Today, we are announcing the most comprehensive and compelling migration offer available in the industry to help customers simplify their cloud analytics journey. This collaboration between Microsoft and Informatica provides customers an accelerated path for their digital transformation.
Migrating hundreds of SQL Server instances and thousands of databases to Azure SQL Database, our Platform as a Service (PaaS) offering, is a considerable task, and to streamline the process as much as possible, you need to feel confident about your relative readiness for migration.
Last July, I shared our approach to helping customers migrate to Azure. Today, we are bringing together a best practice-based, holistic experience for migrating existing applications and systems to Azure.
How do you migrate live, mission-critical data for a flagship product that must manage billions of requests with low latency and no downtime? The Consumer Business Unit at Symantec faced this exact challenge when deciding to shift from their costly and complex self-managed database infrastructure, to a geographically dispersed and low latency managed database solution on Azure.
In June 2018, we released the App Service Migration Assessment Tool. The Assessment Tool was designed to help customers quickly and easily assess whether a site could be moved to Azure App Service by scanning an externally accessible (HTTP) endpoint.
The blob storage interface on the Data Box has been in preview since September 2018 and we are happy to announce that it's now generally available. This is in addition to the server message block (SMB) and network file system (NFS) interface already generally available on the Data Box.
Azure Data Box offline family lets you transfer hundreds of terabytes of data to Microsoft Azure in a quick, inexpensive, and reliable manner. Today, we are excited to share that support for managed disks with the Azure Data Box family of devices is now available and includes support for Data Box, Data Box Disk, and Data Box Heavy.
Migrating on-premises Apache Hadoop® and Spark workloads to the cloud remains a key priority for many organizations. In my last post, I shared “Tips and tricks for migrating on-premises Hadoop infrastructure to Azure HDInsight.”
With the increasing benefits of cloud-based data warehouses, there has been a surge in the number of customers migrating from their traditional on-premises data warehouses to the cloud.
We’re committed to ensuring that you can run your workloads reliably on Azure. One of the areas we’re investing heavily into optimizing reliability is using the combination of machine learning and live migration to predict and proactively mitigate potential failures.