Legacy systems have been important in the growth and success of several organizations. However, as these systems and applications age, they slow down and become increasingly costly to operate and manage. As per the U.S. Government Accountability Office, operating and maintaining legacy systems consumes over 80% of IT expenses. Organizations must scale and update these systems to accommodate their evolving business needs. This is where legacy application modernization can help.

Significant API adoption, management, and integration with emerging technologies like Generative AI underscore the crucial role of API in modern digital landscapes. Recent research shows that companies integrating APIs experienced a significant 12.9% surge in market capitalization. Moving a step further, Gartner predicts that by 2026, more than 80% of enterprises will have employed Generative AI APIs or implemented applications enabled by Generative AI.

A robust, modern data platform is the starting point for your organization’s data and analytics vision. At first, you may use your modern data platform as a single source of truth to realize operational gains — but you can realize far greater benefits by adding additional use cases. In this blog, we offer guidance for leveraging Snowflake’s capabilities around data and AI to build apps and unlock innovation.

The Health Insurance Portability and Accountability Act (HIPAA), established in 1996, keeps individual health information private. It introduced HIPAA EDI (Electronic Data Interchange) Standards to secure electronic health data sharing among healthcare providers, insurers, and related entities. Healthcare EDI sets rules for how data should be formatted when electronically exchanged to ensure secure and efficient communication and reduce manual tasks.

Like many other industries, fintech has also been witnessing the integration of cognitive technologies as a key driving force behind recent transformations. Powerful tools, such as artificial intelligence (AI) and machine learning, are empowering businesses with swift transaction processing, precise data analysis, and optimized resource utilization. Thus, it is apt to say that a future with sustainable fintech software development cannot be mutually exclusive to the aspirations of cognitive technologies.

When you run your Go programs or integration tests, numbers of raw coverage files are typically generated and dumped into a directory specified by the GOCOVERDIR environment variable. These files contain valuable data about which parts of your code were executed during tests, offering a glimpse into your code’s effectiveness and robustness. However, sifting through these raw files to extract actionable insights can be daunting and unclear for many developers.

In an era where data breaches generate headlines and data privacy is an increasing concern, how can organizations use the massive amounts of data they acquire without jeopardizing individual privacy? The solution could be found in a unique amalgamation of data anonymization approaches. As we dive into this fascinating topic, we discover how technology is transforming how we anonymize data, ensuring that the insights we gain are useful and respectful of privacy.

AI and generative Al can lead to major enterprise advancements and productivity gains. By offering new capabilities, they open up opportunities for enhancing customer engagement, content creation, virtual experts, process automation and optimization, and more.

When it comes to keeping your OAuth Client Credentials endpoints secure and high-performing, monitoring is crucial. Whether you’re a non-technical business owner, a software engineer, a student, a product owner, a web agency, or a DevOps professional, understanding and implementing effective monitoring can save you from a lot of headaches. In this article, we’ll dive into the top 10 best tools for monitoring OAuth endpoints, helping you find the best fit for your needs.