The Rapid Analytics Platform (RAP) is designed to significantly reduce the time, effort, and costs associated with analyzing large data sets and creating analytic models. This cloud-based solution provides a single, turnkey platform for users to access diverse data assets and run advanced analytics. With its real-time, high-speed processing capabilities, RAP delivers exceptionally fast results, even in complex scenarios. It allows users to quickly and efficiently source data, execute queries, create advanced analytics, and train machine-learning models. Organizations across the mortgage, real estate, and capital markets industries leverage RAP for critical applications such as portfolio valuation, equity analysis, benchmarking, due diligence, and market analysis. RAP provides direct access to a marketplace of diverse data assets, advanced analytics, and third-party data, while also allowing users to incorporate their own data in the same hosted environment. The platform supports multiple programming languages, including SQL, Python, R, and Scala, and offers a familiar integrated development environment (IDE) for writing code and building analytics, including distributed computing, AI, and machine learning models. Hands-on training sessions, including data set coding, are available to get analysts up-to-speed quickly.

Features:

• Programming language flexibility: Multi-language support, including SQL, Python, R and Scala.
• Data updated daily: Data is refreshed daily and managed in the cloud for easy access to the most current information.
• Shared access: Ability to share access to analytics and code snippets across your enterprise.
• Integration with business-intelligence tools: The data and analytics in RAP integrate with Tableau and Power BI; multiple pre-built dashboards are available.
• All-you-can-use: All-you-can-use environment with no additional costs per query.
• Familiar, intuitive user interface: Powerful and familiar IDE for users to write and organize code, run queries and build advanced analytics.
• Sample views: Ability for users to easily view data and analytic samples, schemas and data dictionaries.
• Automated analytics: Analytics can be scheduled to automatically run and be pushed to different endpoints.