Building Data Foundations for the AI Era

/ AI, Data Management, DevOps, Infrastructure
Building Data Foundations for the AI Era

The News

CData Software announced its annual Foundations 2025 virtual conference, a free two-day event focusing on building enterprise data foundations for analytics and AI. The event, split between September 17 (Data & Analytics Day) and September 24 (AI Day), will feature speakers from AWS, Databricks, Red Wing Shoes, and ServiceNow.

Analysis

The application development and modernization market is undergoing a shift: analytics pipelines and AI workflows increasingly depend on real-time, governed data access. According to theCUBE Research, data integration remains one of the top three investment priorities for enterprises modernizing IT, with AI and real-time analytics acting as primary drivers. Developers are under pressure to not only unify data silos but also ensure that data pipelines can support AI-native architectures. Industry demand is expanding from batch analytics to streaming, low-latency data integration that feeds both dashboards and AI inference engines.

Why This Conference Matters for Developers

By centering its Foundations 2025 event on both analytics and AI, CData is spotlighting the dual challenge developers face: building reliable analytics environments while preparing infrastructure for AI agents and workflows. Sessions on customer stories from companies like Red Wing Shoes and Databricks could provide concrete examples of how organizations approach these challenges in production. For developers, exposure to both practical integration strategies and thought leadership around AI-ready data pipelines may offer context to guide their own architecture decisions.

Traditional Approaches to Data and AI Connectivity

Historically, developers have tackled analytics and AI integration by cobbling together point solutions (custom ETL pipelines, API gateways, and middleware) that often lacked governance, security, or real-time responsiveness. This approach introduced latency and complexity, making it difficult to extend infrastructure for emerging use cases like AI agents that require continuous data context. As we have noted, fragmentation in tooling remains a major barrier to modernization, particularly when developers need to move quickly from proof-of-concept to enterprise scale.

A Forward-Looking Model for AI-Ready Data

The Foundations 2025 agenda reflects a shift toward unified, platform-level solutions where data connectivity, governance, and scalability converge. Developers attending may find insights into how organizations are building hybrid and multi-cloud integration frameworks that enable both analytics and AI applications. With initiatives like the Model Context Protocol gaining attention, the industry trend suggests that developers will increasingly rely on standardized approaches to connect enterprise data with AI systems, rather than hand-rolled integrations. While results will vary depending on maturity and environment, the trajectory points to less friction, faster experimentation, and improved governance.

Looking Ahead

The global demand for AI-ready data infrastructure is only accelerating. theCUBE Research projects that as enterprises adopt AI agents and reasoning models, governed, real-time data pipelines will become foundational to competitive advantage. Conferences like Foundations 2025 illustrate how the market is moving beyond incremental analytics improvements toward holistic strategies that enable intelligent applications.

For CData, the event is a platform to demonstrate how its ecosystem aligns with these shifts. For developers, the broader takeaway is that data connectivity is no longer a back-end concern; it is a front-line enabler of AI and analytics innovation. What comes next may be a stronger convergence between open protocols, enterprise governance requirements, and developer tooling designed to make AI integration seamless.

Author

  • Paul Nashawaty

    Paul Nashawaty, Practice Leader and Lead Principal Analyst, specializes in application modernization across build, release and operations. With a wealth of expertise in digital transformation initiatives spanning front-end and back-end systems, he also possesses comprehensive knowledge of the underlying infrastructure ecosystem crucial for supporting modernization endeavors. With over 25 years of experience, Paul has a proven track record in implementing effective go-to-market strategies, including the identification of new market channels, the growth and cultivation of partner ecosystems, and the successful execution of strategic plans resulting in positive business outcomes for his clients.

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