This simplifies complex cloud-to-cloud data migrations, especially from AWS S3 to Azure Blob, reducing operational overhead and costs. Engineers can now securely and efficiently move large datasets, accelerating multicloud strategies and leveraging Azure's advanced analytics and AI.
In today’s modern world, cloud strategies are evolving rapidly. Many organizations are embracing multicloud environments, while others are looking to consolidate and migrate workloads to a single trusted platform. At Microsoft, we recognize that the need for secure, reliable, and efficient data movement across platforms is critical. Today, we are announcing the General Availability of cloud-to-cloud migration from AWS S3 to Azure Blob Storage using Azure Storage Mover.
Since its launch in 2023, Azure Storage Mover has been simplifying on-premises data migrations for organizations of all sizes, making large-scale data transfers to Azure faster, more secure, and less complex. For those who are unfamiliar, Azure Storage Mover is a free, fully managed migration service designed to move data from Files Shares and NAS Storage into Azure Object and File storage with minimal disruption. It enables efficient, scalable, and reliable data transfers via Azure’s centralized orchestration; while also maintaining file metadata and supporting both one-time migrations and sync tasks without requiring custom scripts or third-party tools.
Azure Storage Mover’s new cloud-to-cloud migration capability enables direct transfers from AWS S3 to Azure Blob. Unlike on-premises migrations, cloud-to-cloud transfers do not require a self-hosted agent, simplifying setup and eliminating additional compute requirements. This approach reduces infrastructure costs and migration overhead; no agents, no scripts, and fully managed. Key capabilities include:
During public preview, customers have already realized the benefits of cloud-to-cloud migration and transferred petabytes (PBs) of data from AWS to Azure. For example, one of our customers, Syncro, in partnership with SOUTHWORKS, migrated hundreds of terabytes from AWS S3 to Azure Blob with minimal downtime. The phased migration approach, enabled by Storage Mover, allows for complex orchestration, data integrity, and immediate access to Azure’s analytics and AI capabilities.
Syncro, a leading provider of IT management solutions, faced the challenge of migrating hundreds of terabytes of data from AWS S3 to Azure Blob. Using Azure Storage Mover, SOUTHWORKS completed the pilot migration, transferring 60 TB in the first phase and planning ongoing migrations for approximately 120 TB. The solution enabled complex, phased migrations, maintained data integrity, and leveraged Azure’s advanced analytics and AI capabilities immediately upon arrival.
— Johnny Halife, CTO, SOUTHWORKS
Beyond simplifying migration, Azure Storage Mover opens the door to a broader ecosystem of innovation, helping organizations maximize the value of their data once it’s in Azure. Migrating AI training data into Azure Blob Storage allows organizations to quickly leverage advanced artificial intelligence and machine learning capabilities. With immediate access to powerful Azure tools, teams can develop, train, and deploy models at scale to unlock innovation and accelerate insights across the Azure ecosystem.
We’re also excited to announce several new service capabilities for Azure Storage Mover, including support for additional source and target pairs:
Azure Storage Mover’s cloud-to-cloud migration capabilities are now available to simplify your multicloud journey and accelerate digital transformation. Get started today!
The post Fully managed cloud-to-cloud transfers with Azure Storage Mover appeared first on Microsoft Azure Blog.
Continue reading on the original blog to support the author
Read full articleIt provides a managed, high-availability storage solution that ensures zero data loss and seamless failover across availability zones. This simplifies disaster recovery for mission-critical workloads like SAP HANA and SQL Server while optimizing costs and metadata performance.
Distinguishing between reliability, resiliency, and recoverability prevents architectural anti-patterns. It ensures engineers don't over-invest in recovery when resiliency is needed, or assume redundancy alone guarantees a reliable customer experience.
PostgreSQL is evolving into a central hub for AI development. By integrating vector search, LLM orchestration, and seamless IDE workflows directly into the managed database service, Microsoft reduces the friction of building and scaling intelligent, data-driven applications.
Azure Storage is shifting from passive storage to an active, AI-optimized platform. Engineers must understand these scale and performance improvements to architect systems capable of handling the high-concurrency, high-throughput demands of autonomous agents and LLM lifecycles.
