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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For more than a decade, enterprises have been locked in a cycle of building increasingly complex data stacks to keep pace with the demands of modern analytics. Warehouses, OLAP engines, and streaming systems have all played their part. However, as data volumes have increased, customer-facing use cases have proliferated, and costs have risen, the cracks in legacy approaches are widening.
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Preparing a presentation on the Internet of Things (IoT) in asset management for an executive rail infrastructure audience does something unexpected to you—it forces you to confront not just the technology, but the human cost of its absence. The deeper I dug into the research, the more I realized the story I was building for that room demanded a wider audience. What follows is the article that preparation forced me to write.
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