Analysis and Benchmarking: Maximizing the Benefits of Hotline Data

whitePaper | June 30, 2022

Low code and no code (LCNC) development platforms expedite the creation of new applications with minimal or no coding requirements, as well as offer tools for non-programmers to create applications of their own. Low code development was introduced in the early 2000s to increase developer productivity. It uses a variety of approaches that automate and abstract application development activities, such as drag and drop editors, code generation, component assembly and model-driven development.

whitePaper | October 20, 2022

The digital evolution of the financial services (FS) industry is heavily reliant on having quality information and analytics to deliver services to customers and manage operations in an efficient and risk-appropriate manner.

whitePaper | September 19, 2022

The field of artificial intelligence (AI) traces back to the 1950s. More recently, the increased availability of both data and computing power has bolstered rapid adoption of AI and machine learning (ML) solutions (collectively, AI/ML)

whitePaper | October 20, 2022

Artificial intelligence has already started to have an impact on urban settings at an unprecedented pace, with sophisticated solutions being deployed in the streets, at airports and in other city installations. In fact, cities are becoming experimental sites for new forms of artificial intelligence and automation technologies that are applied across a wide variety of sectors and places.

whitePaper | May 25, 2022

Modernization of data and applications on the mainframe is a necessary priority for modern organizations. Building a MongoDB Operational Data Layer (ODL) is a pattern widely used to make critical mainframe data available for modern mobile and web applications.

whitePaper | December 29, 2022

In our modern world, taking advantage of Artificial Intelligence (AI) to gain insights from data is becoming more prevalent day by day. Graphical Processing Unit (GPU) systems use multiple cores to perform parallel processing, running select workloads to decrease processing times. Compared to GPUs, Central Processing Units (CPUs) have fewer cores; previously, this resulted in less capacity for parallelized processing. To move beyond this limitation, Intel has released new hardware that runs typical AI mathematical computations more efficiently on the CPU, and has also released libraries with hardware optimizations that enable an additional increase in performance.