Generated on: January 29, 2026 Target period: Within the last 24 hours Processing mode: Details Mode Number of updates: 4 items
Published: January 28, 2026 23:15:47 UTC Link: Retirement: Support for Python 3.10 ends on October 1, 2026 – upgrade your Azure Functions apps to Python 3.13
Update ID: 545771 Data source: Azure Updates API
Categories: Compute, Containers, Internet of Things, Azure Functions
Summary:
What was updated
Azure Functions will retire support for Python 3.10 starting October 1, 2026.
Key changes or new features
After this date, Python 3.10 apps on Azure Functions will continue to run but will no longer receive security patches or performance improvements. Developers are advised to upgrade their Azure Functions apps to Python 3.13 to maintain support, security, and optimal performance.
Target audience affected
Developers and IT professionals who build, deploy, or maintain Azure Functions apps using Python 3.10.
Important notes if any
This change aligns with the official end of community support for Python 3.10. Upgrading to Python 3.13 ensures continued security updates and access to the latest runtime enhancements. Plan your migration ahead of the October 2026 deadline to avoid potential vulnerabilities and performance degradation.
For more details, visit: https://azure.microsoft.com/updates?id=545771
Details:
The Azure update announces the retirement of support for Python 3.10 in Azure Functions effective October 1, 2026, urging developers to upgrade their Azure Functions apps to Python 3.13 to maintain security, performance, and support compliance.
Background and Purpose:
This update aligns Azure Functions’ Python runtime support lifecycle with the broader Python community’s end-of-life schedule for Python 3.10. As Python 3.10 reaches end-of-life status, it no longer receives official security patches or performance improvements from the Python Software Foundation. Consequently, Azure is retiring support to ensure that Azure Functions customers run their serverless workloads on a secure, performant, and actively maintained Python runtime. The purpose is to encourage proactive migration to Python 3.13, which benefits from ongoing updates and improvements.
Specific Features and Detailed Changes:
Technical Mechanisms and Implementation Methods:
FUNCTIONS_WORKER_RUNTIME and pythonVersion settings in the function app configuration or deployment files.Use Cases and Application Scenarios:
Important Considerations and Limitations:
Integration with Related Azure Services:
Published: January 28, 2026 19:15:24 UTC Link: Generally Available: Azure AMD Turin Dasv7, Easv7, and Fasv7-series Virtual Machines
Update ID: 552318 Data source: Azure Updates API
Categories: Launched, Compute, Virtual Machines
Summary:
What was updated
Azure has announced the general availability of AMD-based Turin series virtual machines: Dasv7 and Dalsv7 (general purpose), Easv7 (memory-optimized), and Fasv7, Falsv7, Famsv7 (compute-optimized). These VM sizes now support configurations both with and without local disk storage.
Key changes or new features
These new VM series leverage AMD Milan processors, offering enhanced performance and cost efficiency for diverse workloads. The availability of local disk options provides flexibility in storage performance and persistence. The VMs are currently available in select Azure regions, including Australia East and Central.
Target audience affected
Developers and IT professionals seeking scalable, cost-effective VM options optimized for general purpose, memory-intensive, or compute-heavy applications will benefit. This is particularly relevant for workloads requiring AMD architecture advantages or local disk storage configurations.
Important notes if any
Availability is initially limited to specific regions; users should verify regional support before deployment. The inclusion of local disk support allows for improved I/O performance but may affect data persistence depending on VM lifecycle. Review workload requirements to choose appropriate VM series and storage options.
For more details, visit: https://azure.microsoft.com/updates?id=552318
Details:
The recent general availability (GA) of Azure AMD Turin-based Dasv7, Dalsv7, Easv7, Fasv7, Falsv7, and Famsv7 virtual machine (VM) series marks a significant expansion of Azure’s VM portfolio, offering enhanced performance and cost efficiency for diverse workloads. These VMs leverage AMD’s latest EPYC 9004-series processors (Turin), delivering improved compute power, memory bandwidth, and energy efficiency compared to previous generations.
Background and Purpose
Azure continuously evolves its VM offerings to meet growing demands for scalable, cost-effective, and high-performance cloud compute resources. The AMD Turin-based VM series aims to provide a competitive alternative to Intel-based VMs, optimizing price-performance ratios for general-purpose, memory-optimized, and compute-optimized workloads. The introduction of these VMs with and without local disk support addresses varied storage latency and throughput requirements.
Specific Features and Detailed Changes
Technical Mechanisms and Implementation
These VMs are built on Azure’s hyper-scale infrastructure, integrating AMD EPYC Turin CPUs with Azure’s custom firmware and hypervisor optimizations. The local disk option uses NVMe-based ephemeral storage directly attached to the VM host, enabling high IOPS and low latency for temporary data. The VMs support Azure features such as Accelerated Networking, enabling enhanced network throughput and reduced jitter, and Azure Premium SSDs for persistent storage. They also integrate with Azure Monitor and Azure Security Center for observability and security compliance.
Use Cases and Application Scenarios
Important Considerations and Limitations
Integration with Related Azure Services
Published: January 28, 2026 18:45:08 UTC Link: Generally Available: Azure Databricks Agent Bricks Knowledge Assistant
Update ID: 542455 Data source: Azure Updates API
Categories: Launched, AI + machine learning, Analytics, Azure Databricks
Summary:
What was updated
Azure Databricks introduced the Agent Bricks Knowledge Assistant, now generally available, enabling AI agent creation, deployment, and management directly within the Azure Databricks environment.
Key changes or new features
The update provides a unified platform combining data and AI capabilities to streamline the development of AI agents. It includes prebuilt components and templates that simplify building intelligent agents without extensive custom coding. This integration enhances productivity by allowing developers to leverage Databricks’ scalable data processing alongside AI models seamlessly.
Target audience affected
Developers, data scientists, and IT professionals working with Azure Databricks who want to build, deploy, and maintain AI-driven agents within their data workflows will benefit most from this feature.
Important notes if any
This feature leverages the existing Azure Databricks infrastructure, so users should ensure their workspace is updated to the latest version to access Agent Bricks. It is designed to accelerate AI agent development while maintaining security and governance within enterprise environments. For detailed implementation guidance, refer to the official Azure Databricks documentation.
Details:
The Azure Databricks Agent Bricks Knowledge Assistant, now generally available, introduces a powerful capability to build, deploy, and manage AI agents natively within the Azure Databricks environment by leveraging unified data and AI services. This update addresses the growing demand for integrated AI-driven automation and intelligent agent workflows directly on big data platforms, streamlining the development lifecycle and operational management of AI agents.
Background and Purpose
As enterprises increasingly adopt AI to automate complex workflows and decision-making processes, there is a need to embed intelligent agents closer to data processing environments. Azure Databricks, a leading unified analytics platform, traditionally focuses on big data engineering, data science, and machine learning. However, integrating AI agents that can interact with data, perform tasks, and provide insights directly within Databricks clusters was a gap. The Agent Bricks Knowledge Assistant fills this gap by enabling AI agent creation and management without leaving the Databricks workspace, thus reducing context switching and accelerating AI-driven automation.
Specific Features and Detailed Changes
Technical Mechanisms and Implementation Methods
The Agent Bricks Knowledge Assistant is architected as a set of microservices and SDKs embedded within the Databricks runtime environment. It leverages Databricks’ existing compute clusters and integrates with Azure Cognitive Services for NLU and language understanding capabilities. Agents operate as managed workloads that can invoke Spark jobs, MLflow models, or REST APIs, enabling complex workflows. The assistant uses Delta Lake for state management and knowledge base storage, ensuring consistency and scalability. Developers interact with the assistant through Databricks notebooks or RESTful APIs, enabling programmatic control and automation.
Use Cases and Application Scenarios
Important Considerations and Limitations
Published: January 28, 2026 17:00:53 UTC Link: Generally Available: Latest PostgreSQL minor versions supported by Azure Database for PostgreSQL – Flexible Server
Update ID: 550164 Data source: Azure Updates API
Categories: Launched, Databases, Hybrid + multicloud, Azure Database for PostgreSQL
Summary:
What was updated
Azure Database for PostgreSQL – Flexible Server now supports the latest minor versions of PostgreSQL: 18.1, 17.7, 16.11, 15.15, 14.20, and 13.23.
Key changes or new features
These updates include the most recent minor version releases for multiple major PostgreSQL versions, ensuring improved stability, security patches, and bug fixes. The minor version upgrades are automatically applied during Azure’s monthly planned maintenance windows, minimizing manual intervention.
Target audience affected
Developers and IT professionals managing PostgreSQL databases on Azure Flexible Server will benefit from enhanced performance, security, and compliance with the latest PostgreSQL minor releases. This is particularly relevant for teams prioritizing up-to-date database environments without downtime.
Important notes if any
Automatic minor version upgrades occur during planned maintenance, so users should plan accordingly. No action is required to enable these updates, but reviewing release notes for each PostgreSQL minor version is recommended to understand specific fixes or changes. This update helps maintain secure and reliable PostgreSQL deployments on Azure Flexible Server.
Details:
The recent Azure update announces the general availability of support for the latest PostgreSQL minor versions—18.1, 17.7, 16.11, 15.15, 14.20, and 13.23—on Azure Database for PostgreSQL – Flexible Server. This enhancement ensures that customers benefit from the most recent stability, security, and performance improvements delivered by the PostgreSQL community, with upgrades applied automatically during Azure’s monthly planned maintenance windows.
Background and Purpose:
Azure Database for PostgreSQL – Flexible Server is a managed database service designed to offer high availability, scalability, and operational flexibility for PostgreSQL workloads. PostgreSQL minor version updates typically include critical bug fixes, security patches, and performance optimizations without introducing breaking changes. By supporting the latest minor versions, Azure ensures that customers run secure, stable, and optimized database instances with minimal administrative overhead. Automating these upgrades as part of monthly maintenance reduces manual intervention and helps maintain compliance with security best practices.
Specific Features and Detailed Changes:
The update covers support for PostgreSQL minor versions 13.23 through 18.1, spanning multiple major versions. Each minor version includes cumulative fixes and enhancements such as improved query planner stability, security vulnerability patches (e.g., CVE mitigations), and fixes for edge-case bugs affecting replication, indexing, or data integrity. While no major feature changes occur in minor updates, these incremental improvements collectively enhance database reliability and performance.
Technical Mechanisms and Implementation Methods:
Azure Database for PostgreSQL – Flexible Server employs a managed upgrade process integrated into its monthly planned maintenance cycle. During this window, the service automatically applies minor version upgrades to the underlying PostgreSQL engine with minimal downtime, leveraging rolling upgrade techniques and failover mechanisms where applicable. This process includes pre-upgrade validation, backup snapshots for recovery, and post-upgrade health checks to ensure service continuity. Customers can monitor upgrade status through Azure Portal or Azure CLI and configure maintenance windows to align with their operational requirements.
Use Cases and Application Scenarios:
This update is particularly relevant for enterprises and developers running production workloads on PostgreSQL that require high availability and security compliance. Applications benefiting include web and mobile backends, analytics platforms, and SaaS solutions relying on PostgreSQL’s extensibility and robustness. By staying current with minor versions, customers reduce exposure to known vulnerabilities and improve overall system stability, which is critical for regulated industries and mission-critical applications.
Important Considerations and Limitations:
While minor version upgrades are backward-compatible, customers should test application compatibility in staging environments before production rollout, especially if they use PostgreSQL extensions or custom configurations. The automatic upgrade process may cause brief service interruptions during maintenance windows, so planning for maintenance windows is essential. Additionally, major version upgrades remain a separate process requiring manual intervention and testing. Customers should also review Azure’s documentation for any region-specific availability or feature differences.
Integration with Related Azure Services:
Azure Database for PostgreSQL – Flexible Server integrates seamlessly with Azure services such as Azure Monitor for performance and health metrics, Azure Backup for data protection, and Azure Active Directory for authentication. The update ensures that these integrations continue to function optimally with the latest PostgreSQL engine versions. Furthermore, compatibility with Azure Data Factory and Azure Synapse Analytics enables streamlined data movement and analytics workflows on up-to-date PostgreSQL instances.
In summary, the general availability of the latest PostgreSQL minor versions on Azure Database for PostgreSQL – Flexible Server enhances security, stability, and performance by automating critical engine updates during planned maintenance, thereby enabling IT professionals to maintain robust and compliant PostgreSQL deployments with minimal operational overhead.
This report was automatically generated - 2026-01-29 03:02:29 UTC