

Pentaho Data Integration and Analytics and SAS Data Management are both leaders in the ETL tools category. Pentaho, with its open-source nature, seems to have the upper hand due to its cost-effectiveness and ease of use.
Features: Pentaho supports a wide range of data integrations with its intuitive drag-and-drop interface, rapid ETL development, and big data technology compatibility, facilitating the parallel processing of complex workflows. SAS Data Management provides robust metadata management, a comprehensive data integration environment, and reliable data governance features, which enable effective data quality and regulatory compliance.
Room for Improvement: Pentaho can improve by enhancing its handling of extremely large data volumes and optimizing its real-time data processing capabilities. Users also report occasional stability issues during upgrades. SAS Data Management could become more cost-transparent, streamline its complex pricing structure, and simplify user interactions for non-technical personnel.
Ease of Deployment and Customer Service: Pentaho offers straightforward deployment options and is known for its supportive user community, which compensates for its less comprehensive official support. SAS Data Management enjoys a reputation for strong technical support but may require more advanced skills for deployment due to its complex features.
Pricing and ROI: Pentaho's Community Edition stands out for being free without licensing fees, appealing to businesses looking for budget-friendly solutions. Its potential for significant cost savings, coupled with the flexibility for plugin integrations, enhances ROI. SAS Data Management, although offering extensive features, requires a higher investment with its complex pricing structure, which may limit accessibility for smaller businesses, but offers significant capabilities for high-budget deployments.
I have seen a return on investment; my team was able to stay extremely small even though we had a lot of data integrations with many companies.
I can testify to the return on investment with metrics regarding time saved; we have increased our efficiency by about 20 to 30 percent due to the swift migration processes facilitated by the tool.
I have noticed a return on investment with Pentaho Data Integration and Analytics in terms of time savings and staff reduction.
Reliable data plus less human intervention and less error result in a strong return on investment.
24/7 assistance is available for the Enterprise Edition.
take the time to understand our business requirements, offering appropriate recommendations.
Communication with the vendor is challenging
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 can be scaled well until you reach a point where you need to perform a lot of operations, and the issue arises when it runs out of memory to handle some data.
Its ability to scale horizontally in cloud-native architectures or for massive real-time processing is limited.
Pentaho Data Integration handles larger datasets better.
Performance issues arise due to reliance on a flowchart-based mechanism instead of scripts, which can lead to longer execution times.
I find that version 3.1 is the most stable version I have ever used.
It's pretty stable, however, it struggles when dealing with smaller amounts of data.
We should also explore more effective partitioning for parallel processing and fine-tuning database connections to reduce load times and improve ETL speed.
Pentaho Data Integration and Analytics can be improved by working with different environments, specifically the possibility to change the variables, meaning I write my variables only once and can change them for different environments such as production or development.
Pentaho Data Integration and Analytics could have real-time processing and automatic alerting, having alerts or automatic notifications when a job fails or when certain data doesn't meet certain rules.
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 use the community version of Pentaho Data Integration and Analytics, and I do not need additional costs.
The setup cost was minimal, and the pricing experience was pretty good.
The company covered it and they had no problem paying for it because they saw that it was cost-effective in terms of performance afterwards.
From my experience, SAS Data Management is an expensive tool.
Pentaho Data Integration and Analytics has positively impacted my organization because it meant we didn't have to write a lot of custom API back-end processing logic; it did the majority of that heavy lifting for us.
It automates the data workflow, including extraction, cleansing, and loading into warehouses for BI reporting purposes, while also removing duplicates, validating data, and standardizing formats, enabling real-time decision-making.
Pentaho Data Integration and Analytics has positively impacted my organization because it is easier to use, and my knowledge about this work facilitates the translation from the source to my final system.
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 (%) |
|---|---|
| Pentaho Data Integration and Analytics | 1.7% |
| SAS Data Management | 1.2% |
| Other | 97.1% |


| Company Size | Count |
|---|---|
| Small Business | 18 |
| Midsize Enterprise | 17 |
| Large Enterprise | 32 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 2 |
| Large Enterprise | 8 |
Pentaho Data Integration and Analytics offers an intuitive platform for data workflows, enabling users to easily manage ETL processes across diverse data formats, ensuring seamless automation and development.
With its drag-and-drop interface, Pentaho allows for efficient ETL workflows without extensive coding. It supports a multitude of data formats and sources such as SQL, NoSQL, Hadoop, CSV, and JSON. Advanced features like metadata injection and API integration enable seamless automation. However, improvements in big data performance, better cloud service integration, and enhanced real-time processing capabilities can enhance user experience. Additional connectors and improved documentation are sought after by many. Providing support for more programming languages and optimizing memory usage also presents opportunities for enhancement.
What are the key features of Pentaho Data Integration and Analytics?Pentaho is employed across finance, healthcare, and retail industries for ETL processes. It's instrumental in integrating data from ERP, SAP systems, Excel, and APIs to develop comprehensive reports and data models. Companies rely on its capabilities for both on-premises and cloud deployments, improving data transparency and management.
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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