Multi-platform Integration Techniques

A single update—and suddenly, all my AI integrations broke. Two weeks of patching, testing, and frustration later, I thought: there has to be a better way. That’s when I discovered 𝗠𝗖𝗣. Imagine connecting any AI model to any tool with just one simple setup. Let me save you time for long research: MCP is an open protocol using JSON-RPC that standardizes how AI models interact with external tools and data sources. When Anthropic said, "Let's unify how AI connects to tools via a shared protocol," the industry listened. And for good reason. 𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 (𝗕𝗲𝗳𝗼𝗿𝗲 𝗠𝗖𝗣): • LLMs used conflicting integration methods • Custom adapters needed for every app-tool pair (M apps × N tools = M×N integrations) • Engineering teams wasted time by reinventing the same connections • Maintenance nightmare as platforms evolved 𝗧𝗵𝗲 𝗠𝗖𝗣 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: ✅ Build one MCP server for your tool ✅ Works with any MCP-compatible AI app (Claude, GPT, etc.) ✅ Only M + N integrations needed total ✅ AI models dynamically discover available tools via the protocol Here's the beautiful part: If you understand JSON and basic APIs, you're already 80% ready for MCP. What used to take development teams weeks now takes hours. Connect databases, APIs, or internal tools through real-time bidirectional communication using a single standardized protocol. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻: → Build once, integrate everywhere (zero platform-specific code) → Tools work instantly with all current AND future MCP-compatible AI systems → Teams focus on core functionality instead of maintaining fragile adapters → Reduced technical debt and faster time-to-market Instead of reading 50+ different integration docs, you just learn one simple standard. Instead of managing fragmented connections, you maintain one standards-compliant server. MCP isn't just solving today's integration headaches – it's future-proofing AI tool connectivity. Have you explored MCP's dynamic tool discovery capabilities yet? What's your biggest AI integration challenge? #MCP #AIIntegration #DeveloperTools #AgenticAI #AI

💭 Ever faced the challenge of keeping your data consistent across regions, clouds, and systems — in real time? A few years ago, I worked on a global rollout where CRM operations spanned three continents, each with its own latency, compliance, and data residency needs. The biggest question: 👉 How do we keep Dataverse and Azure SQL perfectly in sync, without breaking scalability or data integrity? That challenge led us to design a real-time bi-directional synchronization framework between Microsoft Dataverse and Azure SQL — powered by Azure’s event-driven backbone. 🔹 Key ideas that made it work: Event-driven architecture using Event Grid + Service Bus for reliable data delivery. Azure Functions for lightweight transformation and conflict handling. Dataverse Change Tracking to detect incremental updates. Geo-replication in Azure SQL to ensure low latency and disaster recovery. What made this special wasn’t just the technology — it was the mindset: ✨ Think globally, sync intelligently, and architect for resilience, not just performance. This pattern now helps enterprises achieve near real-time visibility across regions — no more stale data, no more integration chaos. 🔧 If you’re designing large-scale systems on the Power Platform + Azure, remember: Integration is not about moving data. It’s about orchestrating trust between systems. #MicrosoftDynamics365 #Dataverse #AzureIntegration #CloudArchitecture #PowerPlatform #AzureSQL #EventDrivenArchitecture #DigitalTransformation #CommonManTips

I’ve just published a new article exploring strategies to unify data sharing across Snowflake, Databricks, and Microsoft Fabric. While consolidating onto a single platform is often ideal, the reality for many large enterprises is more complex. Team autonomy, legacy investments, and strategic diversification often lead to multi-cloud and multi-product environments. Can your cross-platform integration architecture become a strategic advantage? The article focuses on options to share delta parquet and iceberg format storage amongst the three platforms: https://lnkd.in/gs4nS8Tt In the real world, very few large organizations are unified on a single data and analytics platform. Snowflake, Databricks, and Microsoft Fabric are all very popular products with widespread adoption. All three offer lakehouse architecture tools, but what are your options if you have data in more than one of these products? How do you share data amongst the platforms in a way that minimizes replication, is cost efficient, and has low latency? This post is the first in a three-part series focusing on interoperability amongst Snowflake, Databricks, and Microsoft Fabric. #Snowflake #Databricks #AzureDatabricks #MicrosoftFabric

In 20+ years of building software, this is one of the things I’m most proud of to have built together my product team. It’s the future-proof architecture for scaling any number of customer-facing integrations. If you’re working on developing integrations with 3rd party APIs, you need a well-designed framework that scales in the long-term. It’s the most important piece of your puzzle to build an ecosystem of apps around your product efficiently. If you’re exploring unified APis or Ipaas’s, over years you will still miss one of the layers I will describe below. Depending on your use case, being able to customise at each part of the stack is crucial to using an integration partner, otherwise you will need to write custom code and go around the 3rd party provider. Integration Layers When developing integrations for customers, consider these three layers of abstraction: ⚙️ Universal Blueprints: These cater to common use cases applicable across various external apps at once. An example is a blueprint for "Creating a Task in any Project Management Tool," which can be adapted to different platforms. ⚙️ Application-Level Blueprints: These are tailored for specific external apps. For instance, a blueprint for "Creating a Task in JIRA" falls into this category. These blueprints either stem from universal blueprints or address unique needs of particular apps. ⚙️ Customer Deployments: These define integration logic for individual customers and their specific connections. Usually derived from application-level blueprints, they can be customized further based on individual requirements.

The Pros of Deploying Prevalent Integration Middleware Platforms: As enterprises strive to connect disparate systems and streamline data flows, integration middleware platforms have become a common choice. These platforms offer a structured approach to data movement, automation, and interoperability. Simplified Connectivity – Middleware platforms provide pre-built connectors and APIs, reducing the need for custom coding and enabling faster integration across diverse applications. Scalability & Performance Optimization – Modern integration platforms support cloud-native architectures, ensuring efficient data processing and real-time synchronization as businesses grow. Enhanced Security & Compliance – With built-in governance frameworks, encryption, and access controls, middleware helps enterprises meet regulatory requirements while protecting sensitive data. Next Steps for CDO/CIO: Deploy middleware solutions with pre-built connectors for key enterprise applications. Automate data flows to reduce manual intervention and ensure real-time synchronization. Implement security and compliance protocols using middleware’s built-in governance features.