KNIME vs SAS Visual Analytics comparison

KNIME is an open-source analytics software used for creating data science that is built on a GUI based workflow, eliminating the need to know code. The solution has an inherent modular workflow approach that documents and stores the analysis process in the same order it was conceived and implemented, while ensuring that intermediate results are always available. 

KNIME supports Windows, Linux, and Mac operating systems and is suitable for enterprises of all different sizes. With KNIME, you can perform functions ranging from basic I/O to data manipulations, transformations and data mining. It consolidates all the functions of the entire process into a single workflow. The solution covers all main data wrangling and machine learning techniques, and is based on visual programming.

KNIME Features

KNIME has many valuable key features. Some of the most useful ones include:

• Scalability through data handling (intelligent automatic caching of data in the background while maximizing throughput performance)
• High extensibility via a well-defined API for plugin extensions
• Intuitive user interface
• Import/export of workflows
• Parallel execution on multi-core systems
• Command line version for "headless" batch executions
• Activity dashboard
• Reporting & statistics
• Third-party integrations
• Workflow management
• Local automation
• Metanode linking
• Tool blending
• Big Data extensions

KNIME Benefits

There are many benefits to implementing KNIME. Some of the biggest advantages the solution offers include:

• Integrated Deployment: KNIME’s integrated deployment moves both the selected model, and the entire data model preparation process into production simply and automatically, allowing for continuous optimization in production and also saving time because it eliminates error.

• Elastic and Hybrid Execution: KNIME’s elastic and hybrid executions helps you reduce costs while covering periods of high demand, dynamically.

• Metadata Mapping: KNIME enables complete metadata mapping of all aspects of your workflow. In addition, KNIME offers blueprint workflows for documenting the nodes, data sources, and libraries used, as well as runtime information.

• Guided Analytics: KNIME’s guided analytics applications can be customized based on reusable components.

• Powerful analytics, local automation, and workflow difference: KNIME uses advanced predictive and machine learning algorithms to provide you with the analytics you need. In combination with powerful analytics, KNIME’s automation capabilities and workflow difference prepare your organization with the tools you need to make better business decisions.

• Supports enterprise-wide data science practices: The deployment and management functionalities of KNIME make it easy to productionize data science applications and services, and deliver usable, reliable, and reproducible insights for the business.

• Helps you leverage insights gained from your data: Using KNIME ensures the data science process immediately reflects changing requirements or new insights.

Reviews from Real Users

Below are some reviews and helpful feedback written by PeerSpot users currently using the KNIME solution.

An Emeritus Professor at a university says, “It can read many different file formats. It can very easily tidy up your data, deleting blank rows, and deleting rows where certain columns are missing. It allows you to make lots of changes internally, which you do using JavaScript to put in the conditional. It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured.”

Benedikt S., CEO at SMH - Schwaiger Management Holding GmbH, explains, “All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function. Technical support has been extremely responsive so far. The solution has a very strong and supportive community that shares information and helps each other troubleshoot. The solution is very stable. The initial setup is pretty simple and straightforward.”

Piotr Ś., Test Engineer at ProData Consult, says, “What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.”

SAS Visual Analytics is a data visualization tool that is used for reporting, data exploration, and analytics. The solution enables users - even those without advanced analytical skills - to understand and examine patterns, trends, and relationships in data. SAS Visual Analytics makes it easy to create and share reports and dashboards that monitor business performance. By using the solution, users can handle, understand, and analyze their data in both past and present fields, as well as influence vital factors for future changes. SAS Visual Analytics is most suitable for larger companies with complex needs.

SAS Visual Analytics Features

SAS Visual Analytics has many valuable key features. Some of the most useful ones include:

• Data
• Interactive data discovery
• Augmented analytics
• Chat-enabled analytics
• Sharing and collaboration
• Visual analytics apps
• Embedded insights
• Location analytics
• Security and administration
• In-memory engine

SAS Visual Analytics Benefits

There are many benefits to implementing SAS Visual Analytics. Some of the biggest advantages the solution offers include:

• Machine learning and natural language: SAS Visual Analytics uses machine learning and natural language explanations to find, visualize, and narrate stories and insights that are easy to understand and explain. This enables you to find out why something happened, examine all options, and uncover opportunities hidden deep in your data.

• Easy and efficient reporting: With SAS Visual Analytics, you can create interactive reports and dashboards so you can quickly summarize key performance metrics and share them via the web and mobile devices.

• Easy to use: SAS Visual Analytics was designed to be easy to use. Its easy-to-use predictive analytics enables even business analysts to assess possible outcomes, which also helps organizations make smarter, data-driven decisions.

• Self-service data: Self-service data preparation gives users the ability to import their own data, join tables, create calculated columns, apply data quality functions, and more. In turn, the solution empowers users to access, combine, clean, and prepare their own data in an agile way, which helps facilitate faster, broader adoption of analytics for your entire organization.

Reviews from Real Users

Below are some reviews and helpful feedback written by PeerSpot users currently using the SAS Visual Analytics solution.

A Senior Manager at a consultancy says, “The solution is very stable. The scalability is good. The usability is quite good. It's quite easy to learn and to progress with SAS from an end-user perspective.

PeerSpot user Robert H., Co-owner at Hecht und Heck GmbH, comments, “What I really love about the software is that I have never struggled in implementing it for complex business requirements. It is good for highly sophisticated and specialized statistics in the areas that some people tend to call artificial intelligence. It is used for everything that involves visual presentation and analysis of highly sophisticated statistics for forecasting and other purposes.

Andrea D., Chief Technical Officer at Value Partners, explains, “The best feature is that SAS is not a single BI tool. Rather, it is part of an ecosystem of tools, such as tools that help a user to develop artificial intelligence, algorithms, and so on. SAS is an ecosystem. It's an ecosystem of products. We've found the product to be stable and reliable. The scalability is good.”