WPS Analytics
A powerful and versatile software platform
for scalable data manipulation and analytics
Access multiple data sources, including big data
Use standalone or with enterprise management facilities including data access control and model deployment to on-demand APIs
Flexible deployment to link on-premise, mainframe and cloud environments
One unified analytics platform
Build data-focused applications using visual workflows and coding. Data science, ETL, predictive modeling, data mining, algorithm and API development
Workbench
An intuitive user interface for analysts and data scientists with visual workflows, mining, modeling and coding tools for beginners to advanced users. Powered by a high-performance data Processing Engine, the Workbench can be used as a standalone solution on your PC, or connected to a centralized WPS Analytics infrastructure on a server or cloud platform
Hub
Centralized control for data access, model and program deployment and analytics API publishing for on‑demand and real-time scoring
Processing Engine
An ultra-robust, scalable back-end to the WPS Analytics platform, that connects to data and runs programs, workflows and models in development and production. With a built-in SAS language compiler the Processing Engine can run applications that mix the languages of SAS, SQL, Python, and R
Who uses WPS Analytics?
Data Engineer
"I want a coding tool"
Data Analyst
“I want a coding and workflow tool”
Data Scientist
"I want a data workflow tool"
Data Modeler
“I want a coding and workflow tool”
DevOp
“I need a streamlined toolset”
Access old and new data
WPS Analytics is designed to handle large data volumes and includes data access to all data sources, including Hadoop, data warehouses, databases, SAS and WPS datasets, Excel spreadsheets, SPSS, web data, JSON and XML
Flexible deployment
Choose a deployment architecture that fits now, with options to adapt and enhance easily in the future. Desktop, server, mainframe, datacenter, cloud
From basic to advanced in one platform
- Coders can mix the languages of SAS, SQL, Python and R
- Non-coders can use a drag-and-drop workflow
- Coders can extend workflows with code blocks
- Create repeatable ETL and data prep processes
- Interactive modeling tools
- Scorecard building
- Collaboration for sharing workflows between different groups and skill sets
