Why Azure SQL Data Sync Retirement Matters

Azure SQL Data Sync relies on:

• Triggers
• Metadata tables
• Hub-and-spoke topology

While functional, this architecture introduces complexity, performance overhead, and operational risks, especially as data volumes and workloads grow.

Microsoft’s long-term direction favors scalable, resilient, and observable data integration services, such as Azure Data Factory (ADF) and event-driven replication patterns.

If you are currently using Data Sync, planning a migration early is strongly recommended.

Official guidance:

https://learn.microsoft.com/azure/azure-sql/database/sql-data-sync-data-sql-server-sql-database 

Sample Customer Scenario

Let’s consider a real scenario commonly seen in the field:

• 4 Azure SQL Databases
• Subscription: Contoso-DEV
• Current topology: Azure SQL Data Sync
• Target state: Consolidate all data into one Azure SQL Database
• Environment flow: DEV → UAT → PROD
• Database tiers: Standard (S0 / S1)
• Size: Below 250 GB per database
• Key requirements:
  • Minimal data loss
  • Quick replication
  • Azure-native and supported replacement
  • Clear operational model

Migration Design Considerations

Before selecting a tool, several factors must be evaluated:

• ✅ Latency tolerance (near real-time vs scheduled sync)
• ✅ Write patterns (conflicts, bidirectional vs unidirectional)
• ✅ Schema compatibility
• ✅ Operational overhead
• ✅ Long-term supportability

For most consolidation scenarios, unidirectional replication (many → one) provides the best balance of simplicity and reliability.

Diagram 1: Current State – Azure SQL Data Sync (Before Retirement)

This diagram represents the existing topology, where multiple databases are synchronized using Azure SQL Data Sync into a single consolidated database.

Characteristics

• Trigger‑based synchronization
• Additional metadata tables
• Limited observability
• Service approaching retirement

Diagram 2: Target State – Azure Data Factory Based Consolidation

This diagram shows the recommended replacement architecture using Azure Data Factory.

Advantages

• No triggers or sync metadata tables
• Parallel ingestion
• Built‑in retry, monitoring, and alerting
• Fully supported and future‑proof

Diagram 3: Incremental Replication Logic (ADF)

This diagram explains how minimal data loss is achieved using incremental replication.

Key Points

• No continuous connection required
• Typical RPO: 1–5 minutes
• Safe restart after failures

Diagram 4: DEV → PROD Migration Flow

This diagram highlights the recommended rollout approach starting with POC in DEV.

Best Practices

• Build once, reuse across environments
• Parameterize connection strings
• Enable monitoring before PROD cutover

Recommended Alternatives to Azure SQL Data Sync

✅ Option 1: Azure Data Factory (ADF) – Primary Recommendation

Azure Data Factory provides a fully supported and scalable replacement for Data Sync when consolidating databases.

Architecture Overview

• One pipeline per source database
• Initial full load
• Incremental replication using:
  • Change Tracking, or
  • CDC (if applicable), or
  • Watermark columns (ModifiedDate / identity)

Why ADF?

• Microsoft’s strategic data integration platform
• Built-in monitoring and retry logic
• Parallel ingestion
• Schema mapping and transformation support

📌 Best fit when:

• You need consolidation
• Near real‑time (minutes) is acceptable
• You want a future‑proof design

📘 References:

• https://learn.microsoft.com/azure/data-factory/copy-activity-overview 
• https://learn.microsoft.com/azure/data-factory/incremental-copy-overview 
• https://learn.microsoft.com/azure/data-factory/connector-azure-sql-database 

⚠️ Option 2: SQL Transactional Replication (Limited Use)

Transactional replication can still work in narrow scenarios, but:

• Adds operational complexity
• Limited flexibility for schema changes
• Not recommended for new designs

📘 Reference:

• https://learn.microsoft.com/azure/azure-sql/database/replication-to-sql-database 

🧭 Option 3: Azure SQL Managed Instance Link (Future‑Facing)

If your long-term roadmap includes Azure SQL Managed Instance, the MI Link feature enables near real-time replication.

However:

• Not applicable if your target remains Azure SQL Database
• Requires infrastructure change

📘 Reference:

• https://learn.microsoft.com/azure/azure-sql/managed-instance/link-feature 

Recommended Migration Approach (DEV → PROD)

Phase 1 – Assessment

• Review schema overlaps and key conflicts
• Identify identity and primary key strategies
• Confirm availability of:
  • Change Tracking
  • ModifiedDate / watermark columns

📘 Change Tracking:

• https://learn.microsoft.com/sql/relational-databases/track-changes/about-change-tracking-sql-server 

Phase 2 – Initial Seeding (DEV)

• Use ADF Copy Activity for full loads
• Ingest each source DB into:
  • Dedicated schemas, or
  • Logical partitions
• Validate:
  • Row counts
  • Referential integrity
  • Performance impact

Phase 3 – Incremental Replication

• Enable incremental pipelines
• Recommended frequency: every 1–5 minutes
• Use parallelism for scalability
• Simulate Data Sync behavior without triggers

Phase 4 – Cutover

• Optional short write freeze
• Final delta sync
• Application validation
• Promote pipelines to PROD

Data Loss and Performance Expectations

Final Recommendation Summary

✅ Azure Data Factory with initial full load + incremental replication
✅ Azure-native, strategic, and supported
✅ Ideal for Data Sync retirement scenarios
✅ Scales from DEV to PROD with minimal redesign

 

Azure SQL Data Sync retirement is an opportunity—not a setback.

With services like Azure Data Factory, customers can move toward:

• Better observability
• Cleaner architectures
• Easier production operations
• Long-term platform alignment

If you are still relying on Azure SQL Data Sync, now is the right time to assess, plan, and migrate.

Helpful Resources

• Azure SQL Data Sync overview
  https://learn.microsoft.com/azure/azure-sql/database/sql-data-sync-data-sql-server-sql-database 
• Azure Data Factory incremental copy
  https://learn.microsoft.com/azure/data-factory/incremental-copy-overview 
• Azure SQL change tracking
  https://learn.microsoft.com/sql/relational-databases/track-changes/about-change-tracking-sql-server 

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When we closed the door on 2024, there was both pearl-clutching and hype over AI — but at that time, not a lot of functionality for the frontend. That rapidly shifted in 2025, as AI use in web development evolved from code production into the creation of systems-aware components and plugins. This year also saw developers move past the generic chatbot era to an approach that bakes AI directly into the frontend architecture, via a new category of generative tools that better understand UI/UX.

The Death of the ‘Figma Gap’ and the Rise of Code-First Prototyping

At the start of the year, TNS highlighted WebCrumbs’ Frontend AI project, a generative AI model that created a plugin or template for developers, exporting the code as CSS/Tailwind, HTML, React, Angular, Svelte or Next.js. This differentiated it from Vercel’s v0, which at first tightly coupled Next.js and Tailwind. (v0 now offers more flexible exporting of React, Svelte, Vue, and Remix, as well as standard CSS code.) WebCrumbs Frontend AI also incorporated a code editor and Visual Studio.

WebCrumbs has since shut down, including Frontend AI, according to its site.  But it was a hallmark of what was to come by the end of the year, as development moved toward creation of components and more integration with frontend development work.

We saw the “Figma gap” closing, as new tools generated code and design simultaneously.

We also saw the “Figma gap” closing, as new tools generated code and design simultaneously, ensuring that what the developer sees in the visual editor is what gets rendered in the browser.

But some, including CEO Eric Simons of Bolt, an AI-based online web app builder, foresaw an even bigger shift coming that might make it easier to create and design at the same time. Simons argued it could make the Figma gap irrelevant.

“We’ve entered a new era where it’s now faster to make working prototypes with code, than design them in Figma,” Simons said in a tweet at the time.

It’s worth noting that Bolt still includes a way to upload Figma files, suggesting we’re not quite at a place where code has converged with design.

Still, instead of asking AI to rewrite a whole file for a small change, new interfaces allowed for visual tweaking — spacing, colors, fonts — that syncs instantly with the code.

The LLM Reasoning Revolution

One thing that made that possible was a shift in Large Language Models (LLM). LLMs moved away from being general-purpose models as AI companies created models optimized for development, as well as for specialized AI developer tools.

In mid-2024, Claude Artifacts was released, providing a preview of what was to come in 2025. It provided an AI-powered UI feature with a side-panel that allows developers to view or interact with React or HTML code in real-time, as the model writes it.

Then in April 2025, we saw the release of OpenAI’s GPT-4.1, which was specifically tuned for coding and instruction following, with a 1 million token context window. OpenAI also released reasoning versions with the o3 and o4 models, which introduced the ability to use images. Developers could suddenly convert whiteboard sketches or UI screenshots directly into logical reasoning chains.

In early 2025, Anthropic released Claude 3.7 Sonnet. Its standout feature was a dual thinking mode that allowed developers to toggle between a standard fast response and an “Extended Thinking” mode. This was key for the frontend because it allowed for complex UI logic or state management issues.

Google also launched Google Stitch as experimental in May. Powered by Gemini 3, it integrates directly with Figma and can read a design file and generate high-fidelity frontend code that follows specific design system rules, such as Material Design.

AX Over DX: Standardizing Agent-to-UI Communication With MCP

In January, Netlify CEO Matt Biilmann spoke with TNS about AX (agentic experience), arguing that we must now design websites for AI agents as much as for human users. It was a warning we heard repeatedly over the course of the year in terms not just of websites, but even APIs. By June, we heard it repeated by companies like PayPal, which had already began working on transitioning its PayPal APIs to be more agentic-friendly.

One way companies do this is by using the Model Context Protocol. MCP servers quickly came on the scene, emerging as the industry standard for how AI agents talk to application data. Extensions like MCP-UI allow these agents to not just fetch data, but to “pull” rich, branded UI components from a server and display them inside a chat interface (e.g., a flight picker appearing directly inside a Claude or ChatGPT window).

MCP servers quickly came on the scene, emerging as the industry standard for how AI agents talk to application data.

MCP servers soon became table stakes for both companies and JavaScript frameworks that wanted to share documentation best practices with developers. Angular and React both launched MCP Servers this year, and we’ve heard rumors of other frameworks following suit.

The year also saw the beginning of “self-healing UIs” that use agents embedded in the dashboard — such as Netlify’s Agent Runners — that can scan for broken links, identify accessibility violations, or fix responsive design bugs on mobile devices and submit the Pull Request automatically.

Generative UI: Moving Toward the Self-Assembling Interface

All of this soon led to what was arguably the most radical frontend shift in 2025: The evolution of Generative UI, where the interface is assembled by AI in response to a user’s prompt. While LLMs had been able to create interfaces since the beginning, these solutions became more complex and developer-friendly, allowing for more complex creations.

One such tool is the Hashbrown Framework, which we featured in December. This open source framework enables AI agents to run entirely in the browser. An app using Hashbrown can deploy an LLM to decide which UI components to render on the fly — filling out forms, creating custom charts, or suggesting shortcuts based on live user behavior.

It also supports, via the Skillet library, streaming, which resolves issues with LLM speeds regarding prompting. This allows the UI to start rendering and animating components the millisecond the AI begins “thinking,” making the experience feel instantaneous. By leveraging experimental browser APIs in Chrome and Edge, these tools also will also be able to run lightweight models on-device. This allows for a “private AI” that doesn’t need to send sensitive user data to a cloud server to provide a smart experience.

2025 has given us a preview of what’s possible with AI on the frontend. We look forward to finding out what sticks and what doesn’t in 2026.

The post 2025’s Radical Frontend AI Shift appeared first on The New Stack.

December 2025 - Astro v6 alpha release, and more!

This article dives into how psychological safety and self awareness help creative people take risks, speak honestly, and learn faster, and why that matters more than ever in modern teams.



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