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Mobile development blogs, tutorials and resources inside!Latest Mobile Dev Insights: iOS, Android, Cross-PlatformAdvertise with Us|Sign Up to the NewsletterMobilePro #186: The Model Context Protocol Is Changing How We Build Agentic AI AppsWhy developers are turning to MCP for scalable, secure, and intelligent solutions — and how to get started todayHi ,Welcome to the 186th edition of MobilePro!We have all seen the impressive demos. AI assistants that can code, summarize, plan, and collaborate almost like a teammate.But when it comes time to connect them to your real systems, your APIs, databases, or business tools, things quickly become complicated. Integrations break. Capabilities are hardcoded into specific workflows. Scaling to new features often means starting from scratch.A new approach is emerging that changes how AI-powered apps interact with the world around them.It is not another SDK or framework. It is a shift in how we define, discover, and connect capabilities so that tools and AI systems can work together more naturally.On August 30, we’re running a hands-on workshop with Christoffer Noring, Sr. Cloud Advocate at Microsoft, to show you exactly how to build and deploy with this new model. You’ll leave not just knowing what it is, but having implemented it yourself.What Exactly Is MCP?The Model Context Protocol (MCP) is like a USB Hub for AI.Right now, most AI assistants live in their own bubble. They can reason well, but they can’t easily reach into your tools, databases, or services without custom, one-off integrations. That means every new data source or capability often means starting from scratch.MCP fixes that by giving developers a single, open standard for connecting AI systems to the outside world. With MCP, you can securely expose capabilities (like “generate an invoice” or “pull customer history”) in a way that any compatible AI client, from an LLM-powered app to a VS Code agent, can discover and use instantly.Think of it as a USB-C port for AI: one connection that works for everything, whether you’re plugging into a CMS, an API, or a cloud service. The result? AI assistants that are faster to build, easier to maintain, and able to do more in the real world.Real-World Use CasesSo what does MCP actually look like in action? Here are a few places where it can make an immediate impact:E-commerce that responds in real time: An AI shopping assistant can pull live product data from your inventory, check shipping status, and even trigger a reorder all by calling MCP capabilities you’ve exposed.Developer tools that work across systems:A VS Code agent could refactor your code, query API documentation, check deployment logs, and open a support ticket without you building a separate connector for each action.AI helpdesk that actually solves problems: A support bot could securely access customer history, issue refunds, and schedule callbacks, all by discovering and using MCP tools without manual setup.In each of these scenarios, MCP makes the connection once and lets it be reused anywhere. Once you’ve set up a capability, any MCP-aware client can find it, understand it, and use it. That’s why more developers are starting to see MCP not as another integration layer, but as a foundation for AI-ready applications.Putting MCP into PracticeUnderstanding the principles behind the Model Context Protocol is one thing. Turning those ideas into a functioning client–server setup is another. This is where the real value of MCP comes alive.If you’re ready to go beyond reading about MCP and actually build something you can use, join our focused 2.5-hour workshop on August 30th with Christoffer Noring (Sr. Cloud Advocate, Microsoft).As a bonus, you can grab your spot at 35% off with the code LIMITED35. But act fast, this special deal disappears on Monday, 18th August.Save your spot at 35% OFFIn one session, you’ll:Understand the “why” behind MCP: Learn how separating tools from LLMs makes AI systems more scalable, maintainable, and adaptableMaster the core architecture:See exactly how MCP clients and servers discover and communicate with each otherBuild and deploy your first MCP server:Code along in Python, guided by Christoffer, and test it with a working MCP clientConnect MCP to real-world systems:Securely expose and consume capabilities from APIs, databases, or cloud servicesYour Workshop Package Includes:Free eBook: Model Context Protocol for Beginners (worth $35.99) — full of real-world examples and tips.Exclusive AMAs: Small-group Q&A with Christoffer to troubleshoot and go deeperCertificate of Completion to showcase your new skillsAfter this workshop, you’ll be able to:Decide if MCP is right for your AI projectsBuild both MCP servers and clients from scratchCreate LLM-powered clients that instantly discover and use MCP capabilities📅 Date: August 30th📍 Location: Live Online⏱ Duration: 2.5 hoursWhy This Session Appeals to Developers and EngineersThis is for anyone building AI-powered tools or agentic apps:Developers who want to integrate agents, tools, or LLMs without reinventing server designSoftware architects rethinking interoperability, modularity, and contextAI engineers building structured, scalable, and secure infrastructure for agentsProduct managers working on AI initiatives and looking for a scalable reference architectureA basic understanding of software development and AI concepts is recommended.Ready to Get Started?👉 Reserve your seat in the MCP Workshop (Second Cohort)👉 Buy the book: Model Context Protocol for BeginnersYou’ll walk away with a better understanding of how to structure your systems, interact with tools programmatically, and deploy AI-native applications with confidence.👋 And that’s a wrap. We hope you enjoyed this new format of MobilePro.P.S.: If you have any suggestions or feedback, help us improve by sharing your thoughts. Click on the survey below.Take the Survey!Cheers,Runcil Rebello,Editor-in-Chief, MobilePro*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

FREE GUIDE: Airflow 3 Tips & Code SnippetsFREE GUIDE: Airflow 3 Tips & Code SnippetsThinking about upgrading to Apache Airflow® 3? You’ll get powerful new features like a modernized UI, event-based scheduling, and streamlined backfills. Quick Notes: Airflow 3 Tips & Code Snippets is a concise, code-filled guide to help you start developing DAGs in Airflow 3 today.You’ll learn:How to run Airflow 3 locally (with dark mode) and navigate the new UIHow to manage DAG versioning and write DAGs with the new @asset-oriented approachThe key architectural changes from Airflow 2 to 3GET YOUR FREE GUIDESponsoredSubscribe|Submit a tip|Advertise with UsWelcome to DataPro #146: Expert Insight Edition.We’re excited to bring on board Sagar Lad, Lead Data Solution Architect at a leading Dutch bank, to the Expert Insight edition of the DataPro newsletter. Sagar will be sharing his hard-won lessons, practical tips, and implementation strategies for navigating the challenges of data in the Gen AI and Agentic AI era.Each week, Sagar will guide you through his in-depth analysis and research, showing what really works in complex production environments. His goal is simple: help you turn concepts into practice and ideas into impact.This week, he kicks things off with a deep dive into Data Products: Turning Data into Tangible Value. As always, our mission at DataPro is to bring you first-hand, practical insights from industry experts. We believe Sagar’s expertise will provide valuable guidance you can apply directly to your daily data practice.So, without further ado, let’s jump in.Cheers,Merlyn ShelleyGrowth Lead, PacktData Products: Turning Data into Tangible Value - By Sagar LadIn today’s digital economy, data has become one of the most valuable assets for organizations. Every transaction, interaction, and process generates data that — when properly harnessed — can unlock powerful insights, drive innovation, and create competitive advantages. However, simply collecting and storing vast amounts of data is not enough. To truly realize its value, organizations must transform data into usable, scalable, and outcome-driven solutions. This is where the concept of adata productcomes into play.A data product is not just raw data, but rather a packaged, consumable, and value-generating asset built on top of data. Just as traditional products solve customer needs, data products solve business challenges by delivering insights, predictions, or automated decisions in a way that is accessible and reliable for end users.What is a Data Product?At its core, adata productis a solution designed around data to serve a specific purpose or generate business value. It could take many forms — such as a dashboard, an API serving machine learning predictions, a recommendation engine, or even a dataset curated for a particular domain.For example:→ Netflix’s recommendation systemis a data product built to enhance user engagement.Characteristics of a data product include:1. Purpose-driven— It is built to achieve a clear outcome (e.g., increase sales, reduce costs, improve customer satisfaction).2. Reusable— A well-designed data product can serve multiple teams or applications.3. Consumable— It is packaged in a way that non-technical users or systems can leverage it seamlessly.4. Scalable— It is designed to evolve with changing business needs and data volumes.Data Product: Bridge between Producer & ConsumerData Products vs. Data AssetsIt is important to differentiate betweendata assetsanddata products.Adata assetcould be a data lake, warehouse, or dataset that stores raw or processed data. While valuable, assets by themselves may not generate outcomes unless someone analyzes them.Adata product, on the other hand, transforms these assets into actionable, consumable outputs that stakeholders can directly use to make decisions or power business processes.In other words, data assets are ingredients, while data products are the finished dishes that customers can consume.Why Do Organizations Need Data Products?Organizations often struggle with extracting value from their data investments. Billions of dollars are spent globally on data platforms, yet many businesses face the“last mile problem”— where insights fail to reach decision-makers in a meaningful way. Data products help bridge this gap by operationalizing data and embedding it into workflows.Key benefits of data products include:1. Faster Decision-MakingWith well-packaged insights, business users don’t need to spend hours querying databases or waiting for reports. A data product like a sales forecasting model can instantly provide actionable intelligence.2. Democratization of DataData products abstract technical complexity, enabling business users, analysts, and applications to easily consume data-driven insights.3. Standardization and ReusabilityInstead of rebuilding analytics pipelines repeatedly, a single data product can serve multiple business units. For example, a customer segmentation data product could be reused by marketing, sales, and product teams.4. Scalability and AutomationData products, once designed, can be scaled to handle growing data volumes and embedded into automated workflows.5. Value RealizationUltimately, data products help organizations move beyond storing data tomonetizing and operationalizing it— whether through cost savings, revenue generation, or improved customer experiences.Key Principles for Designing Data ProductsDesigning a successful data product requires more than technical skills — it requires product thinking. Some guiding principles include:1.Start with Business ValueA data product must solve a real business problem. Before building, clearly define the outcome it should drive.2. User-Centric DesignThe product should be intuitive for its target users, whether that’s executives, developers, or customers.3. Trust & TransparencyUsers must trust the data product. This requires data quality checks, explainability in AI models, and governance measures.4. Scalability & ReusabilityBuild products that can adapt to future needs, serve multiple stakeholders, and scale across datasets and domains.5. OperationalizationA data product should integrate seamlessly into business workflows and systems, rather than existing as a standalone artifact.6. Monitoring & ImprovementData products must be continuously monitored for performance, accuracy, and relevance, with feedback loops for improvements.Challenges in Building Data ProductsWhile data products are powerful, organizations face challenges in creating and scaling them:1. Data Quality Issues: Poor data leads to unreliable products.2. Cultural Resistance: Teams may hesitate to trust automated insights.3. Lack of Product Mindset: Many companies treat data as IT projects, not products.4. Scalability Hurdles: A data product may work for a pilot but struggle in enterprise-wide deployments.5. Governance & Compliance: Ensuring data products adhere to regulatory and ethical standards is critical.Overcoming these requires strongdata governance, clear ownership, cross-functional collaboration, and a product-centric approach.The Role of Data Mesh and Data ProductsThe concept ofdata productsis also central toData Mesharchitecture. In Data Mesh, each domain team is responsible for building and managing its own data products, treating them as first-class citizens. This shifts ownership from centralized IT teams to domain experts, making data products more relevant, accurate, and consumable.By combining Data Mesh principles with robust product management practices, organizations can scale their data strategy while ensuring alignment with business outcomes.Future of Data ProductsThe future of data products looks promising as technology evolves:1. AI-driven Data Products: With advancements in generative AI, data products will become more conversational, adaptive, and personalized.2. Marketplace of Data Products: Organizations may buy and sell data products just like SaaS solutions, creating new revenue streams.3. Self-Service Ecosystems: Business users will increasingly be able to design their own data products using no-code/low-code platforms.4. Embedded Trust & Ethics: As AI governance matures, responsible AI principles will be embedded directly into data products.ConclusionData products represent a fundamental shift in how organizations leverage data. They move beyond static reports or siloed datasets to create reusable, scalable, and outcome-driven solutions. By applying product thinking to data initiatives, companies can ensure that data investments directly translate into measurable business value.In a world where data is the new currency,data products are the vehicles that convert raw information into tangible value. The organizations that master this art will be the ones that thrive in the data-driven future.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}