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Unlocking Innovation and Efficiency for Growth-Stage Companies  By Tony Berry  Today’s data teams are facing pressure from all sides. The mandate is clear: deliver faster insights, better decisions, and smarter AI-driven solutions—without compromising accuracy, compliance, or performance.  The opportunity is massive. But so is the complexity.  To stay competitive, growth-stage companies need more than raw data—they need modern infrastructure, AI-embedded workflows, and seamless collaboration across tools and teams. That’s where Microsoft Fabric and Copilot come in.  At Interloop, we’ve seen how teams move faster and think bigger when data and AI work hand in hand. In this Launchpad edition, we explore the most common challenges facing data teams today—and how Microsoft Fabric  and Copilot  can help solve them at scale.  Common Data Challenges Slowing Teams Down  Even experienced data professionals run into familiar friction. Here are five hurdles we see again and again:  Data Quality & Consistency   Incomplete, outdated, or mismatched data remains a top barrier to decision-making. Poor data quality breaks trust—and breaks pipelines.  Scalability & Performance   Teams are managing more data than ever before. Processing, storing, and analyzing at scale without degrading performance is a constant balancing act.  Siloed & Disparate Systems   Legacy platforms, point solutions, and department-specific tools create fragmented data environments and manual workarounds.  Security & Compliance Pressures   With evolving privacy laws and increased data volume, maintaining compliance and safeguarding access is mission-critical—and increasingly complex. Talent Shortages & Skill Gaps   Hiring experienced data engineers and AI specialists is tough. Upskilling internally takes time most teams don’t have.  How Fabric + Copilot Help Teams Scale Smarter  Microsoft Fabric and Copilot offer a modern, AI-driven approach to solving these challenges. Together, they form a unified platform that enables faster, safer, and more collaborative data work—whether you’re wrangling spreadsheets, building dashboards, or deploying models.  Here’s how they help teams overcome real obstacles:  Improve Data Quality   Copilot automatically flags inconsistencies, fills missing fields, and standardizes formatting. Example: A retail chain used Copilot to unify sales data across 300+ stores, improving forecasting accuracy and reducing stockouts.   Scale Workloads On-Demand   Fabric intelligently allocates compute resources in real time, while Copilot prioritizes workloads based on business needs.  Example: A financial services firm cut processing time in half using Copilot to scale analytics during peak reporting cycles.   Unify Disconnected Systems   Copilot leverages natural language processing and smart connectors to harmonize legacy and modern platforms.  Example: A healthcare org unified patient records across five databases, enabling a full 360° view of care.   Ensure Real-Time Security & Compliance   Copilot monitors access, flags anomalies, and enforces policies dynamically.  Example: A pharmaceutical company used Copilot to streamline internal audits and strengthen regulatory compliance.   Empower More People, Faster   With low-code/no-code functionality, Copilot enables non-technical users to participate in data workflows.  Example: A manufacturer used Copilot to equip frontline teams with simple, self-serve reporting tools—reducing bottlenecks and boosting efficiency.   More Ways to Use Copilot Across Fabric  Because Copilot is embedded across Microsoft Fabric’s unified architecture, data teams can tap into AI support at every layer—from data ingestion to transformation to visualization.  Data Factory   Generate transformation code with natural language prompts.  Example: A retail company automated data ingestion from multiple e-commerce platforms, accelerating reporting cycles.   Data Science & Engineering   Copilot can interpret existing code, automate data prep, and enrich datasets for advanced analytics.  Example: Marketing teams used Copilot to segment audiences and design more targeted campaigns.   Power BI   Create reports and visuals simply by describing what you need.  Example: BI teams reduced turnaround time on custom dashboards while improving clarity and impact.   Real-Time Intelligence   Translate natural language into KQL to analyze streaming data.  Example: Analytics leads used this feature to monitor operations in real time and act on live trends.   OneLake   Manage and discover data across the business in a unified layer.  Example: A logistics firm used OneLake and Copilot to centralize shipping and warehouse data, improving transparency enterprise-wide.   Ready for Takeoff?  Whether you're building the foundation or scaling your AI strategy, Fabric and Copilot offer the tools to accelerate your journey. These platforms aren’t just for enterprise giants—they’re built for growth-stage companies ready to work smarter and unlock new value from their data.  Need help charting your roadmap? Interloop’s Flight Plan consultation can help you map what’s possible—and make it actionable.  From insight to action - let’s get you looped in .

By Ralph Jaquez  If you’re working with Microsoft Fabric or managing Power BI workloads, understanding how Capacity Units (CUs) work is essential to planning, optimizing, and scaling effectively. Whether you’re choosing the right SKU, trying to make sense of your existing capacity, or just troubleshooting mysterious slowdowns, this post breaks down what CUs are, how they’re metered, and how they affect your workloads.  What Are Capacity Units (CUs)?  A Capacity Unit (CU) is the core metric Microsoft uses to measure and provision compute power in Microsoft Fabric. It defines the amount of compute  and memory resources available to a Fabric capacity—think of it like a fixed-size engine powering your workloads.  When you purchase a Fabric capacity (e.g., F8, F64), you’re buying a fixed number of CUs per hour. For example, an F8 SKU provides 8 CUs that are continuously available—regardless of whether they’re actively being used.  Importantly, CUs are only consumed when compute work is actively being done. Creating or storing a lakehouse, warehouse, dataset, or pipeline does not use compute. Similarly, OneLake storage doesn’t pull from your CU balance.    How CU Consumption Is Metered  CU usage is tracked in CU seconds —measured based on how many CUs are used and for how long. This “metering” works like a utility bill—continuously recording usage as your workloads run.  Examples:  A job that uses 4 CUs for 100 seconds = 400 CU seconds  Eight jobs using 1 CU each for 50 seconds = also 400 CU seconds  This metering helps you understand both volume and intensity—not just how many jobs ran, but how heavily they taxed your environment.  Capacity Units Explained: The Highway Analogy  Think of Microsoft Fabric  as a highway.  Your F SKU determines how many lanes your highway has. An F8 gives you 8 lanes. An F64 gives you 64. These lanes are always available to you, and you’re billed for reserving them—regardless of whether they’re at full capacity.  Each job is like a vehicle. Some are small sedans that use one lane. Others are oversized trucks that need 4, 8, or more lanes. The wider and longer the vehicle stays on the road, the more CU seconds it consumes.  Managing Workload Flow: Smoothing, Bursting, Throttling, and Queuing  Fabric uses several mechanisms to keep things running efficiently:  Smoothing  spreads out the impact of short-term spikes across a 24-hour window. One heavy job won’t immediately result in throttling or queuing—its average usage over time.  Bursting  allows workloads to temporarily exceed your lane count— if  spare capacity is available elsewhere. It’s opportunistic, not guaranteed.  Throttling  slows jobs down rather than blocking them. A truck needing four lanes might only get two, so it moves—but more slowly.  Queuing  delays execution entirely when there’s no available capacity. Jobs sit at the on-ramp until lanes clear.  When Are CUs Consumed? Interactive vs. Background Workloads  Microsoft Fabric distinguishes between interactive and background operations:  Interactive workloads  are user-initiated—like opening Power BI dashboards. They’re optimized for speed and usually prioritized unless the system is under strain.  Background workloads  include dataset refreshes, pipelines, notebooks, and T-SQL queries—basically anything that runs behind the scenes. Even if triggered manually, these jobs draw from compute capacity.  Important note: Power BI reports hosted in shared workspaces don’t directly consume Fabric CUs. But if they connect to Fabric-powered sources (lakehouse, warehouse, etc.), any compute work performed by those sources will consume CUs—whether through Import or DirectQuery mode.  Final Note  This post offers a simplified framework to help you understand how Capacity Units work in Microsoft Fabric. In real-world use, CU consumption will vary based on workload complexity, concurrency, and data size. And as Microsoft evolves Fabric, details around metering may change—so we always recommend checking the official docs for the latest.  If you’re evaluating SKUs, troubleshooting workload delays, or want help optimizing performance, our team is here to help.  In the next post, we’ll cover how to monitor CU usage with the Fabric Capacity Metrics App—including how to read CU second charts, interpret trends, and catch early signs of performance bottlenecks.  References  Microsoft Fabric licences and concepts   Optimize your capacity   Fabric Operations   The Fabric throttling policy   Surge Protection   🚀 Get looped in.  Explore how Interloop helps you make the most of Microsoft Fabric. From implementation to optimization, we’re your copilots for modern data.  Get looped in today .

By Mclain Reese  Marketing agencies have more tools than ever—but, with that, more pressure.   Clients expect fast, personalized, data-driven campaigns. Internal teams are juggling tight timelines, shifting scopes, and tech stacks that don’t always play nice. Both the demand and the churn is constant.   That’s where smarter data infrastructure and AI automation come in. Not to replace creativity—but to make the backend operations smoother, quicker, and more reliable.  The Common Headaches  Even with a solid CRM and campaign tools in place, most agencies still run into the same issues:  Contact-Company Confusion  When contact records aren’t properly tied to companies, communications miss their mark. It makes targeting harder and reporting murky.  Messy Account Hierarchies  Parent-child account relationships—especially in platforms like HubSpot or SugarCRM—can be tough to track. But they’re essential for segmentation and performance insight.  Hitting the Wrong People  All it takes is one wrong data point to send your best campaign to the wrong inbox. Without clean data, even brilliant creative falls flat.  What Better Data + AI Actually Do  This isn’t about buzzwords—it’s about real fixes to recurring problems.  Automatic Contact-Company Associations  AI-powered data pipelines can keep your CRM clean by automatically linking contacts to the right companies. Updates push straight into HubSpot, so your campaigns stay on target.  Clearer Account Structures  Build and maintain multi-level account hierarchies in your data layer, then sync them to your CRM. The result? Better reporting and smarter targeting.  Smarter Segmentation Using Data Flags  AI can tag contacts based on job title, seniority, company match, and more. That way, your list pulls the right people—and your message lands better.  What It Looks Like in Practice  One advertising agency partnered with Interloop to improve how they were targeting email campaigns in HubSpot. The team was manually uploading Excel files with lists of contacts—a time-consuming process that often led to errors.  Interloop helped them automate that flow. Using dynamic data flags, we mapped each contact to the right company, role, and campaign segment—then synced it all into HubSpot.  The result?  Sharper targeting, more reliable execution, and several hours a week freed up to focus on campaign planning and creative. Less time in the weeds. More time on the work that moves the needle.  Ready to Take This Off Your Plate?  Whether you’re deep in campaign season or just trying to clean up your CRM, Interloop meets you where you are. Our agency partners count on us to simplify the backend—so their teams can stay focused on what they do best.  Let’s talk about what’s possible and your marketing agency’s potential to do more with data – start here .

We’re excited to share some big news. Interloop has officially joined Atlantic , a leading provider of managed services and digital transformation solutions.  This move marks a new chapter for Interloop. It allows us to scale our mission, reach more organizations, and continue helping businesses connect, analyze, and act on their data to drive smarter decisions and strategic growth.  Since 2015, our team has collaborated with clients across various industries to address one of the most significant challenges in modern business: making data usable, accessible, and actionable. From integration to automation to advanced analytics, we’ve delivered real-world results by helping customers move from fragmented systems to unified platforms. Our deep expertise in Microsoft Fabric has played a key role in making that possible.  Now, with Atlantic’s backing, we’ll be able to do even more.  "Joining Atlantic marks an exciting new chapter for Interloop," said our CEO, Jordan Berry, who will step into the role of General Manager. "This move allows us to scale our impact, reach more organizations, and stay laser-focused on our mission: helping businesses achieve more with their data. With Atlantic’s backing, we can deliver a more complete, end-to-end experience that combines our data modernization expertise with the infrastructure, security, and support growing companies need."  What This Means for You   We’re still Interloop. Same team. Same mission. Same commitment to helping you succeed with your data. But now, you’ll have access to even more resources, services, and expertise.  As part of Atlantic, we’re expanding our ability to help organizations:  Build a scalable data foundation for automation and AI  Unlock faster, more flexible analytics with Microsoft Fabric  Improve operational visibility and decision-making  Modernize systems and simplify complex tech stacks  Atlantic brings deep expertise in managed IT, cybersecurity, and cloud infrastructure. That means you’ll benefit from an end-to-end approach, from data strategy to the systems that support it.  "This partnership marks an exciting chapter for both companies," Berry added. "By joining forces with Atlantic, we can amplify our impact and bring even greater value to customers while remaining steadfast in our mission to help organizations achieve more with their data."  Want to Learn More?  You can read the official announcement from Atlantic here .  If you have questions about what this means for your business or how we can support your goals going forward, reach out to our team . We’re here and excited about what’s ahead.

By Luisa Torres  Whether you're building data products or using them to power decisions, knowing how your data is structured makes all the difference.  Microsoft Fabric makes it easy to build and scale powerful data environments. Keeping track of all those environments? Not always as simple.  That’s why we started using Fabric to report on itself—unlocking clear, intuitive visibility into everything we’ve built.  Reporting on Fabric  is all about turning its metadata tools inward: surfacing the structure, flow, and relationships behind your data products in one centralized view. It’s a simple idea with big impact—and one that’s helped our team move faster, collaborate smarter, and stay organized at scale.  Why Report on Microsoft Fabric?  See How Data Flows, Visually:  Tracking data from source through each transformation layer makes it easier to spot logic, dependencies, and opportunities for optimization.  Stay Organized as You Scale:  As your data footprint grows, this approach helps you quickly understand what you’ve built—without jumping between workspaces or asking around.  Accelerate Team Onboarding:  New and existing team members can more easily navigate the data landscape, reducing ramp-up time and improving collaboration.  Make Smarter Decisions, Faster:  Identify the most relevant tables and columns for any analysis with ease—boosting both speed and accuracy in data-driven work.  This visibility becomes especially valuable across larger or cross-functional teams, where shared context leads to faster alignment, fewe r miscommunications   and better business outcomes.  How We Do It  We tap into Fabric’s APIs using PySpark notebooks to dynamically pull detailed metadata from our workspaces and Lakehouses—everything from tables and schemas to columns and their characteristics.  To make that metadata truly usable, we assign a unique key to each asset. Then, we visualize the data in Power BI.   The result? A clean, interactive experience that helps users of all backgrounds navigate the system and understand how everything connects.  Solving a Real-World Challenge  As our own data ecosystem expanded, so did the complexity. Manually checking each Lakehouse to understand what tables existed—and how they were structured—became a time sink.  We’d already built visualizations to help make sense of specific sources. So we thought: why not apply that same principle to the platform itself?  That idea led us to develop a custom Fabric Data Explorer —a solution built on Fabric’s metadata that helps both technical and business users quickly see the shape and flow of our data environment.  It’s helped us eliminate guesswork, avoid confusion, and collaborate with more clarity across every team.  Final Thoughts  Using Fabric to report on Fabric isn’t just an internal organization hack—it’s a strategic way to see your systems clearly, work smarter, and build with confidence.  A clean, well-mapped foundation pays off. It makes maintenance easier, onboarding smoother, and evolution faster as your needs shift.  Want to explore how Fabric can help your team gain visibility and scale smarter?  🛰️ Get looped in today .

Delivering Unparalleled Value in Data Integration and Operational Syncing   What’s better than having partners whose tools complement your services and help customers get more from their data? Having those partners join forces. Our longtime collaborators, Fivetran  and Census , are officially becoming one: Fivetran has announced an agreement to acquire Census.  Why This Acquisition is Transformative  With the announcement that Fivetran—a leader in automated data pipelines—has agreed to acquire Census—a pioneer in reverse ETL technology—the future of data integration and operational syncing just got a major boost. Here’s why we’re excited:  Unified Expertise:  This acquisition combines Fivetran’s robust data ingestion capabilities with Census’s innovative operational syncing, creating a truly end-to-end data movement ecosystem.  Enhanced Customer Value:  Customers can now benefit from a more streamlined approach to transforming raw data into actionable insights—delivered directly within their operational tools.  Stronger Partnerships:  At Interloop, we’ve seen firsthand the value both platforms bring to the Microsoft Fabric ecosystem. Together, their combined potential is greater than the sum of their parts.  Innovation in Data Usage:  This collaboration lays the groundwork for the next wave of advancements in how businesses activate data across analytics and operations.  Real-Life Impact: A Customer Success Story  We’ve already supported several Fabric customers using both Fivetran and Census—and the results speak for themselves.  One example: a complex manufacturing organization faced challenges integrating their ERP, marketing platform (HubSpot), and sales CRM (SugarCRM). Using Microsoft Fabric, Fivetran, and Census, we designed a unified solution. Fivetran automated the ingestion of all three systems into Fabric and built a cross-system Power BI dashboard. Census then synced that data back into their CRM, enabling the sales team to view both order details and marketing engagement scores directly on account records.  The result?  Sales reps are now fully informed at every touchpoint, and the dashboard reflects real-time data across all three systems. Now, imagine that capability—with the tools combined under one roof.  Impact on Industry Trends and Market Growth  This acquisition marks a major shift in how companies think about and implement data movement. Here’s what we see on the horizon:  Accelerated Digital Transformation:  As data syncing becomes simpler, more businesses will adopt agile, data-driven strategies.  More Cohesive SaaS Ecosystems:  The alignment between Fivetran and Census will encourage tighter integrations across tools and platforms—especially in Microsoft-centric environments.  Reverse ETL Goes Mainstream:  This deal validates the critical role of reverse ETL in modern data stacks, pushing adoption forward.  Broader Market Accessibility:  With more accessible, powerful tooling, companies of all sizes—not just enterprise—stand to benefit.  Final Thoughts  This partnership between Fivetran and Census is more than a business transaction—it’s a leap forward for the data industry. At Interloop, we’re excited by what this unlocks for our customers and for the broader Microsoft Fabric ecosystem.  With best-in-class tools now united, and Interloop’s expertise layered on top, we’re more equipped than ever to help mid-market businesses harness the full power of their data.  Want to understand how we work together to make your data system soar? Get looped in today .

By Mclain Reese   In today’s fast-paced business landscape, automation isn’t just nice to have — it’s expected. One of our clients needed a better way to keep customers in the loop when product returns were received. But the data required to trigger those notifications? It lived across two separate systems, each with its own structure and quirks.  Our task was clear: automate email alerts with precision, pulling from disparate data sources and delivering accurate, real-time updates to both customers and internal teams.   By combining Microsoft Fabric, Census and SendGrid, we built a streamlined, scalable solution that eliminated manual effort and improved the customer experience.  The Challenge  The client needed to send real-time email notifications when product returns were received. But the required information was fragmented across two different systems. To make this work, we needed to:  Extract and align product return data from both sources  Identify the correct customer recipient for each return  CC appropriate internal stakeholders  Ensure accuracy, reliability and scalability  This wasn’t just a sync-and-send — it required precise data matching, transformation and a smart automation layer to ensure each alert fired correctly.  The Solution: Powered by Fabric, Census and SendGrid  We approached the project with a modern data activation mindset, leveraging the strength of the Microsoft Fabric ecosystem for data integration and curation, and using Census and SendGrid to drive activation and delivery.  Data Integration + Curation with Fabric  We started by extracting return data from both source systems into OneLake. Each system required its own extraction method. Once ingested, we cleaned and organized the data, then used Notebooks in Fabric to transform it into a curated Product Returns table — structured specifically for downstream use in Census.    Triggering Alerts with Census  Census enabled us to activate the curated data. We defined a sync-key that identified new product return records. When Census detected a new sync-key, it triggered the email alert workflow — no manual intervention required.  Delivering Dynamic Emails with SendGrid  With the sync event in motion, SendGrid took over. Using dynamic templates, we populated each message with personalized return details— including product info, recipient contact and internal CCs. SendGrid’s robust API allowed us to ensure reliable delivery and easy testing.  The Outcome  What was once a manual, error-prone task is now a seamless, automated workflow. Customers receive timely updates about their returns. The internal team is looped in automatically. And our client has reclaimed valuable time to focus on higher-impact work.  This use case highlights how data activation tools like Census, paired with modern delivery platforms like SendGrid, can bring real-time visibility and operational efficiency to life — with the help of a strong integration layer like Fabric.  Want to see more in action? Learn how we connect data to outcomes on the Interloop Resource Hub and get looped in  today.

By Meaghan Frost  Your Power BI dashboards are only as powerful as the people who use them. But if accessing insights means bouncing between platforms, logging into separate tools, or dealing with disconnected data, adoption suffers—and so does decision-making.  Embedding Power BI reports directly within operational platforms like Salesforce or Sugar CRM removes those barriers, delivering analytics exactly where users need them. The result? Faster insights, fewer disruptions, and better business outcomes.  Bring Insights to the Right Place  Data is most valuable when it’s available at the right moment. With embedded Power BI dashboards, your teams can access critical insights without switching between tools or disrupting their workflow. Whether it’s a sales team reviewing real-time customer data inside a CRM or a finance team analyzing trends within an ERP, keeping insights in-platform eliminates unnecessary friction.  Cost-Effective, No Extra Tools Needed  Advanced analytics shouldn’t require an expanding tech stack. By embedding Power BI reports directly into the platforms your team already uses, you eliminate the need for additional analytics tools or redundant reporting systems. Instead of paying for separate visualization tools, teams can leverage Power BI’s robust capabilities exactly where they work—without added cost or complexity.  One Dashboard, Multiple Destinations  A common frustration for business analysts and data engineers is building dashboards only to be asked to recreate the same insights elsewhere. Embedding removes this redundancy by allowing teams to create one intelligence solution that seamlessly integrates into  multiple environments. This ensures data consistency across applications while freeing up technical resources for higher-value work.  Boost Productivity, Cut the Noise  The less time employees spend searching for insights, the more time they have to act on them. Embedding Power BI reports eliminates context switching, reducing wasted effort and improving focus. Instead of logging into multiple platforms to piece together information, teams get a centralized, real-time view of the data they need—without distraction.  Security That Adapts to Your Needs  Embedding Power BI isn’t just about accessibility—it’s about smart security. With built-in filtering and row-level security, reports dynamically adjust based on user roles, permissions, and application context. That means employees, customers, and partners see only the data relevant to them—no custom-built dashboards, no unnecessary duplication, just efficient, secure data access.  The Impact in Action  One of our clients, a leading material handling equipment dealer, embedded Power BI reports directly within their Sugar CRM. This allowed their sales team to access real-time ERP data and customer insights inside Salesforce, eliminating the need to jump between platforms.  The impact?  Time savings for the sales team when preparing for customer meetings.  Centralized data access that enabled management to make faster, more proactive decisions.  Stronger adoption and engagement with analytics, since insights were available where the team already worked.  Let’s Make Your Data Work for You  Your teams shouldn’t have to chase insights—insights should come to them. Embedded Power BI dashboards bring real-time, actionable data directly into the platforms your business already relies on, transforming decision-making and operational efficiency.  Want to explore what this could look like for your business? Get looped in today.

In the rapidly advancing world of data operations, the ability to manage complex, outcome-driven data solutions is key. Interloop’s Orbit Architecture offers a flexible, resilient framework that helps DataOps engineers structure data to meet specific business needs without causing unwanted disruptions. Designed to work within the gold layer of the medallion architecture, Orbit Architecture enables each solution to stay on track, reducing risk and enhancing security. What is Orbit Architecture? Orbit Architecture is Interloop’s unique data design pattern, crafted to organize data within the "gold" layer of the medallion architecture. This layer serves as the trusted source of curated data that drives outcome-focused solutions. Much like the “separation of concerns” concept in computer science, Orbit Architecture establishes independent data structures and models within the gold layer, tailored to each specific business outcome. Each "orbit" within the gold layer becomes a specialized mission module, delivering consumption-ready, outcome-specific data to keep systems aligned and resilient. How It Works: A Use Case Imagine a scenario where your team manages two key solutions: a customer health dashboard and an operational Copilot. Both rely on data tables from the gold lakehouse. Now, your engineering team receives a request to adjust an ERP data table to better support the Copilot, and once the update is completed, the Copilot runs seamlessly. However, this update causes unintended issues on the customer health dashboard, which also uses the ERP table to link customer order data to sales opportunities. With Orbit Architecture, these disruptions are prevented. Each solution is contained within its own orbit, all connected to the gold data but insulated from one another. This structure allows each outcome to use the data model that best suits its needs—such as a dimensional model for Power BI or a graph model for an AI tool—without interfering with other data solutions. Key Benefits of Orbit Architecture 🚀 Flexibility Each orbit can be tailored to specific business requirements, giving DataOps engineers the freedom to use the data model (graph, dimensional, relational) that best supports each outcome, rather than forcing every solution into one mold. 🚀 Mitigated Downstream Effects Isolated data structures mean updates in one orbit don’t impact others, reducing the risk of unintended disruptions and data inconsistencies. 🚀 Supports OneLake Ideology Aligned with the OneLake approach, Orbit Architecture maximizes the value of a single golden version of data, allowing for analysis without duplication or data movement. 🚀 Metadata Management Orbit Architecture supports comprehensive metadata for data lineage, quality, and governance, helping to keep all systems organized and compliant. 🚀 Security and Access Control Robust security and access controls are available at each orbit level, allowing sensitive data to be protected without sacrificing accessibility for authorized users. The Wrap Up: Why Orbit Architecture Matters Orbit Architecture equips DataOps engineers with a powerful, structured framework for building flexible, outcome-specific solutions that are efficient and secure. By providing isolated data structures within the gold layer, Orbit Architecture allows engineers to mitigate downstream effects, ensure seamless collaboration, and make informed, data-driven decisions without duplication or interference. Get Looped In Looking to achieve more with your data? Get looped in   with one of our data experts today to explore how Orbit Architecture can streamline your data systems and elevate your outcomes.

By Meaghan Frost Artificial Intelligence is everywhere. This is leading to new feature announcements, new capabilities, and... sometimes leading to confusion. There are so many terms and tools to know, after all! This blog is intended to help explain some of Microsoft's key AI platforms and tools, noting what's what and supporting you on your AI learning journey. Let's dive in... Copilot Studio   Copilot Studio is a platform designed to extend and customize the capabilities of Microsoft 365 Copilot. It allows developers to create custom copilots tailored to specific business needs by integrating various data sources and actions. Key features include the ability to add knowledge from Dataverse tables, create topics with generative answers, and extend functionalities using plugins and connectors.    Azure Studio  Azure Studio is a comprehensive platform for developing, deploying, and managing AI applications. It brings together models, tools, services, and integrations necessary for AI development. Key features include drag-and-drop functionality, visual programming environments, prebuilt templates, and tools for advanced data integration and workflow orchestration.    Bot Framework  The Bot Framework is a set of tools and services for building conversational AI experiences. It includes Bot Framework Composer for designing bots, Bot Framework Skills for adding capabilities, and Power Automate cloud flows for integrating with other services. Key features include the ability to create and manage actions, define business rules, and integrate with various APIs.    Key Features and Use Cases  Copilot Studio :  Key Features : Customizable copilots, integration with Dataverse , generative answers, plugins, and connectors.  Use Cases : Enhancing productivity by creating domain-specific copilots, automating repetitive tasks, and providing contextual information to users.  Azure Studio :  Key Features : Drag-and-drop functionality, visual programming, prebuilt templates, advanced data integration, and workflow orchestration.  Use Cases : Rapid prototyping, building and refining AI applications, deploying scalable AI solutions, and managing AI workflows.  Bot Framework :  Key Features : Bot design with Composer, adding skills, integrating with Power Automate, defining business rules, and API integration.  Use Cases : Creating conversational AI experiences, automating customer support, integrating with enterprise systems, and enhancing user interactions.    Empowering Developers and Data Engineers  These tools empower developers and data engineers by simplifying the process of creating and deploying AI-driven applications.     Copilot Studio  allows developers to create custom copilots without deep technical knowledge, enabling them to focus on business-specific needs and integrate various data sources seamlessly.    Azure Studio  provides a comprehensive platform that supports the entire AI lifecycle, from model selection to deployment. Its user-friendly interface and prebuilt capabilities accelerate development and reduce the need for extensive coding.    Bot Framework  offers a robust set of tools for building conversational AI, allowing developers to create sophisticated bots with minimal effort. Its integration with Power Automate and other services streamlines the development process and enhances functionality.    Supporting the Future of AI and Machine Learning  These platforms are at the forefront of AI and machine learning innovation. In the next year, we can expect several advancements:  Enhanced Integration : Improved integration between Copilot Studio, Azure Studio, and Bot Framework, allowing for more seamless workflows and data sharing.  Advanced AI Capabilities : New AI models and tools that provide more accurate and context-aware responses, enhancing the overall user experience.  Increased Automation : More automation features that reduce manual intervention and streamline processes, making it easier to deploy and manage AI applications.    Preparing for the Future  Businesses should start preparing by:  Investing in Training : Ensuring that their teams are well-versed in using these platforms and understanding their capabilities.  Exploring Use Cases : Identifying areas where AI can add value and experimenting with pilot projects to understand the potential benefits.  Building a Data Strategy : Developing a robust data strategy to ensure that the necessary data is available and accessible for AI applications.    By leveraging these tools and preparing for the future, businesses can stay ahead of the curve and harness the full potential of AI and machine learning.  Get Looped In Trying to understand how to set your organization up for the best possible AI foundation?  We have a team of experts to support with that. Let us know you'd like to connect, and we'll happily support you on anything Microsoft, data, or artificial intelligence. Get Looped In today.