New Azure Data Box capabilities to accelerate your offline data migration

Azure Data Box offline data transfer solution allows you send petabytes of data into Azure Storage in a quick, inexpensive, and reliable manner. The secure data transfer is accelerated by hardware transfer devices that enable offline data ingestion to Azure.

We’re excited to announce several new service capabilities including:

General availability of self-encrypted drives Azure Data Box Disk SKU that enables fast transfers on Linux systems.

Support for data ingestion to multiple blob access tiers in a single order.

Preview of cross-region data transfers for seamless data ingest from source country or region to select Azure destinations in a different country or region.

Support in Azure Storage Mover for online catch-up data copy of any changes active workloads may have generated post offline migrations with Azure Data Box.

Additionally, we’re happy to share the Azure Data Box cloud service is HIPAA/BAA, PCI 3DS and PCI DSS certified. More details on each of these new capabilities can be found below.

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Azure Data Box Disk: self-encrypted drives

Azure Data Box Disk is now generally available in a hardware-encrypted option in the European Union, United States, and Japan. These self-encrypting drives (SEDs) use the dedicated/native/specialized hardware for data encryption, without any software dependency from the host machine. These SEDs use the specialized native hardware present on the disk for data encryption, without any software dependencies on the host machine. With this offering, we now support comparable data transfer rates on Linux as that of our BitLocker-encrypted Data Box Disk drives on Windows.

Azure Data Box Disk SED is popular with some of our Automotive customers as it connects directly to the in-car Linux-based data loggers through a SATA interface, thereby eliminating the need for a secondary data copy from another in-car storage and saving time. Here is how Xylon, manufacturer of automotive data loggers uses Azure Data Box Disk: self encrypted drives to migrate Advanced driver-assistance systems (ADAS) sensor data to Azure: 

Through the cooperation with the Microsoft Azure team, we have enabled direct data logging to the hardware-encrypted Data Box Disks plugged into our logiRECORDER Automotive HIL Video Logger. It enables our common customers to transfer precious data from the test fleet to the cloud in the simplest and fastest possible way, without wasting time on unnecessary data copying and reformatting along the way.” 
—Jura Ivanovic, Product Director, Automotive HIL Video Logger, Xylon 

Learn more about Data Box Disk: self encrypted drives and get started migrating your on-premises data to Azure. 

Multi-access tier ingestion support

You can now transfer data to different blob access tiers including Cold Tier in a single Azure Data Box order. Previously, Azure Data Box only supported transferring data to the default access tiers of Azure Storage Accounts. For example, if you wanted to move data to the Cool tier in an Azure Storage Account that has the default set to hot, you would have had to first move the data to hot tier via Azure Data Box and then leverage life cycle management to move the data to the Cool tier after it’s uploaded to Azure. 

We have now introduced new “access tier” folders in the folder hierarchy on the device. All data that you copy to the “Cool” folder will have it’s access tier set as cool, irrespective of the default access tier of the destination Storage account, and similarly for data copied to other folders representing the various access tiers. Learn more about multi-access tier ingestion support. 

Cross-region data transfer to select Azure regions 

We’re excited to share that Azure Data Box cross-region data transfer capabilities, now in preview, supports seamless ingest of on-premises data from a source country or region to select Azure destinations in a different country or region. For example, with this capability you can now copy on-premises data from Singapore or India to the West United States Azure destination region. Note that the Azure Data Box device isn’t shipped across commerce boundaries. Instead, it’s transported from and to an Azure data Center within the originating country or region where the on-premises data resides. Data transfer to the destination Azure region takes place across the Azure network without incurring additional fees. 

Learn more about this capability and the supported country or region combinations for Azure Data Box, Azure Data Box Disk, and Azure Data Box Heavy respectively. 

Support for online catch-up copy with Azure Storage Mover Integration 

If your data source has any active workloads, it will likely make changes while your Azure Data Box is in transit to Azure. Consequently, you’ll also need to bring those changes to your cloud storage, before a workload can be cut over to it. We’re happy to announce that you can now combine the Azure Storage Mover and Data Box services to form an effective file and folder migration solution to minimize downtime for your workloads. Storage Mover jobs can detect differences between your on-site and cloud storage to effectively transfer any updates and new files not previously captured by your Data Box transfer. For example, if only a file’s metadata (such as permissions) has changed, Azure Storage Mover will upload only the new metadata instead of the entire file content. 

Learn more about how catch-up copies with Azure Storage Mover’s merge and mirror copy mode can help transfer only the delta data to Azure.

Certifications

The Azure Data Box cloud service has achieved HIPAA/BAA, PCI 3DS & PCI DSS certifications. These certifications have been key requests from many of our customers across the healthcare and financial sectors respectively, and we’re happy to have achieved the compliance status to enable our customers’ data transfer needs.

Additional product updates

Support for up to 4 TB Azure files across the product family. 

Support for data transfer to “Poland Central” and “Italy North” Azure regions. 

Transfers to Premium Azure Files and Blob Archive tiers now supported with Data Box Disk. 

The data copy service, which significantly improves the ingestion and upload time for small files, is now generally available.

Our goal is to continually enhance the simplicity of your offline data transfers, and your input is invaluable. Should you have any suggestions or feedback regarding Azure Data Box, feel free to reach out via email at DataBox@microsoft.com. We look forward to you reviewing your feedback and comments.
The post New Azure Data Box capabilities to accelerate your offline data migration appeared first on Azure Blog.
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Announcing a new OpenAI feature for developers on Azure 

We are thrilled to announce the launch of OpenAI’s latest model on Azure. This new model, officially named GPT-4o-2024-08-06, brings innovative features designed to elevate developer experiences on Azure. Specifically, the new model focuses on enhancing productivity through Structured Outputs, like JSON Schemas, for the new GPT-4o and GPT-4o mini models. 

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A focus on Structured Outputs 

GPT-4o was first announced in May 2024, as OpenAI’s new multimodal model, followed by GPT-4o mini in July 2024. Today’s version is designed with a specific use case in mind: simplifying the process of generating well-defined, structured outputs from AI models. This feature is particularly valuable for developers who need to validate and format AI outputs into structures like JSON Schemas. Developers often face challenges validating and formatting AI outputs into well-defined structures like JSON Schemas.  

Structured Outputs addresses this by allowing developers to specify the desired output format directly from the AI model. This feature enables developers to define a JSON Schema for text outputs, simplifying the process of generating data payloads that can seamlessly integrate with other systems or enhance user experiences. 

Use cases for JSON 

JSON Schema is essential for defining the structure and constraints of JSON documents, ensuring they follow specific formats with mandatory properties and value types. It enhances data understandability through semantic annotation and serves as a domain-specific language for optimized application requirements. Development teams use JSON Schema to maintain consistency across platforms, drive model-driven UI constraints, and automatically generate user interfaces. It aids in data serialization, security testing, and partial validation in technical scenarios. JSON Schema also supports automated testing, Schema inference, and machine-readable web profiles, improving data interoperability. It standardizes validation interfaces and reporting, handles external validation, and ensures data consistency within and across documents. It can also help with customer support and how to communicate in a timely manner. 

Two flavors of Structured Outputs 

Structured Outputs is available in two forms: 

User-defined JSON Schema: This option allows developers to specify the exact JSON Schema they want the AI to follow, supported by both GPT-4o-2024-08-06 and GPT-4o-mini-2024-07-18.

More Accurate Tool Output (“Strict Mode”): This limited version lets developers define specific function signatures for tool use, supported by all models that support function calling, including GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, and GPT-4o models from June 2023 onwards. 

Technical guidance on using Structured Outputs 

To help you get started with Structured Outputs, we recommend the following approach. 

Getting started with Structured Outputs 

Define Your JSON Schema: Determine the structure you want your AI outputs to follow. This can include required fields, data types, and other constraints. 

Configure the AI model: Use the Structured Outputs feature to specify your JSON Schema within the API call. This ensures that the AI output adheres to your defined structure. 

Integration and testing: Integrate the output into your application or system, and test thoroughly to ensure compliance with your JSON Schema. 

Example use case: Customer support automation 

Imagine you’re developing a customer support chatbot that needs to generate responses in a specific format for logging and analytics. By using Structured Outputs, you can define a JSON Schema that includes fields like responseText, intent, confidenceScore, and timestamp. This ensures that every response generated by the chatbot is formatted correctly, making it easier to log, analyze, and act upon. 

Example API call 

Here’s an example API call to illustrate how to use Structured Outputs:

{
"model": "gpt-4o-2024-08-06",
"prompt": "Generate a customer support response",
"structured_output": {
"schema": {
"type": "object",
"properties": {
"responseText": { "type": "string" },
"intent": { "type": "string" },
"confidenceScore": { "type": "number" },
"timestamp": { "type": "string", "format": "date-time" }
},
"required": ["responseText", "intent", "confidenceScore", "timestamp"]
}
}
}

Pricing 

We will make pricing for this feature available soon. Please bookmark the Azure OpenAI Service pricing page. 

Learn more about the future of AI

We’ve been rolling out several new models recently, and we understand it can be a lot to keep up with. This flurry of activity is all about empowering developer innovation. Each new model brings unique capabilities and enhancements, helping you build even more powerful and versatile applications. 

The launch of this new model feature for GPT-4o and GPT-4o mini marks a significant milestone in our ongoing efforts to push the boundaries of AI capabilities. We’re excited to see how developers will leverage these new features to create innovative and impactful applications. 

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Craft AI solutions your way

Stay tuned for more updates and get ready to experience the future of AI with these new developer features for GPT-4o and mini. Start experimenting in the Azure OpenAI Playground. 

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The post Announcing a new OpenAI feature for developers on Azure  appeared first on Azure Blog.
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