The big data and analytics space has been shaken up by the increasing pressure to integrate AI into the business or be left behind. However, diving headfirst without guardrails can be a high-stakes introduction to AI. To help bring new resources and innovation to light, each year, Database Trends and Applications magazine showcases the DBTA 100, a list of forward-thinking companies seeking to expand what's possible with data for their customers. Spanning the wide range of established legacy technologies, from MultiValue to cutting-edge breakthroughs such as AI, semantic layers, data lakehouses, data mesh, and data fabric, the DBTA 100 is a list of hardware, software, and service providers working to enable their customers' data-driven future.
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What technologies for the data-driven era are emerging now? As you might guess, much of the activity seen across the market is connected to AI. At the same time, there is a critical data element that needs both to support AI implementations, as well as be supported by AI. DBTA queried industry leaders to get their views on the most compelling technologies now emerging in this data-driven era.
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According to recent data from the SolarWinds "State of Monitoring and Observability Report," 51% of IT pros believe database performance would benefit from better observability. Oftentimes, database observability suffers because teams have multiple, disparate observability and monitoring tools. This tool sprawl creates blind spots that make it harder to identify and react to database performance issues.
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We hear a lot about AI's benefits to productivity for data teams, but less about the potential risks. The moment the problem becomes real usually looks something like this: an engineer connects an AI assistant to a NoSQL database, such as MongoDB, through an MCP server or a similar tool-calling interface. At the time, they consider it safe to grant the AI direct authentication with their own credentials instead of using best practices such as delegated authorization. It takes twenty minutes. The agent can now query live data, return results, and feed them into whatever workflow it's part of. Nobody updates the access policy, scopes the authorization down, or asks what the agent can actually reach.
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