Enterprise

Winsage
July 16, 2026
Microsoft announced that Windows 10 Enterprise LTSB 2016 and Windows 11 24H2 Home and Pro editions will stop receiving updates on October 13, 2026. The Enterprise and Education editions will continue to receive mainstream support until October 12, 2027. Users are encouraged to upgrade to Windows 11 25H2, which became available in September 2024. For unmanaged Windows 11 24H2 Home and Pro users, the transition to 25H2 will occur automatically, although they can postpone the update. Microsoft has also extended the free Windows 10 Extended Security Updates program for consumers until October 12, 2027.
Winsage
July 16, 2026
Microsoft has extended support for Windows 10 Extended Security Updates (ESU) until October 2027. Windows 11 version 24H2 will reach end of support on October 13, 2026, along with Windows 10 Enterprise LTSB 2016. Windows 11 versions typically offer 24 months of support for Home and Pro editions, while Windows 10 Enterprise LTSB 2016 had five years of mainstream support followed by five years of extended support. Windows 11 version 23H2 is also expected to sunset around the same time. Users on Windows 11 version 24H2 Home and Pro are recommended to upgrade to version 25H2, with version 26H2 anticipated soon. Windows 11 Enterprise and Education SKUs of version 24H2 have an extra year of support until October 12, 2027. Organizations using Windows 10 Enterprise LTSB 2016 should consider transitioning to Windows 10 Enterprise LTSC 2024 or purchasing ESUs separately.
Winsage
July 13, 2026
Microsoft has released a guide on the Windows servicing model, detailing monthly security updates, optional preview releases, hotpatch updates, and feature rollout mechanisms. Patch Tuesday occurs every second Tuesday of the month, delivering cumulative security updates to supported Windows versions. For consumers and small businesses, updates are managed through Windows Update, while enterprises can use various tools like Windows Autopatch and WSUS. Hotpatch updates, which focus on security fixes, can be installed without a restart, unlike quarterly baseline updates that require one. Optional non-security preview updates are released in the fourth week of each month for testing upcoming fixes and new features, available only for the latest supported Windows versions. Unmanaged devices can access these updates through Windows Update settings, while IT-managed devices depend on organizational policies. Microsoft also issues out-of-band updates to address urgent issues, which can be deployed through enterprise management tools. New features for Windows 11 are rolled out throughout the year via various channels, with a gradual rollout strategy to monitor quality and compatibility, using the Controlled Feature Rollout approach.
Winsage
July 12, 2026
Microsoft will discontinue support for several products in 2026, including: 1. Windows 11 24H2 (Home and Pro) - End of servicing on October 13, 2026. 2. Office 2021 and Office LTSC 2021 - End of support on October 13, 2026. 3. Windows Server 2012 and 2012 R2 - Final ESU year ending on October 13, 2026. 4. SQL Server 2016 - Extended end of support on July 14, 2026. 5. SharePoint Server 2016 and 2019, Project Server 2016 and 2019 - Support ends on July 14, 2026. 6. Microsoft Publisher - Retired after October 2026. 7. Exchange Server 2016 and 2019, Skype for Business Server - Final ESU cutoff on October 31, 2026. 8. Windows 11 Enterprise and Education 23H2, .NET 8, .NET 9, PowerShell 7.4 - End of servicing on November 10, 2026. 9. Windows 10 2016 LTSB and IoT Enterprise LTSB 2016 - Extended support ends on October 13, 2026. 10. Windows 11 SE - Support ends on October 1, 2026. 11. Dynamics CRM 2016 and older versions - Lost support earlier in January 2026. 12. Microsoft Configuration Manager, version 2409 - End of support on June 6, 2026. 13. Visual Studio 2022 LTSC channels, versions 17.10 and 17.12 - End of support in January and July 2026, respectively. 14. InfoPath 2013, SharePoint Designer 2013 - End of support on July 14, 2026. 15. Azure service retirements - Occurring in late 2026. Organizations are advised to review their software landscape and plan for transitions to avoid disruptions.
Tech Optimizer
July 12, 2026
Serverless PostgreSQL is a fully managed cloud database model that separates compute and storage, allowing them to scale independently and automatically based on demand. It eliminates the need for manual infrastructure provisioning and capacity planning, charging only for active usage. Unlike traditional PostgreSQL setups, which require continuous resource allocation and manual scaling, serverless PostgreSQL provisions resources on demand and can scale down to zero during idle periods. Serverless PostgreSQL integrates with serverless compute platforms, enabling analytical queries to access the same data within a unified architecture. Key differences between traditional and serverless PostgreSQL include manual versus automatic provisioning and scaling, fixed versus usage-based billing, and high versus reduced operational overhead. Lakebase architecture is an emerging model that combines transactional databases with lakehouse foundations, allowing operational and analytical workloads to coexist on a single platform. This architecture minimizes data duplication and simplifies access, enhancing data management and analysis. Serverless PostgreSQL operates on a cloud-native architecture that enhances efficiency by allowing compute and storage to scale autonomously. It features scale-to-zero behavior, where compute resources are suspended when inactive and reactivated upon new queries. Major providers include Databricks Lakebase, Amazon Aurora Serverless v2, and Neon, each offering varying capabilities and integrations. Pricing for serverless PostgreSQL typically includes charges for compute resources, storage, and data transfer, with costs fluctuating based on workload activity. Cold start latency is a performance consideration, as reactivating compute resources can introduce delays. Strategies to mitigate this include keeping resources partially active or selecting providers with minimal cold start impacts. Serverless PostgreSQL is well-suited for OLTP workloads, while lakebase architecture is better for AI development, variable workloads, and environments requiring rapid iteration. Setting up serverless PostgreSQL involves choosing a provider, creating a database instance, and configuring access settings. It can also be used alongside serverless compute platforms for analytics, further extending its capabilities.
Tech Optimizer
July 12, 2026
Running pgvector on Amazon Aurora PostgreSQL-Compatible Edition offers a vector store with operational capabilities, high availability, and scalability. It is favored for Retrieval Augmented Generation (RAG) workloads transitioning to production, but increased traffic introduces challenges like query latency and memory management. Key operational practices for pgvector workloads include selecting the appropriate index type (HNSW or IVFFlat), establishing a baseline schema, choosing a suitable distance operator, scaling the index through quantization and partitioning, and preparing for churn and observability. The prerequisites for using pgvector include an Aurora PostgreSQL-Compatible cluster with specific PostgreSQL versions and the vector extension enabled. The embedding model used in examples is Amazon Titan Text Embeddings V2, which produces 1024-dimensional embeddings. pgvector supports two Approximate Nearest Neighbor (ANN) index types: HNSW, which is efficient for querying and allows for incremental insertions, and IVFFlat, which is less resource-intensive but requires rebuilding if data changes. There are scenarios where forgoing an index is beneficial, such as small datasets or partitioned datasets requiring 100% recall. A baseline schema for a multi-tenant document store includes creating a table for documents with an embedding vector and establishing indexes for tenant IDs and embeddings using HNSW. The recommended parameters for HNSW include m = 16 and ef_construction = 128. Scaling to millions of vectors involves quantization, tuning HNSW parameters, and partitioning. Aurora Optimized Reads can extend effective cache capacity, and managing index churn is crucial for maintaining performance. Observability metrics include query-level statistics, instance-level metrics, and custom application-defined metrics. To clean up after testing, it is advisable to drop the created indexes and tables, and delete the Aurora PostgreSQL-Compatible cluster and any manual snapshots taken during testing.
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