dukicn explores how Silk’s software-defined storage platform and Silk Echo for AI, in partnership with Microsoft Azure, deliver high-performance, resilient, and cost-effective data management solutions for AI, database migration, and enterprise cloud workloads.

Enhancing Azure Data Management with Silk Software-Defined Storage and Silk Echo for AI

Co-authored by Silk

Data agility and resilience are paramount for modern enterprises leveraging Microsoft Azure. In this article, silk’s software-defined storage platform and the new Silk Echo for AI are presented as powerful solutions for maximizing performance, optimizing resource use, and enabling advanced AI, analytics, and database scenarios in the cloud.

Key Challenges in Cloud Data Management

When migrating workloads such as SQL Server, Oracle, or DB2 to Azure, organizations often encounter:

Performance bottlenecks due to cloud storage limitations
Data copy proliferation for non-production environments
Operational overhead tied to backups, refreshes, and storage management

Silk’s Platform: Software-Defined Storage on Azure

Runs on Azure IaaS as a performance-optimized data layer
Harnesses Azure compute and native storage, including L-series VMs and NVMe
Delivers up to 34GB/s throughput per VM
Employs erasure coding for multi-zone resilience
Provides inline deduplication and compression
Enables zero-cost, autonomous snapshots and clones for rapid environment refreshes

Silk Echo for AI: Next-Gen Copy Data Management

Silk Echo for AI adds intelligence via AI-powered data copy orchestration and management:

AI-driven recommendations automate and optimize data snapshot and clone workflows
Instant, space-efficient cloning reduces storage cost and accelerates dev/test
Cross-environment consistency ensures reliable and synchronized copies
Policy-based lifecycle management keeps data governance and compliance simple
Resource optimization through deduplication and compression
AI/ML workflow enablement by providing fresh, production-grade data clones to data science teams

Real-World Example: Sentara Health

Migrating EHR and SQL Server workloads to Azure, Sentara Health drastically cut down environment refresh time from days/weeks to minutes. Development and data science teams benefit from rapid self-service access to production-grade data clones, supporting faster iteration and reliable model training, all running on resilient Azure-powered infrastructure.

Silk and Microsoft: Joint Value

Data-as-a-Service: Gives DevOps, DataOps, and AI/ML teams on-demand access to up-to-date snapshots
AI-ready Infrastructure: AI/ML supports predictive issue mitigation and real-time inferencing over operational data
Lower Costs: Storage optimization and reduced manual intervention
Seamless Cloud Migrations: Migrates tier 1 workloads to Azure with minimal refactoring

Get Started

To explore how Silk’s software-defined platform and Silk Echo for AI can modernize your data infrastructure on Azure, or to take your AI and analytics projects further, connect with Silk at Alliances@silk.us.

Updated Nov 04, 2025 – Version 1.0

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