More often in today’s society of growing ‘entrepreneurs’, almost all are developing Minimum Viable Products MVPs that breathe life into their concepts. A shocking but true statistic reveals that 90% of startups fail. One big reason for this is the poor execution of a key tool in the startup world: the minimum viable product (MVP). One of the reasons for this failure is the lack of tests conducted appropriately.

This year's Gartner Hype Cycle for Data Management report mentions self-service data management. It’s a game-changer that gives business users the power to work with data without constantly relying on IT, boosting data quality and making data available for analytics and decision-making. But what is it, really? How do you achieve self-service? Let’s take a closer look.

In the fast-paced world of software development, the efficiency of build processes plays a crucial role in maintaining productivity and code quality. At ThoughtSpot, while Gradle has been effective, the growing complexity of our projects demanded a more sophisticated approach to understanding and optimizing our builds. This requirement prompted us to explore Build Analytics—harnessing data from our build processes to gain actionable insights.

Fivetran Managed Data Lake Service brings automation, governance and reliability to data lakes.

In the world of data management, clearinghouses often serve as intermediaries, enabling secure data transactions across different platforms and ensuring compliance with industry regulations. However, as data management needs evolve, businesses are increasingly looking for alternatives to clearinghouses that offer more flexibility, control, and advanced features.

What’s one thing all your favorite legal shows have in common? Whether it’s Suits or The Lincoln Lawyer, they rarely show the amount of paperwork lawyers have to handle on a daily basis. Understandably so, paperwork isn’t the most glamorous part of the job but that doesn’t mean it’s not crucial. In fact, lawyers deal with tens, if not hundreds, of documents on a daily basis during most parts of their job, such as discovery, research, or drafting.

Did you know that the AI market size is likely to reach USD 826.73 billion dollars by 2030? Yes, you read it right! The use of artificial intelligence can be seen in every field. AI in UX design is bringing about a revolution in the way users interact with technology. Traditionally, UX design relied on designer intuition, manual processes, and labor-intensive user testing. No doubt, the conventional approach had a positive impact on the user experience.

To enhance security and ease operational burden, many organizations with data lakes or lakehouses want flexibility to securely integrate their tools of choice on a single copy of data. An open standard for storage format and catalog API has helped, but there’s still a need for open standards for the catalog, including a consistent way to apply security access controls to data.

Imagine your app faces a sudden surge in users. Thousands log in at once. Can your system handle the pressure, or will it crash?

There is a sense of growing maturity in the DevOps market that is also reflected by its evolving tools. For the past some time, I’ve also seen these tools expanding beyond the limited utilities of continuous integration and continuous delivery. They are now seeping into database management, QA automation, and even AI integration in the SDLC. However, I see no deviation in their efficiency that enables teams to transition smoothly from ITOps to DevOps.