Connect to any data with Shortcuts, Mirroring and Data Factory using Microsoft Fabric
Easily access and unify your data for analytics and AI — no matter where it lives. With OneLake in Microsoft Fabric, you can connect to data across multiple clouds, databases, and formats without duplication. Use the OneLake catalog to quickly find and interact with your data, and let Copilot in Fabric help you transform and analyze it effortlessly. Eliminate barriers to working with your data using Shortcuts to virtualize external sources and Mirroring to keep databases and warehouses in sync — all without ETL. For deeper integration, leverage Data Factory’s 180+ connectors to bring in structured, unstructured, and real-time streaming data at scale. Maraki Ketema from the Microsoft Fabric team shows how to combine these methods, ensuring fast, reliable access to quality data for analytics and AI workloads. Access your data instantly. Connect to Azure, AWS, Snowflake & on-prem sources in OneLake without moving a single file. Get started with Microsoft Fabric. Replicate databases with near-zero latency. Fast, reliable analytics. Check out Mirroring in Microsoft Fabric. ETL, data prep & movement at scale. Fabric Data Factory makes it simple & efficient to move data faster. See how it works. Watch our video here. QUICK LINKS: 00:00 — Access data wherever it lives 00:42 — Microsoft Fabric background 01:17 — Manage data with Microsoft Fabric 03:04 — Low latency 03:34 — How Shortcuts work 06:41 — Mirroring 08:10 — Open mirroring 08:40 — Low friction ways to bring data in 09:32 — Data Factory in Microsoft Fabric 10:52 — Build out your data flow 11:49 — Use built-in AI to ask questions of data 12:56 — OneLake catalog 13:36 — Data security & compliance 15:10 — Additional options to bring data in 15:42 — Wrap up Link References Watch our show on Real-Time Intelligence at https://aka.ms/MechanicsRTI Check out Open Mirroring at https://aka.ms/FabricOpenMirroring Unfamiliar with Microsoft Mechanics? 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And importantly, we’ll show you how easy it is for your team members to find the data that you brought in with the new OneLake catalog and how you can use Copilot and Fabric as you work to interact with your data wherever it lives from OneLake. And joining me today from the Microsoft Fabric Product team is Maraki Ketema. Welcome to the show. - Thanks for having me. - And thanks so much for joining us today. But before we get into this, why don’t we set a bit of context for anyone who’s new to Microsoft Fabric. So, Microsoft Fabric is a preintegrated optimized SaaS environment, which provides a comprehensive set of data analytics and an AI platform with built-in capabilities for data integration, data engineering, data science, data warehousing, real time intelligence, data visualization and overall data management. Underpinning Fabric is its multi-cloud data lake, OneLake, which gives you a central point for data to be discovered and accessed wherever it resides across your data estate at scale. Now, we’ve covered Microsoft Fabric in a lot of past shows, but today we really want to be able to specifically demystify how it can help you get a better handle on your data. - Well, it helps on a number of levels. You’ve already mentioned scalability and with all of the integrated capabilities for data teams to collaborate on building clean quality data, it can be done at scale for any use case. And OneLake really is the key to getting a handle on your data by making it accessible with support for open formats like Delta Parquet and Iceberg. This helps eliminate traditional barriers to working with your data, and we give you a variety of methods to bring your data into OneLake like Shortcuts where you can virtualize data from where it’s stored, which creates a pointer to any structured open file-based tabular data or unstructured files, even images and multimedia. All this happens without duplicating the data or options for Mirroring where you can create an always up-to-date replica of the source in Fabric. And this is great for databases and data warehouses with proprietary formats where your business critical data may be stored. Now both of these options can be used like any other native data in OneLake and they require no ETL. Then for all of your other sources that require data transformation or read or write capabilities, you can use the hundreds of connectors provided by Data Factory and Microsoft Fabric to make your data natively available in OneLake and to bring streaming data, you’ll use Microsoft Real Time Intelligence. You’ll likely use these techniques to different extents depending on your data and AI needs and whichever method you use to connect data, we make it available with minimal latency. This is super important, for example, for real time or gen AI tasks because they’re less predictable whereas a user or agent interacts on the backend. This can quickly create a series of requests to retrieve data which need to happen fast to ground the AI so that responses aren’t delayed. Fabric takes care of all of this for you at scale and at low latency. - So quality data then become super accessible whenever you need it and wherever it lives. Why don’t we show them a few examples of this? - Sure. So, today I’m going to walk you through an e-commerce system and it’s for a Supermart with a grocery department where we need to quickly understand demand versus supply as well as market competition over prices and get a 360 view of operations and customer experiences. Now, different teams, including marketing, analytics and IT are collaborating together in a single Fabric workspace. Now here the marketing team creates promotions daily and they work with different vendors who are using different systems to store data and there’s no standard file type. The good news is that we can connect to all of these different systems using Shortcuts. Let me show you how that works. Here under Get Data, I can see my option to bring data in. I’ll choose a new shortcut. You’ll see that I have both Microsoft and non-Microsoft locations. In this case, I want to connect to Amazon S3 for unstructured data. From here, if I don’t already have a connected data source, I can create a new connection using credentials for the service. But to save time, I’ll use an existing connection. I’ll choose the second option here. I can explore the data available to me and I can choose the specific folders I want. I’ll pick a few for Kentoso and confirm. Now the data’s in OneLake, and I can expand the folders and look at different data like these markdown files with texts, which contain customer feedback and I have a nice preview of the data to understand what’s in it. Additionally, I have some image data on my local drive that I want to share with others on my team as we’re trying to figure out the best placement for in-store promotions. The good news is that I can also shortcut to all of this data in OneLake directly from my desktop. Let’s take a look. Here I am in Windows File Explorer and I’m connected to OneLake and I can interact with these files and sync them right from here. In fact, here I’m adding an image file from our grocery department, and from the status I can see that it’s already synced. Now if I move back over to Fabric, you’ll see that it’s just synced into my lakehouse view. From here, I can preview the image right away. So now I have the information I need to start analyzing customer sentiment and where we can place point of sale promotions. Again, in both examples, the file data still remains at the source, just like shortcuts on your desktop, the data doesn’t actually live in OneLake, but always stays in sync. Shortcuts in Microsoft Fabric are supported for open storage formats like Microsoft Dataverse, Azure Data Lake Storage, Google Cloud Storage, Databricks, Amazon S3, and any S3 compatible stores and more. And you can also use Shortcuts for on-premise data sources using Fabric on-premise data gateway. - And beyond your file data, your Supermart is probably dependent on operational data that’s sitting in databases and warehouses, all of which might have their own proprietary formats. So what’s the path of least effort then to bring that data in? -So this is where Mirroring in Microsoft Fabric comes into play. It makes it easy to replicate data into OneLake and storage is included as part of your existing Microsoft Fabric capacity. Let’s jump in. Here, you can see my sales dashboard, which is broken down by category, location and even has some forecasting built in. And on the back end, I already have various sources mirrored into my Fabric workspace in OneLake that are feeding into this particular view. -I’m going to use Mirroring and create a new item to connect to Azure SQL DB and bring in data from the Supermarts in the same region. I’ll filter by mirror and then select the Azure SQL Database option. From here, I’ll add my connection details. I’ll type the database name and the rest securely auto completes. After I connect, it takes seconds to show the table in the database. And from there it’s just one more click to create the mirror database and now it’s ready to use in OneLake. Just like Shortcuts, all of this works without ETL or moving the source data. And now if we go back to our Get data page, you’ll notice that most of the Azure databases are directly supported for Mirroring as well as Snowflake. That said, you aren’t limited to using Mirroring for just these sources. You’ll notice that I have two sources here, Salesforce and our legacy on-prem SQL database. These were brought into OneLake using open mirroring. Open mirroring is an API, which lets users and data providers bring data in from any source while keeping them in sync. You can learn more about open mirroring at aka.ms/FabricOpenMirroring. - So Mirroring then has a great potential than in terms of being a frictionless way to bring your data in. But how real time then is the synchronization? - It’s near real time. Once you’ve created the Mirror database and brought your data in, you don’t need to do anything else to keep the data fresh. On the backend Fabric is continuously listening for changes and making updates to the data in OneLake. So I’ll go ahead and refresh my sales dashboard and you can see the updates flow in. Our sales just quadrupled in seconds with this new database. That’s actually because we’ve added a lot more stores with their sales data. - This is really a game changer then in terms of the time to insights and that you have these low friction ways to bring your data in. That said though, there are lots of cases where you might want to transform your data and need to be able to use more data integration work before you bring it in. -Right. And that’s where Data Factory and Microsoft Fabric is a powerful engine that can bring in your data at petabyte scale with everything you need to prep and transform the data to. Let’s take a look. As you begin to create pipelines, to bring your data, you’ll see that we now have more than 180 connectors to the most common data types. And these span both Microsoft and non-Microsoft options. And connecting to one is like we showed before with Shortcuts. If I click on Snowflake, for example, I just need to add connection settings and valid credentials to add the data source to my pipeline. And from here, let me go deeper on the pipeline experience itself. Here is one that I’ve already started. It takes our Supermart data through the bronze and silver layers before landing the curated data in the gold layer. To gain a deeper understanding, we can actually use Copilot to generate a summary of what the pipeline is doing and in seconds, as Copilot explains here, data is loaded before data is curated, and we have schema validation, which picks up on file mismatches and places them in a separate folder after sending an alert. The pipeline provides a visual view of all of these steps. Then if I move over to my notebook, you’ll see that it applies transformations on the data before it’s loaded into our gold layer. -Now, once my data’s in OneLake, I can also start building out my own data flows. Here’s a table that I just pulled in from Excel that looks at grocery transactions over the past quarter. This table is currently super wide, making analysis, very, very difficult. Here’s where the power of Copilot comes in. I don’t need to know the right buttons or terms or words. Sometimes it can just be as simple as describing how I want my tables to look, and I’ll submit this prompt, and almost instantly the table is transformed and more optimized for analysis. While I’m at it, I can also use Copilot to do a simple operation like renaming a column and pay attention to the middle column. The name was just changed. But what if someone inherits the state of flow? Copilot can also provide descriptions of what your query is doing to help save time. It’s described the query and it’s easy to understand for anyone. And here’s the real power of everything we’ve done today. As you can see in our lineage, we now have all our connected data sources from Shortcuts, Mirroring, and now Data Factory. Not only can I now see everything connected in my dashboard, but I can also use natural language with built-in AI to ask questions of my data. -In this case, I want to get ahead of wastage issues in our grocery department. My dashboard doesn’t quite help me here. This is where we can use the built-in AI to ask questions of the data. So I’ll go ahead and prompt it with which products are at risk of spoilage and required discounting. It’ll take a sec, and once that completes, I’ll get a top level view of the products at risk with details about their expiration dates. Under that, I can see the breakdown of its reasoning with a detailed table of each item with quantity per store. And there’s even the raw SQL query, the agent used to derive these insights. - And that was a really powerful example of what you can do once your data is in OneLake. But what if I’m not as close to the data and I want to be able to discover data that I have access to? - OneLake has the one Lake catalog, which is a central place for data users to discover the data they need and manage the data they own. Let’s take a look from the OneLake catalog, I can see everything I have access to. On the left, I can filter the views by my items, items endorsed by others on my team favorites and individual workspaces. At the top, I can also filter by different types of data artifacts, insights, and processes. Let’s take a look at the Ask questions. AI experience I just showed, and here I can see the lineage for how the data’s coming in. That said, with all this ease of data discovery, it’s super important to control and manage access to the data that’s exposed through OneLake. And what’s great is that data compliance controls from Microsoft Purview are built in. I can see the sensitivity labels for any data asset, and from a lineage perspective, these labels are automatically inherited from upstream parent data sources. Permissions are also fully manageable, and if there’s a direct link to this artifact, I’ll be able to see it here. Under the direct access tab, I can see who and which groups have access to this data already. And as a data admin, I can also add users to grant access to specific resources. In fact, I’ll go ahead and add you to this one, Jeremy and I can determine if you’re allowed to share it with others, edit or even view the data itself. - Okay, so now if we move over to my screen, I can see that the Ask Queue item has been shared with me, and it’s available right here. Now to show you the process to discover and request something, I’ll first filter data in my catalog view by semantic models just to narrow the list down a bit and for items that you can see but not access. You’ll see this icon here and there’s a button to request access like with this operations model here. And when I use that, I can add a message for why I’m requesting and send it to the admin for that data to get their approval. - And beyond access management, the integrations with Microsoft Purview for data security and compliance keep getting deeper. Also, there’s another option for bringing data into OneLake that we haven’t demonstrated, and that’s real time streaming data. That’s because there’s an entire show on how to do that using real-time intelligence that you can check out at aka.ms/MechanicsRTI - It’s really great to see all the ways that you can bring quality data into OneLake for analytics to ground your AI workloads. In fact, you can bring data in from OneLake for use with your Gen AI apps and agents using Azure AI Foundry, which we’ll cover more in an upcoming show. So, Maraki what do you recommend for all the people watching right now to learn more? - It’s simple, you can try everything I show today and everything else Fabric has to offer by signing up for a generous 60 day free trial. We don’t even require a credit card to get started. - So now you have lots of options to bring data in and to start working with it. 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Driving Better Patient Outcomes with Care Management Analytics in Healthcare data solutions
In today's rapidly evolving healthcare landscape, effective data driven decision is more crucial than ever. The ability to analyse, manage, and optimize patient care processes relies on the seamless integration of diverse data sources like clinical, claims, social determinants of health etc. Leveraging the innovative medallion Lakehouse architecture, care management analytical template capabilities provide a robust platform for organizations to derive actionable insights and drive better patient outcomes. The Medallion Lakehouse for Care Management analytics Built on the foundation of the healthcare data solutions in Microsoft Fabric which utilizes the medallion Lakehouse architecture. This architecture consists of three foundational layers, each playing a critical role in transforming raw data into actionable insights: Bronze: The Raw Zone The Bronze layer serves as the raw data zone, storing all data in its original format. This data includes various sources such as patient encounters, conditions, treatment adherence records, and other relevant care management information. By maintaining this data in its raw form, organizations ensure the integrity and completeness of the dataset, providing a solid foundation for subsequent processing and analysis. Silver: The Enriched Zone In the Silver layer, data from the Bronze Lakehouse is enriched and transformed into a standardized format for analysis. This layer stores metadata and file references based on healthcare interoperability standards such as FHIR (Fast Healthcare Interoperability Resources). The enriched data provides a holistic view of the patient record, integrating different modalities in healthcare data solutions which are critical for comprehensive care analysis. Gold: The Curated Zone The Gold layer represents the curated zone, where data is refined and structured for advanced analytics and reporting. By building a comprehensive data model, the data is optimized for, predictive analytics, and reporting dashboards that can provide deep insights into care quality, patient outcomes, and operational efficiency. Conceptual Architecture Care management analytics involves integrating and analysing diverse datasets, including clinical, claims and social determinants of health data. The medallion Lakehouse architecture in Microsoft Fabric offers the flexibility to ingest and process these data types at scale. The data flows from raw data ingestion to transformation into the Gold Lakehouse format. End to End execution steps Step 1: Create a workspace and add health solutions capability. Step 2: Set up healthcare data solutions on your Fabric workspace. Follow the guidance from the deployment wizard and add sample data if needed. Step 3: Select the Care Management analytics capability and click on Deploy. Step 4: Copy the sample data downloaded into the bronze lakehouse under Process\Clinical\FHIR-HDS folder Step 5: Run the care management analytical data pipeline to transform the data from the bronze lakehouse to gold lakehouse. s of Care Management analytics Step 6: Access the Power BI dashboards once the above steps are completed to view detailed visualization on Clinical and Claims data. Transforming Care Management analytics with healthcare data solutions Healthcare data solutions care management analytics capability provides a comprehensive template solution for customers and partners to unify and analyze diverse data. By leveraging the medallion Lakehouse architecture, healthcare organizations can unlock the potential of their data, enhance care coordination, and drive better patient outcomes. The seamless integration of raw, enriched, and curated data layers ensures that insights are not only actionable but also scalable and sustainable. For more information on how Healthcare data solutions can revolutionize your care management analytics, please review our detailed documentation and get started with transforming your healthcare data landscape today. https://go.microsoft.com/fwlink/?linkid=2284603 FHIR® is a registered trademark of Health Level Seven International, registered in the U.S. Trademark Office, and is used with their permission. Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations.
