In recent years, governments across the globe have recognized the transformative potential of artificial intelligence (AI) and have embarked on initiatives to harness this technology to drive innovation and serve their citizens more effectively. These government-led efforts have had a profound impact on the development and adoption of AI solutions in the public sector, paving the way for a future where data-driven decision-making and automation are the norm.

GenAI depends on data maturity, in which an organization demonstrates mastery over both integrating data – moving and transforming it – and governing its use.

When monetizing APIs, specific ways exist to entice customers to begin using your services. Much of the time, users like to see some value before they commit to becoming a full-blown paid customer. One of the most popular billing providers we see used for API monetization is Stripe, and thankfully, Stripe supports free trial models. Paired with Moesif, you can easily adhere to what model you require to support your API monetization needs.

As software testers, we are not just cogs in a machine, executing tests, writing reports, and meeting deadlines. We are professionals who can make a difference in the world. We can find purpose in our testing and use it as a guiding light through the challenges we face. Kristel Kruustük, the founder of Testlio, is living proof that finding a bigger sense of meaning and purpose can transform not only our work but also our lives.

Recently we introduced one breaking change in how user information is exported from Countly, and we wanted to explain why such change was made and what to do to keep your plugins supported. But before we dive into why's, let's first reiterate the "what" part.

Microsoft SQL is a popular relational database management system organizations use to manage and store data. As data volumes grow, extracting, transforming, and loading data has become essential for many businesses.

Like peanut butter and jelly, ETL and data modeling are a winning combo. Data modeling can't exist without ETL, and ETL can't exist with data modeling. Not if you want to model data properly. Combining the two defines the rules for data transformations and preps data for big data analytics. In the age of big data, businesses can learn more than ever about their customers, identify new product opportunities, and so on.