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Unlocking Advanced Data Analytics & AI with Azure NetApp Files object REST API

GeertVanTeylingen

Microsoft

Jan 15, 2026

Azure NetApp Files object REST API enables object access to enterprise file data stored on Azure NetApp Files, without copying, moving, or restructuring that data. This capability allows analytics and AI platforms that expect object storage to work directly against existing NFS based datasets, while preserving Azure NetApp Files’ performance, security, and governance characteristics.

Abstract

Introduction

Technical Primer: What is the Azure NetApp Files object REST API?

Applying object REST API in Practice: Integration Scenarios and Use Cases

Quick Bytes: Azure NetApp Files object REST API Overview

How-to: Integrating Azure NetApp Files object REST API with Microsoft OneLake

How-to: Integrating Azure NetApp Files object REST API with Azure Databricks

Quick Bytes: Accelerating AI Insights with Microsoft Discovery AI and Azure NetApp Files

How These Videos Fit Together

Summary

Learn More

Abstract

Azure NetApp Files object REST API enables object access to enterprise file data stored on Azure NetApp Files, without copying, moving, or restructuring that data. This capability allows analytics and AI platforms that expect object storage to work directly against existing NFS‑based datasets, while preserving Azure NetApp Files’ performance, security, and governance characteristics.

This blog builds on How Azure NetApp Files Object REST API powers Azure and ISV Data & AI services – on YOUR data and goes deeper into applied integration patterns, highlighting real‑world scenarios with Azure Databricks and Microsoft OneLake. We explain how the object REST API works, the architectural patterns it enables, and where it fits within modern analytics and AI workflows. Companion videos are included to help architects and solution teams build a clear mental model for when and how to use this capability in practice.

Co-authors:

Thomas Willingham, Azure NetApp Files Technical Marketing Engineer
Sean Luce, Azure NetApp Files Product Manager
Asutosh Panda, Azure NetApp Files Technical Marketing Engineer

Introduction

As organizations expand their use of analytics and AI services, they are increasingly constrained not by compute availability, but by data access. Many enterprise file datasets already reside on high performance file storage such as Azure NetApp Files, while modern analytics platforms and AI services often expect object-based access patterns. Integrating file-based enterprise data with object centric platforms typically requires copying data into separate object stores – adding cost, complexity, and operational overhead.

The Azure NetApp Files object REST API addresses this challenge by exposing existing Azure NetApp Files volumes through an S3-compatible object interface – providing what is called ‘file/object duality’; the same (file) data remains in place on Azure NetApp Files and can be accessed using traditional file protocols (NFS/SMB) as well as via REST based object operations, depending on the needs of the consuming service. This allows analytics and AI workloads to operate directly on enterprise data, without introducing additional storage layers or data movement pipelines.

In this blog, we provide a technical overview of how the object REST API is implemented, how it maps object semantics onto existing file systems, and how it integrates with commonly used platforms such as Azure Databricks and Microsoft OneLake. The objective is to give architects and solution teams a clear understanding of the architecture and integration patterns, so they can evaluate where the object REST API fits within their broader data and AI strategies.

Technical Primer: What is the Azure NetApp Files object REST API?

At a technical level, the Azure NetApp Files object REST API provides an S3-compatible REST interface over existing Azure NetApp Files volumes. In essence, it allows you to treat files stored on an Azure NetApp Files volume as objects in a bucket, enabling dual access: file protocols (NFS/SMB) and object REST API protocol on the same data. This duality means an application can write a file via NFS, and another application can read it back via an S3 GET request (or vice versa), all without data copying. The object interface maps a specified directory on the volume to an S3 bucket name. Files under that directory become objects in the bucket (with keys corresponding to file paths).

Key capabilities and requirements:

Capability and Enablement Model: The Azure NetApp Files object REST API is now available. It incorporates certificate-based trust to securely expose object access on Azure NetApp Files volumes. This model ensures that object access is deliberate, scoped, and aligned with enterprise security and governance expectations during early adoption.
Bucket Abstraction on Azure NetApp Files Volumes: Object access is enabled through a bucket abstraction that maps a logical object namespace onto a directory within an existing Azure NetApp Files volume. The bucket defines the scope of object visibility and serves as the root for object operations. This design allows object-based access without altering how data is organized or managed at the file level.
Access Control via Object Credentials: The object REST API uses access keys that follow familiar S3 authentication models, allowing object‑aware applications and services to authenticate without requiring changes to existing file‑based access patterns. Credentials are lifecycle‑managed and scoped to the bucket context, supporting secure integration with analytics and AI platforms that expect object‑level authentication.
S3‑Compatible Object Operations: The object REST API supports core S3 operations required for analytics and AI workflows, including object listing, read, write, and delete. This operational scope is intentionally focused on enabling interoperability with platforms such as Azure Databricks, Microsoft OneLake using shortcuts, and other object‑centric services, rather than replicating the full surface area of traditional object storage platforms.
Enterprise Security and Network Integration: Object REST API access is secured using TLS with certificate‑based authentication and is fully integrated with Azure virtual networking. Azure NetApp Files volumes remain deployed within customer virtual networks, and object access adheres to the same enterprise security boundaries and compliance standards as file access. This ensures that sensitive data remains protected while being made available to a broader set of analytics and AI consumers.
Single‑Copy Data Access (No Data Movement): A defining capability of the object REST API is that it exposes the same physical data through both file and object interfaces. This eliminates the need to maintain separate object storage copies for analytics workloads, reducing duplication, operational overhead, and data latency. Analytics and AI services can operate directly on data as it is produced, enabling near‑real‑time insights without introducing additional storage layers or data pipelines. This real-time integration is at the heart of the object REST API’s value proposition.

With this primer in mind, let’s explore how this capability is applied in practice. The following sections walk through two primary integration scenarios with OneLake (Microsoft Fabric) and Azure Databricks and then briefly highlight additional use cases (Azure AI services and partner solutions). Each scenario includes a description of what it enables, key architectural considerations, and a link to a companion demo video that walks through configuration details.

Applying object REST API in Practice: Integration Scenarios and Use Cases

To complement the architectural concepts described above, the following videos walk through how the Azure NetApp Files object REST API is applied across common analytics and AI scenarios. Each video serves a distinct purpose ranging from a high-level conceptual overview to hands-on configuration and deeper integration examples.

Readers can choose the level of depth most relevant to their role or use case.

Quick Bytes: Azure NetApp Files object REST API Overview

Best starting point

This short Quick Bytes video provides a concise introduction to the Azure NetApp Files object REST API. It explains why the feature exists, how it enables object-based access to existing file data, and where it fits modern analytics and AI architectures. The video focuses on the core value proposition of S3-compatible access, dual protocol support, and zero data movement without going into configuration details, making it a useful starting point before exploring deeper scenarios.

How-to: Integrating Azure NetApp Files object REST API with Microsoft OneLake

Unified governance and downstream analytics

This video focuses on exposing Azure NetApp Files data into Microsoft OneLake using shortcuts, enabling Fabric and downstream services to operate on file-based enterprise data as part of a unified data estate. It highlights how object REST API enables virtualization of Azure NetApp Files data inside OneLake, supporting governed analytics, search, and AI workflows without duplicating datasets.

How-to: Integrating Azure NetApp Files object REST API with Azure Databricks

Analytics and machine learning workflows

This walkthrough demonstrates how Azure Databricks can access enterprise data stored on Azure NetApp Files through the object REST API. It shows how Spark based analytics and machine learning workloads can read and write data using familiar S3 semantics, while the data itself remains stored on Azure NetApp Files. This integration enables real time analytics and model development without requiring data to be copied into separate object storage systems.

Quick Bytes: Accelerating AI Insights with Microsoft Discovery AI and Azure NetApp Files

Advanced AI and HPC driven scenarios

This advanced scenario shows cases of how the Azure NetApp Files object REST API supports high performance AI and scientific discovery workloads. It illustrates how simulation and HPC generated files stored on Azure NetApp Files can be accessed directly by AI agents through object interfaces, enabling near real time analysis and insight generation. This example demonstrates how object REST API extends beyond traditional analytics into emerging AI and agent driven workflows while still operating on a single, governed copy of data.

How These Videos Fit Together

Start with Quick Bytes to understand what object REST API is and why it matters
Explore Databricks and OneLake integrations for analytics and governance scenarios
Watch Discovery AI for advanced, performance intensive AI and HPC use cases

Together, these videos illustrate how the Azure NetApp Files object REST API scales from foundational data access patterns to sophisticated analytics and AI workloads without introducing additional data movement or storage complexity.

Beyond the scenarios covered here, the Azure NetApp Files object REST API can be used by any service or application that supports S3‑compatible access. This includes partner solutions, open‑source tools, and emerging AI services that benefit from direct access to enterprise file data. The scenarios shown in this post Databricks, OneLake, and Discovery AI represent common starting points, but the same architectural principles apply broadly across analytics, AI, and partner ecosystems where minimizing data movement is a priority.

Summary

The Azure NetApp Files object REST API extends Azure NetApp Files with S3-compatible object access, allowing analytics and AI platforms to work directly with enterprise file data without copying, moving, or restructuring that data. In this post, we explored how this capability enables two common integration patterns: virtualized data access through Microsoft OneLake for governed analytics and downstream AI services, and Lakehouse style analytics with Azure Databricks operating directly on file-based datasets.

For architects and solution teams, object REST API provides a way to simplify data architectures by reducing duplicate storage layers and minimizing the operational overhead of data pipelines. Analytics and AI workloads can access the same governed datasets using the interfaces they expect, while Azure NetApp Files continues to provide the enterprise performance, security, and availability required for production environments.

Object REST API is well suited for teams evaluating modern analytics and AI architectures that prioritize data locality and zero copy access. By understanding the architectural patterns described here and exploring the accompanying integration guides and videos, organizations can begin assessing how this approach fits within their broader data and AI strategies while remaining aligned with enterprise governance and security requirements.