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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With the growing deployment of AI agents, these tools are exposing a flaw in observability: To cut costs, teams routinely filter or offload the very data those systems depend on. Without full historical context to validate outputs and understand patterns, AI performance degrades, and teams aren't understanding why. The limiting factor for enterprise AI isn't just the AI model—it's also the data platform underneath it.
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The Internet of Things (IoT) has been around as a concept for almost 2 decades now, with great promise and hype about tying far reaches of organizations into a single flowing network of interactive data. But there is still a lot of work to be done, especially in assuring that the data moving between the edge and more centralized systems is timely, viable, and accurate.
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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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