If you're going to modernize your database, start small but with purpose. Add CDC, set up a streaming backbone, switch to an open table format, and test out a vector-enabled feature. Add HTAP to make workflows easier, and use AIOps to take care of routine tuning, recovery, and finding problems.
Frequently Asked Questions
Do you have Questions for Future of Database Architecture: Designing Scalable and AI-Driven Data Systems ?
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AI-driven database architecture improves scalability, automates routine operations, enhances query performance, and enables real-time insights, helping businesses reduce latency and accelerate decision-making.
Custom software development tailors database solutions to your unique business needs, ensuring seamless integration of distributed, cloud-native, and AI-powered features to modernize your data systems effectively.
Costs depend on data volume, complexity, cloud infrastructure, automation levels, and integration with AI capabilities. Early planning and staged modernization reduce unexpected expenses.
They use distributed databases, sharding, read replicas, and cloud‑native deployments to scale horizontally and elastically, maintaining performance and availability as data grows.
Many organizations see initial improvements within 30‑90 days by implementing CDC, real‑time streaming, and automation, with further gains as HTAP and vector‑enabled analytics mature.
By implementing policy‑as‑code, data catalogs, lineage tracing, access controls, and audit pipelines from the start, ensuring compliance with regulations like GDPR and CCPA.
Yes, our IT consulting expertise guides organizations in designing multi‑cloud, portable data platforms that decouple storage and compute for flexibility and disaster recovery.
Avoid lift‑and‑shift without redesign, premature sharding, over‑indexing without measurement, neglecting schema evolution, treating vector stores as temporary caches, ignoring data deletion/compliance, and lacking clear SLOs.
