivering high ROI through efficient data operations despite occasional costly updates.
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.
IBM technical support sometimes transfers tickets between different teams due to shift changes, which can be frustrating.
There is significant room for improvement, especially with regard to using a hybrid approach that involves both CAS and persistent storage.
It would be beneficial if StreamSets addressed any potential memory leak issues to prevent unnecessary upgrades.
From my experience, SAS Data Management is an expensive tool.
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 stands out because of its data standardization, transformation, and verification capabilities.
It allows a hybrid installation approach, rather than being completely cloud-based or on-premises.
| Product | Mindshare (%) |
|---|---|
| StreamSets | 1.2% |
| SAS Data Management | 1.3% |
| Other | 97.5% |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 1 |
| Large Enterprise | 8 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 2 |
| Large Enterprise | 11 |
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.
StreamSets streamlines data pipeline creation, connecting data from multiple sources to destinations like cloud platforms with minimal coding. Its centralized platform and intuitive design enhance ETL and data migration processes.
StreamSets integrates seamlessly with analytics platforms, offering tools such as Data Collector and Control Hub to facilitate data ingestion, transformation, and machine learning integrations. Its user-friendly interface and ready connectors aid in configuring complex data pipelines. With built-in data drift resilience and scheduling options, users experience efficient, scalable data management, despite challenges like latency in cloud storage and interface enhancement needs. Users often employ StreamSets for batch loading, real-time data processing, and smart data pipeline management, offering comprehensive data integration solutions.
What are the key features of StreamSets?In industries like finance and technology, StreamSets supports data migration, machine learning integrations, and analytics by simplifying data transformation and enhancing decision-making capabilities through its robust pipeline management.
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