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Welcome to another episode.

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Today's topic is one you've probably heard

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in meetings or tech articles.

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Microsoft fabric, but if you're like most people,

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you're not entirely sure what it actually is.

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Is it just another Microsoft product,

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a rebrand of something old,

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or is it something completely different?

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Let me give you the short answer.

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Microsoft fabric is not just another product.

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It's a complete data platform,

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one place where you can do all your data work,

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storage, transformation, analytics, and reporting,

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all in one place.

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By the end of this episode,

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you'll understand what fabric is, why Microsoft built it,

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and how the main pieces fit together.

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Grab your coffee and let's dive in.

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The old way, why data was broken?

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To understand why fabric exists,

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you need to understand the problem it solves.

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20 years ago, if you were running a company,

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you'd buy a server for storing data,

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a separate tool for running reports,

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and yet another product for real-time analysis.

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Before you know it, you've got five different systems

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each with its own login, its own way of working,

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and its own team managing it.

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For a long time, data lived in silos, sales data set in one system,

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customer data in another,

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and financial data, somewhere else entirely.

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If you wanted to ask a question

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that crossed those systems,

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like which customers are buying the most profitable products,

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you had to move data around.

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Export it from one system, transform it, load it into another,

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then do it again when the data changed.

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More time was spent moving data between systems,

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than actually analyzing it.

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Data integration became a full-time job for entire teams.

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The worst part was that every time you moved data,

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you created another copy, another version of the truth,

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and pretty soon nobody knew which copy was the right one.

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Microsoft looked at this fragmentation

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and asked the simple question,

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"What if all these services work together?

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"What if instead of stitching together separate products,

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"you had one platform that did everything?"

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That question is what led to Microsoft fabric?

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What Microsoft fabric actually is?

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So what is fabric? Here's the simplest definition.

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Microsoft fabric is a unified data and analytics platform.

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One place for all your data work,

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think of it like a modern office building.

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Each tool like Power BI or Azure Synapse

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is a room inside that building.

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They have their own purpose, furniture, and tools,

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but they're all part of the same structure.

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You don't need to walk outside to get from one room to another.

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They're connected. That's what fabric does.

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It brings together data engineering,

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data integration, data warehousing,

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real-time analytics, and business intelligence,

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all in one platform.

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Everything runs on a single SaaS platform,

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which means no separate infrastructure to manage,

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no servers to provision on networking to configure.

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Microsoft handles all of that for you.

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Since its launch in 2023,

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fabric has become the fastest growing data product

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in Microsoft's history.

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Today, over 25,000 customers use it,

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including 80% of the Fortune 500.

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That's a lot of adoption in a very short time.

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The key difference is this.

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In the old world, you bought separate products

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and tried to make them talk to each other.

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With fabric, you don't stitch together different services anymore.

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It's one experience, one login, one way of working,

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and that changes everything.

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One lake, the foundation.

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Now let's talk about the foundation

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that makes all of this work.

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At the heart of fabric is something called one lake.

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The easiest way to think about one lake

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is like one drive for your data.

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You know how one drive gives you one place

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to store all your personal files

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and every app on your computer can access those files.

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One lake does the same thing,

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but for your organization's data.

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Here's what makes it powerful.

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Every fabric workload,

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whether it's a lake house, a warehouse,

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or a real-time analytics job,

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automatically stores its data in one lake.

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You don't have to manually copy anything

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or configure connections.

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It just happens.

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And there's only one one lake per tenant,

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one single source of truth for your entire organization.

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Now you might be thinking,

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what if I want to switch platforms later?

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Am I locked in?

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The answer is no.

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One lake stores data in open formats like Delta Parque,

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an industry standard format used by Databricks

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and Apache Spark.

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You're never locked into Microsoft.

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If you want to move your data somewhere else,

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you can.

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It's your data.

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But here's where one lake gets really interesting.

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Shortcuts.

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A shortcut is a virtual pointer to data

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that lives somewhere else.

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You can create a shortcut to data stored in AWS, S3,

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Google Cloud, or even on premises storage.

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And it shows up in one lake as if it was stored there.

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No data movement required, no duplication.

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You just point to it and it appears.

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One lake is built on top of Azure Data Lake Storage.

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Microsoft's enterprise-grade cloud storage.

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But you don't manage that infrastructure

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as it's all handled as a SaaS service.

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You don't provision servers or configure networking.

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You just use it.

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What does this mean for you?

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It means you can have data from multiple sources,

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cloud storage, on-premises databases,

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third-party platforms, all appear in one place

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without duplication.

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And the real power, any data written to one lake

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can be used by any fabric workload immediately.

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You write it once and every tool in fabric can see it.

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That's the magic of one lake.

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Lake houses, the data engineering hub.

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So you've got your data in one lake.

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Now you need to work with it.

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That's where the lake house comes in.

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Here's the simplest definition.

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A lake house is a data store that combines

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the flexibility of a data lake with the structure of a database.

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In a traditional data lake, you can store anything

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like the raw CSV files, JSON documents, images, video files,

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but it's messy with no structure or schema.

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So you can't query it easily.

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In a traditional database, everything is structured and queryable.

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But you can only store certain types of data.

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A lake house gives you both.

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You can keep raw files like CSV, JSON, or even images

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in a lake house.

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And you can also store structured tables

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that you can query with SQL.

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Both live in the same place.

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Those tables use Delta Park A format,

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the same open fast format used by Databricks

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and other major platforms designed

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to handle massive amounts of data.

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Lake houses support multiple tools.

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You can use Spark for large-scale data processing.

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You can use Python for machine learning.

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You can use SQL for simple queries.

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Whatever tool you prefer, the lake house

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can handle it.

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Think of a lake house as your workspace

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for building data pipelines.

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You bring raw data in, transform it, clean it,

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and prepared for analysis.

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You can run machine learning models on it.

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You can create training datasets all in one place.

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For data engineers who prefer a code-friendly environment,

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lake houses support notebooks.

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These are interactive documents where you write code,

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see results, and document your work,

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like a lab notebook for data.

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You can write Spark code in one cell,

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see the output in the next, and explain what you did

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in the cell after that.

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The lake house is the entry point

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for most data engineers working in fabric.

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It's where the heavy lifting happens.

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And because it stores everything in one lake,

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any data you put in a lake house is immediately available

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to every other fabric workload in the warehouses,

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the SQL Analytics engine.

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Now, if the lake house is the data engineering hub,

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think of the warehouse as the SQL Analytics engine,

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built for a different kind of user.

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A warehouse in fabric is a fully managed

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SQL Analytics database.

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If you're a database administrator or a SQL developer,

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this is where you'll feel at home.

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You can write T-Sycle queries.

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You can create views, stored procedures, and security policies.

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It looks and feels like a traditional data warehouse,

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but here's what makes it different.

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Unlike a traditional data warehouse,

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fabrics warehouse stores data directly in one lake.

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There's no separate storage layer,

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no extra infrastructure to manage.

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The data lives in one lake,

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and the warehouse is just the interface you use to query it.

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This matters because the warehouse and the lake house

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share the same data, so you're not duplicating anything.

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If a data engineer loads data into a lake house,

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a SQL developer can query it from a warehouse immediately,

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no copying, no moving, it's the same data accessed

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through different tools.

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The warehouse is built for large-scale analytics.

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Under the hood, it uses massive parallel processing

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to handle huge data sets.

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And Microsoft has been investing heavily in performance.

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In the last six months alone,

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performance has improved by 36%.

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That's a lot of improvement in a short time.

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If you're migrating from another platform,

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fabric has a migration assistant

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that helps you move from snowflake or synapse.

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It analyzes your existing setup

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and guides you through the process.

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The goal is to make migration as painless as possible.

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So what's the difference between a lake house and a warehouse?

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It comes down to the interface.

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We're houses are SQL first.

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They're built for people who think in terms of tables,

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queries and stored procedures.

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Lake houses are developer first.

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They're built for people who want to use Spark, Python,

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and notebooks.

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But underneath, they both store data in one lake.

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There are two ways of working with the same data.

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Getting data in, data factory, mirroring, and shortcuts.

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So you've got one lake for storage,

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lake houses for transformation and warehouses for querying.

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But how does the data actually get in?

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That's where data factory steps in.

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Data factory is Fabrics built in data integration service.

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And it gives you over 200 connectors

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to pull data from databases, cloud storage, SAS apps, and more.

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It's the most used data integration tool in the world

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and for good reason.

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Within data factory, you get data flows gen 2,

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which gives you a no-code way to transform data

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using power query.

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If you've ever used power query in Excel or Power BI,

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you already know the drill.

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You click through a visual interface

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to clean reshape and combine data, no coding required,

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and it's enterprise-grade, scalable to handle massive data

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sets.

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Over 22 billion orchestrations run on data factory every month,

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which means billions of data movements happen automatically,

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making it a serious piece of infrastructure.

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But there's an even simpler way to get data into one lake, mirroring.

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Mirroring copies data from external databases

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like Azure, School, Oracle, or Google BigQuery

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into one lake in near real time.

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And here's the best part.

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It's free, included in the service with no extra cost.

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You just point to your source database

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and Fabric handles the rest.

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Then there are shortcuts, which we touched on earlier,

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but they deserve more attention.

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Shortcuts are virtual pointers to data stored elsewhere,

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requiring no data movement.

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You create a shortcut to an AWS tree bucket,

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and it shows up in one lake as if it was stored there,

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while the data stays where it is.

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You're just pointing to it.

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And now shortcuts can do more than just point.

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Shortcut transforms let you apply AI-powered transformations

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on the fly.

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You can run sentiment analysis on text data, translate content,

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or detect personally identifiable information

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all without moving the data or building a pipeline.

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The transformation happens as you access it.

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The goal of all these tools is simple.

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Bring all your data into one lake without building complex

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pipelines, whether you use data factory for traditional ETL,

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mirroring for near real time replication

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or shortcuts for virtual access, the result is the same.

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Your data ends up in one lake ready for any Fabric workload.

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Real-time intelligence databases in AI.

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So far, we've talked about moving data and storing it,

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but what about data that never stops moving?

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Streaming data from IoT sensors, website click streams,

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telemetry from devices, that's where real-time intelligence

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comes in.

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Fabric handles streaming data through something called

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an event house, which stores and queries

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high volume time series data using KQL,

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the custochery language.

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It's the same engine that powers Azure Data Explorer

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built for speed, so you can ingest millions of events per second

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and query them in milliseconds.

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This matters.

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And here's why.

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46% of fabric customers already use real-time intelligence.

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That's nearly half.

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The use cases are everywhere.

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A logistics company tracking delivery trucks,

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a retailer monitoring in store for traffic,

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a manufacturer watching sensor data from factory equipment,

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all happening in real time.

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But fabric isn't just about analytics.

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It also includes operational databases

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like a SQL database for relational data

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and Cosmos DB for no-school workloads.

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These are the same databases you'd use to run your applications.

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And because they're built into Fabric,

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they automatically store their data in one lake.

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No extra pipelines, no manual copying,

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your transactional data and analytical data live in the same place.

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Now let's talk about AI because this is where fabric

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gets really interesting.

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AI integration runs deep across the platform

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and you get pre-built models for text analysis,

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translation and sentiment analysis.

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You can use them directly in your data flows

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without writing any code, just point to your data,

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select the model and it runs.

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And then there's Copilot, which is embedded across fabric.

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It helps you write queries, build reports,

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and create pipelines.

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You just describe what you want in plain English

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and Copilot generates the code.

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It's like having a junior data engineer

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sitting next to you ready to help at any moment.

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The vision here is simple.

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AI works on your data without moving it anywhere.

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Your data stays in one lake and the AI models come to the data.

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No copying, no exporting, no security risks.

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The data never leaves your governed environment.

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Power BI, governance and the business value.

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Now we get to the part most people actually see

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the front door to Insights Power BI.

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It's used by 550,000 organizations

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and 34 million users every month,

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making it the most widely used business intelligence tool

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in the world.

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And in fabric, it becomes something even more powerful.

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Here's the key.

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In fabric, Power BI connects directly to one lake

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using something called direct lake mode.

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This is different from the traditional import mode

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where you copy data into Power BI's internal storage.

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Instead, direct lake queries the data

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where it sits in one lake with no refresh needed,

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no duplication and the data is always current

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and blazing fast.

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Underneath every Power BI report is a semantic model.

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The brain of your report where you define business logic,

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measures and relationships.

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And now you can build these models entirely

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in a web browser.

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Web modeling is generally available

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so you can create a semantic model, build relationships

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and write DAX measures all from a browser even on a Mac.

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No desktop app required, but all this data power

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means nothing without governance.

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And fabric has governance built in from the start.

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Microsoft Perview provides sensitivity labels

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that follow your data everywhere.

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If a report contains sensitive information

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that labels stays with it, even when exported.

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Data loss prevention policies automatically detect

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sensitive data and block unauthorized sharing

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and access controls ensure only the right people see the right data.

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Domains help you organize data by business area

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with separate governance rules

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so your finance data can have stricter controls

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than your marketing data.

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Each domain can be managed by its own team,

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making it governance that scales with your organization.

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Now let's talk about real results.

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This isn't just theory.

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Eastman Chemical used fabric to reduce sales preparation

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time by 83%, what used to take four hours now

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takes about 40 minutes.

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Eaton Solutions cut manual effort by 75%

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with processes that require 10 to 15 steps now taking two.

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And Sonata Software saves 30,000 hours

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of manual reconciliation every year,

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redirecting that human effort to higher value work.

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The cost model is straightforward.

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You pay for capacity, the compute power that runs your workloads.

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Microsoft has built in smoothing and search protection

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to manage peaks.

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Smoothing spreads the cost of background operations

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over 24 hours, so you don't get hit with sudden spikes

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and search protection limits background jobs

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during busy periods to keep interactive performance stable.

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You pay for what you use

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and you don't get surprised.

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Let's bring this all together.

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Fabric isn't just another Microsoft product.

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It's a complete rethinking of how data platforms should work.

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One platform, one lake, one copy of data and infinite possibilities.

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And with AI and co-pilot built in, it's accessible to everyone,

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not just data professionals, but business analysts, sales teams,

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operations managers, anyone who needs answers from data.

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Here's your homework.

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Go to fabric, Microsoft.com, start a free trial

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and create your first workspace.

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Just try it.

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You'll see how the pieces fit together in a way

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that no amount of reading can replace.

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If this episode helped you understand fabric better,

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subscribe on your favorite podcast platform

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and share it with someone who's starting their data journey.

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They'll thank you.

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Next time, we'll break down one lake short cuts in plain English.

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How they work, why they matter,

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and how to use them without moving data around.

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That's one you won't want to miss.

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Until then, keep learning.