September 2026 Summaries

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Data gravity describes how large datasets become costly and difficult to move because of transfer time, bandwidth constraints, egress fees, latency, and regulatory requirements, drawing compute, applications, and services toward the locations where data resides. The piece argues that enterprise data environments will remain permanently heterogeneous due to operational systems, specialized workloads, organizational autonomy, data-residency laws, and the coexistence of lakes and warehouses, making complete consolidation impractical and potentially increasing vendor lock-in. Rather than centralizing all data, organizations should move processing closer to data and use federated access to provide governed, cross-system analytics and AI context without unnecessary transfers. It presents Starburst’s Trino-based platform as a governed access layer that queries distributed sources, pushes processing to source systems, and can combine lakehouse capabilities with federation. The recommended approach is to inventory where data is large, restricted, or siloed; establish a federated governance layer; and create curated data products with consistent metadata, security, and business definitions so users and AI systems can access reliable enterprise context regardless of where data is stored.