In today's data-driven world, companies are constantly challenged by the need to process and analyze massive streams of data in real-time. Without efficient solutions in place, they risk falling behind in an increasingly competitive landscape. Let's delve into the top three challenges faced by companies in the absence of Data Stream Processor (DSP) and explore how this technology is

The healthcare system across the world is going through unprecedented pressure due to the ongoing COVID-19 pandemic. It is still unknown when the world will return to full recovery. This makes it more difficult for hospitals, health systems, and healthcare staff to find a solution on how to reduce pressure in the future.

Digital transformation was already a top priority for banks as they were facing extreme competition from much larger banks and fintech companies. The main challenge for banks is to stay ahead of others by offering faster and more convenient customer service options. But with the ongoing pandemic, banks are facing uncertainty and much tougher challenges to stay afloat. The demand for providing the

Powers full lifecycle AI management from data exploration to modeling to production – MLOps, ModelOps, Edge Analytics and DataOps

The rise of data and how organizations are increasingly becoming data driven With huge amounts of data being generated every second from various sources such as business transactions, industrial machinery and social media, data is becoming the fuel that is driving any business. Organizations are relying on data and analytics to run every aspect of their business ranging from optimizing business

By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modelling – Gartner Data-driven businesses are finding ways to utilize the gargantuan amount of data that is collected from their interactions with IoT devices, social networking platforms, industrial equipment, manufacturing work processes, etc.

Risk is one area, which is inevitable in any business, but then, as times are changing, rather than avoiding risk, companies are investing in how to detect, analyze and take preventive measures to manage their risks better.

The global Precision Farming/Agriculture market was valued at USD 5.3 Billion in 2018 and is expected to reach USD 14.1 Billion by the year 2026, at a CAGR of 12.7% (Source: Reports and Data)

Introduction The Convolutional Neural Network (CNN), one of the deep learning models, has seen a lot of success in a variety of computer vision applications. The current approach of deciding a CNN architecture and tuning the hyperparameters of it is either decided by heuristics or by trial and error.

Introduction Neo4J is one of the most popular graph databases. But, unfortunately, ingesting large volumes of data take a lot of time as Neo4J tries to acquire lock on nodes before it creates/updates a node.