Data is piling up so quickly it’s hard to keep track. To handle this surge, we need advanced tools and platforms. We have seen a shift from traditional data warehouses to modern big data analytics tools. In this new landscape, choosing the right platform is crucial. Microsoft is leading this change. It developed Azure Synapse Analytics, a unified analytics service known for its speed and efficiency. Recently, they introduced Microsoft Fabric, a natural successor to Azure Synapse Analytics. Microsoft Fabric is a comprehensive SaaS (Software as a Service)-based platform that integrates multiple analytics services into a single solution.

In this article, we’ll dive into a detailed comparison between Azure Synapse vs Fabric, covering features, architecture, deployment models, data storage, computing engines, data integration, real-time analytics, ML and AI capabilities, security, governance, and pricing.

What is Azure Synapse Analytics?

Azure Synapse Analytics is an integrated analytics service provided by Microsoft as a PaaS (Platform as a Service) within the Azure cloud ecosystem. It unifies enterprise data integration, data warehousing, and big data analytics in a single, cohesive environment. Azure Synapse Analytics enables users to ingest, prepare, manage, and analyze data from various sources, supporting immediate Business Intelligence (BI), advanced analytics, and Machine Learning (ML) workflows.

Azure Synapse Analytics is a PaaS (Platform as a Service) offering from Microsoft. It is an enterprise analytics service that brings together enterprise data warehousing and Big Data analytics. It enables you to ingest, explore, prepare, manage, and serve data for immediate BI and ML needs.

Azure Synapse Analytics was initially launched as Azure SQL Data Warehouse (SQL DW) in 2016 and was designed to overcome the limitations of traditional, siloed storage and compute architectures by decoupling these resources.

Azure Synapse offers two SQL execution engines:

• Dedicated SQL pools for provisioned, MPP-based workloads, perfect for predictable performance and large-scale structured data.
• Serverless SQL pools for on-demand, pay-per-query analysis of data directly from storage, typically Azure Data Lake Storage Gen2.

It also includes Apache Spark pools for distributed data processing, and Data Explorer pools for high-speed log and telemetry analytics.

A significant aspect of Azure Synapse Analytics is its seamless interaction with data lakes, particularly Azure Data Lake Storage. You can define tables directly on files in your data lake, and both Spark and SQL can access and analyze those files (Parquet, CSV, JSON).

Azure Synapse Features

Microsoft Azure Synapse Analytics offers a bunch of features and tools for all your data needs, such as:

1) Unified Workspace — Microsoft Azure Synapse Analytics provides a single interface (Synapse Studio) for data ingestion, preparation, exploration, warehousing, and big data analytics.

2) Multiple Compute Models — Microsoft Azure Synapse Analytics offers Dedicated SQL Pools for predictable, high‑performance queries, Serverless SQL Pools for on‑demand, ad hoc analytics and Apache Spark Pools for big data workloads.

3) Massively Parallel Processing (MPP) — Microsoft Azure Synapse Analytics utilizes an MPP architecture to distribute query processing across numerous compute nodes, enabling rapid analysis of petabyte‑scale datasets.

4) Apache Spark Integration — Microsoft Azure Synapse Analytics natively integrates with Apache Spark which provides scalable processing for big data, interactive analytics, data engineering, and machine learning workloads.

5) Data Integration Capabilities — Microsoft Azure Synapse Analytics includes native data pipelines, powered by the same integration runtime as Azure Data Factory, to support seamless ETL/ELT operations.

6) Security and Compliance — Microsoft Azure Synapse Analytics features advanced security features, like Dynamic Data Masking, Column‑ and Row‑Level Security, Transparent Data Encryption (TDE) for data at rest, Integration with Microsoft Entra ID (formerly Azure Active Directory) for authentication and role‑based access control.

Also, it offers features like Virtual Network Service Endpoints and Azure Private Link for powerful, secure connectivity.

7) Interoperability with the Azure Ecosystem — Microsoft Azure Synapse Analytics integrates deeply with Azure services like Azure Data Lake Storage, Power BI, Azure Machine Learning, and various other Azure services (like Azure Data Explorer, Logic Apps, and more).

8) Language Flexibility — Microsoft Azure Synapse Analytics supports multiple languages and query engines (T‑SQL, Python, Scala, .Net, and Apache Spark SQL) to suit varied developer and analyst preferences.

...and many more features.

Microsoft built Azure Synapse Analytics with a few key goals in mind :

• To help you get value from your data faster.
• To unify the world of analytics and data development.
• To enable responsible data sharing, transformation, and visualization, often with a helping hand from ML, AI, and BI tools.
• And, of course, to manage and protect your data with a robust set of security and privacy features.

What is Microsoft Fabric?

Microsoft Fabric was launched in May 2023. Microsoft announced fabric at the Microsoft Build conference, calling it an all-in-one solution for data and analytics. Just six months later, Microsoft Fabric was open to everyone.

Microsoft Fabric is the natural successor to Azure Synapse. It is an end-to-end analytics platform developed by Microsoft, designed to simplify and unify the data analytics process for organizations. It integrates various data services and tools into a single SaaS (Software as a Service) solution, enabling users to manage data movement, processing, transformation, and visualization all in one place. It’s perfect for big companies that need strong analytics without the hassle of dealing with multiple services.

Microsoft Fabric Features

Microsoft Fabric is packed with a bunch of features and tools for all your data needs. Here’s what they offer:

1) Data Integration — Microsoft Fabric simplifies data integration from nearly any source into a unified, multi-cloud data lake.

2) OneLake — OneLake serves as the central hub for all data within Microsoft Fabric. It automatically indexes data for easy discovery, sharing, governance, and compliance, making sure that all data across the organization is accessible and manageable from one place.

3) Data Engineering — Microsoft Fabric includes tools to help design and manage systems for organizing and analyzing large volumes of data, supporting complex ETL (Extract, Transform, Load) scenarios.

4) Real-Time Analytics — Microsoft Fabric supports real-time data processing, enabling users to explore, analyze, and act on large volumes of streaming data with low latency, which is crucial for timely decision-making.

5) Fabric Data Factory — Data Factory is Microsoft’s data integration service. Data Factory is integrated in Microsoft Fabric, allowing you to create, schedule, and manage data pipelines for moving and transforming data at scale.

6) Copilot AI Assistant in Microsoft Fabric — Copilot leverages AI to enhance productivity by allowing users to interact with the platform using natural language. This feature can be used across notebooks, pipelines, and reports to automate tasks and generate insights.

7) Data Warehousing — Microsoft Fabric provides a highly scalable data warehouse with industry-leading SQL performance, allowing independent scaling of compute and storage resources.

8) Business Intelligence — Microsoft Fabric integrates seamlessly with Microsoft 365, enabling the creation of visually immersive, interactive insights directly within familiar apps like Excel, Teams, and PowerPoint.

9) AI and Machine Learning — Microsoft Fabric incorporates AI capabilities at various levels, including support for building custom ML models and enabling advanced analytics directly within the platform. It also supports generative AI for creating tailor-made AI experiences.

10) Data Governance and Compliance — Microsoft Fabric offers robust data governance and compliance features, including data classification, access controls, and auditing capabilities.

11) Integration with Power BI — Microsoft Fabric has deep integration with Power BI, which is a powerful business intelligence tool for creating interactive dashboards and reports.

… and a whole lot more features!!

Check out this video for in-depth insights into the features, functionalities, and updates about Microsoft Fabric.

So, what’s the big picture for Microsoft Fabric? Why would you use it?

• To get an end-to-end, integrated analytics solution without having to stitch together a bunch of separate services.
• To simplify data management and access with OneLake acting as that central hub for all your data.
• To speed up the journey from raw data to actionable insights through user-friendly experiences that work well together.
• To empower a wide range of people in your organization – data engineers, data scientists, analysts, and even business users – with tools tailored to their needs, all within one platform.
• To insearse productivity and unravel deeper insights with the help of embedded AI and Copilot AI Assistant features.
• And to make administration and data governance easier by centralizing these functions.

What Is the Difference Between Azure Synapse and Fabric?

Now for the main event: how do these two platforms, Azure Synapse vs Fabric compare against each other?

If you want the short version and don’t feel like digging in just yet, check out the table below for a quick overview of Azure Synapse vs Fabric.

Azure Synapse Analytics	🔮	Microsoft Fabric
PaaS (Platform as a Service)	Platform Model	SaaS (Software as a Service)
User manages deployment, configuration, and scaling	Infrastructure Management	Microsoft handles infrastructure, updates, and operations
Deployed in Azure subscription as workspace	Deployment Model	Delivered as managed cloud service with tenant-based access
Modular. Operates as an Azure subscription workspace. It combines various compute engines (Dedicated SQL Pools, Serverless SQL Pools, Apache Spark Pools, Data Integration, Data Explorer) with Azure Data Lake Storage Gen2 (ADLS Gen2) as its underlying storage layer.	Architecture	Unified. Revolves around OneLake, a central data lake storage system that gathers data from various sources. It’s designed with a unified architecture, integrating several components and workloads on top of OneLake.
Manual provisioning and scaling of individual components	Resource Management	Automatic scaling with shared Fabric capacity units
Azure Synapse Studio	Interface	Microsoft Fabric Portal
Multiple engines managed by the user: ▶ ️ Dedicated SQL Pools: MPP, provisioned, pause/resume. ▶ ️ Serverless SQL Pools: Pay-per-query, scales on demand. ▶ ️ Spark Pools: Managed Spark, auto-scaling. ▶ ️ Data Explorer: Real-time analysis (Kusto). ▶ ️ Pipelines Integration: Azure Data Factory-based. User manages scale and allocation.	Compute Engine Architecture	Unified Capacity Model. Users purchase Fabric Capacity Units (CUs) shared across all workloads. ▶ ️ Spark Engine: For Data Engineering & Data Science. ▶ ️ SQL Engine (Polaris): For DW and Lakehouse. ▶ ️ KQL Engine: For Real-Time Analytics. ▶ ️ Analysis Services: For Power BI datasets. ▶ ️ All engines are serverless within purchased capacity.
Uses Synapse Pipelines (based on Azure Data Factory) for ETL/ELT. 90+ connectors. Integrated with Azure services (ADLS, ML, Power BI, Azure Active Directory, DevOps). Requires explicit linked services configuration.	Data Integration & Ecosystem	Includes Data Factory (in Fabric): hundreds of connectors, Dataflows Gen2 (Power Query), Pipelines, Copy Jobs. Features automatic integration, OneLake Shortcuts, Mirroring (real-time replication). Deep integration with other Microsoft services.
SQL Analytics (T-SQL on pools), Big Data (Spark), Data Explorer (KQL), Notebooks, BI (Power BI), ML (Azure ML, SynapseML), Data Science (code-driven). Modular, code-focused.	Analytics Workloads	Unified experience for all workloads: SQL Endpoint, Data Engineering (Spark), Data Science (ML, AutoML, MLflow), Power BI (native), Real-Time Analytics, and Copilot AI Assistant across workloads.
Real-time via Azure Data Explorer/ADX and Synapse Link (e.g. for Cosmos DB). Spark Structured Streaming supports streaming data. Requires integrating multiple Azure services; no dedicated streaming pipeline UI.	Real-Time Analytics	Real-Time Intelligence (RTI) workload unifies streaming analytics. Combines Azure Data Explorer with a user-friendly UI and no-code connectors, Real-Time Hub, automatic ingestion, and Data Activator for no-code alerts/triggers. End-to-end streaming solution.
ML via Azure ML pipelines, SynapseML in Spark, serverless SQL PREDICT. AI is siloed (Azure ML/OpenAI integration). No unified Copilot AI Assistant across Synapse, but exists in Power BI/Azure Data Studio.	ML, AI & Copilot Integration	Deep, unified AI/ML integration. Dedicated Data Science experience, MLflow, AutoML, prebuilt Azure AI services (OpenAI, Language, Translator). Copilot AI assistants across all workloads and interfaces.
Multi-layered security: Managed VNet, Private Endpoints, RBAC, SQL permissions, Microsoft Entra ID, Transparent Data Encryption, TLS, Column/Row Security, DDM. Governance via Microsoft Purview (manual integration required).	Security & Governance	Built-in, simplified security: OneLake governed by workspace roles, item sharing, and external source permissions. Network security is mostly managed by Microsoft. Microsoft Purview built-in for automated discovery, lineage, sensitivity labels. Centralized Purview Hub.
Component-based: Dedicated SQL Pools, Serverless SQL, Spark Pools, Pipelines, Storage all billed separately. Synapse Commit Units (SCUs) for compute discounts.	Pricing Model + Cost + Licensing	Unified: Purchase Fabric Capacity Units (CUs), shared across all workloads. Billed per Capacity Unit Second. OneLake storage billed per GB. Free mirroring up to capacity-based limit. Power BI licenses needed for smaller capacities.

Now let’s break down the nine key detailed differences between Azure Synapse vs Fabric.

1) Azure Synapse vs Fabric — Architecture & Deployment Model

Azure Synapse vs Fabric platforms are built and deployed in different ways.

Azure Synapse Architecture

Azure Synapse operates as a PaaS (Platform as a Service). In a PaaS model, Microsoft manages the underlying infrastructure – the servers, the operating systems, the networking. You, as the user, are responsible for deploying and managing the Azure Synapse Analytics service itself, configuring its various components (like SQL pools or Spark pools), scaling them up or down, and developing your applications and queries that run on it.

Let’s break down its core architectural components and internal workings.

1) Azure Synapse SQL (Dedicated & Serverless SQL Pools)

Azure Synapse SQL serves as the engine for both traditional data warehousing and on-demand query processing:

a) Dedicated SQL Pools — Dedicated SQL pools are provisioned with dedicated compute resources measured in Data Warehousing Units (DWUs) and utilize a Massively Parallel Processing (MPP) architecture, where:

• Control Node — Acts as the entry point, receiving T-SQL queries, parsing, and optimizing them before decomposing into smaller, parallel tasks.
• Compute Nodes & Distributions — Data is horizontally partitioned (by default into 60 distributions) using methods such as hash, round robin, or replication. Each compute node processes its assigned distribution(s) concurrently.
• Data Movement Service (DMS) — When a query requires data from multiple distributions (like joins or aggregations), DMS efficiently shuffles data between compute nodes to assemble the final result.

b) Serverless SQL Pools — Serverless SQL pools provide on-demand query capabilities directly over data stored in Azure Data Lake Storage or Blob Storage. They employ a distributed query processing (DQP) engine that automatically breaks complex queries into tasks executed across compute resources, scaling dynamically without the need for pre-provisioned infrastructure.

2) Apache Spark Pools

Azure Synapse integrates an Apache Spark engine as a first-class component for big data processing, machine learning, and data transformation. The Spark pools:

• Support multiple languages (Python, Scala, SQL, .NET, and R).
• Offer auto-scaling and dynamic allocation to reduce cluster management overhead.
• Seamlessly share data with Azure Synapse SQL and ADLS Gen2, enabling integrated analytics workflows.

3) Data Integration (Synapse Pipelines)

Azure Synapse incorporates the capabilities of Azure Data Factory within its workspace, allowing you to build and orchestrate ETL/ELT workflows that can:

• Ingest data from various sources (over 90+ supported).
• Transform and move data between storage (Azure Data Lake Storage Gen2) and compute layers (SQL or Apache Spark).
• Automate data workflows with triggers, control flow activities, and monitoring within a unified experience.

4) Data Storage – Azure Data Lake Storage Gen2

Azure Synapse Analytics utilizes ADLS Gen2 as its underlying storage layer, offering:

• Hierarchical file system semantics.
• Scalability and high throughput for both structured and unstructured data.
• Seamless integration with both SQL and Apache Spark engines.

5) Azure Synapse Studio

Azure Synapse Studio is the unified web-based interface serving as the development and management environment for the entire Azure Synapse Analytics workspace. It offers:

• Integrated authoring tools for SQL scripts, Spark notebooks, and pipelines.
• Monitoring dashboards displaying resource usage and query performance across SQL, Apache Spark, and Data Explorer.
• Role-based access controls are integrated with Azure Active Directory for secure collaboration.

Here’s how Azure Synapse Analytics operates:

➥ Control Node Orchestration — When a user submits a query (via T-SQL or notebooks), the control node handles query parsing, optimization, and task decomposition. It formulates an execution plan by analyzing data distribution, available indexes, and workload characteristics.

➥ Compute Node Processing & Data Distribution — In a dedicated SQL pool, once the control node generates the execution plan, it dispatches multiple parallel tasks to compute nodes. Each compute node processes its local partitioned data (i.e., its distribution) concurrently, leveraging MPP to minimize latency on large datasets.

➥ Data Movement Service (DMS) — For operations requiring data from different distributions (such as joins, aggregations, or orderings), DMS shuffles data efficiently between compute nodes, ensuring that intermediate results are properly aligned for final result assembly.

➥ Serverless Distributed Query Processing (DQP) — In the serverless SQL model, the query engine automatically decomposes a submitted query into multiple independent tasks executed over a pool of transient compute resources. This abstraction removes the burden of infrastructure management from the user while ensuring that the query scales to meet demand.

Now, let’s move on to Microsoft Fabric’ architecture.

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Microsoft Fabric Architecture

Microsoft Fabric takes a different approach; it’s a SaaS (Software as a Service) offering. With SaaS (Software as a Service), Microsoft handles almost everything behind the scenes; the infrastructure, the software updates, a lot of the operational heavy lifting. You interact with Microsoft Fabric through its web interface or APIs, focusing more on using the analytics capabilities rather than managing the underlying services.

Microsoft Fabric is designed with a unified architecture that revolves around OneLake. OneLake is a central data lake storage system. It can gather data from Microsoft platforms, third-party services like S3 and GCP, and also on-premises data sources such as databases, filesystems, and APIs.

Microsoft Fabric architecture is layered and integrates several components:

➥ OneLake: Centralized Storage

OneLake provides a centralized and scalable storage solution for Microsoft Fabric. It stores data in the open Delta Lake format, enabling efficient management of structured and unstructured data. Here are some key features of OneLake:

• All data in OneLake is stored in the Delta Lake format, supporting ACID transactions, schema enforcement, and efficient data versioning.
• Users can create OneLake shortcuts to external data locations, such as Azure Data Lake Storage Gen2 or Amazon S3, allowing access without data duplication.
• OneLake’s Data Hub serves as a central interface for discovering, exploring, and utilizing data assets within the Microsoft Fabric ecosystem.

➥ Integrated Workloads and Services

Microsoft Fabric offers several workloads and services that operate on top of OneLake, each tailored for specific data tasks:

• Fabric Data Factory — A data integration service that simplifies ingesting, transforming, and orchestrating data from diverse sources.
• Synapse Data Warehousing — A lake-centric data warehousing solution that allows independent scaling of compute and storage, facilitating large-scale analytical workloads.
• Synapse Data Engineering — Utilizes Apache Spark to support the design, construction, and maintenance of data pipelines and data estates.
• Synapse Data Science — Enables the creation and deployment of end-to-end data science workflows, from model development to operationalization.
• Synapse Real-Time Analytics — Focused on real-time data analysis, ideal for processing and analyzing streaming data from applications, websites, and devices.
• Power BI — Integrates with Microsoft Fabric to allow users to create interactive reports and dashboards that draw insights from data stored in OneLake.
• Data Activator — A no-code platform for data observability and monitoring, enabling users to set up alerts and triggers based on data conditions without writing code.

Microsoft Fabric’s architecture is really flexible and open. It runs on the Delta Lake format, which means it can integrate with a bunch of third-party tools and services already set up for Delta Lake. This kind of openness makes it a lot easier to build data solutions that work well together.

🔮 Azure Synapse vs Fabric TL;DR:: Azure Synapse Analytics (PaaS (Platform as a Service)) is deployed in an Azure subscription as a workspace. Compute (DWUs/vCores for SQL, Spark clusters, Data Explorer) is provisioned per workspace. You manage and scale each resource. On the other hand, Microsoft Fabric (SaaS) is delivered as a managed cloud service. A Fabric tenant contains a unified OneLake storage and multiple workspaces with shared Fabric capacity units (CUs). Compute and services (Data Factory, Data Lakehouse, Spark, etc.) automatically scale on demand.

Azure Synapse vs Fabric both of em have web-based studios for design and monitoring. Azure Synapse Analytics uses Azure Synapse Studio, whereas Microsoft Fabric has its own Fabric portal. Synapse workspaces use standard Azure networking (VNet, firewalls) and access roles. Microsoft Fabric workspaces use workspace-level roles built into the tenant. Overall, Azure Synapse Analytics is more like a traditional cloud PaaS (Platform as a Service) that you set up, and Fabric behaves like a turnkey SaaS (Software as a Service).

2) Azure Synapse vs Fabric — Data Storage Models

Now, where your data lives and how it’s structured is another major point of difference.

Azure Synapse Storage Models

Azure Synapse integrates closely with Azure Data Lake Storage Gen2 as its primary storage layer. When you create a dedicated SQL pool, data is stored as tables in ADLS Gen2 under the hood, but accessed via SQL. Likewise, Synapse Spark can read/write Parquet/Delta files in the lake. Azure Synapse Analytics offers multiple storage options: you can store structured data in SQL pools (row/column stores), semi-structured data in Data Lake (e.g. Parquet, JSON), and you can even attach external storage. For example, Azure Synapse Link allows real-time analytics on operational data by automatically placing snapshots into the lake. In summary, Azure Synapse Analytics uses separate data storage (ADLS Gen2) plus its SQL engine’s storage; data may be copied or virtualized.

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Microsoft Fabric Storage Models

Microsoft Fabric uses a different approach: OneLake is the single, unified data lake for everything. OneLake is automatically created for each Fabric tenant and is built on ADLS Gen2. All data in Microsoft Fabric (data warehouses, lakehouses, etc.) is stored in OneLake in an open format so that every analytics engine can access the same files. You never provision storage separately; OneLake scales with your data and all workloads see one consistent view. Microsoft Fabric doesn’t have dedicated SQL pools or traditional relational storage like Azure Synapse Analytics. Key features of OneLake: it holds data in “Lakehouse” folders and “Files” sections, it lets you create OneLake shortcuts (like views) to external ADLS paths, and it enforces a single security/governance fabric across everything.

🔮 Azure Synapse vs Fabric TL;DR:: Azure Synapse Storage is tied to ADLS Gen2 or Blob storage and is fully keyed to your subscriptions. All you have to do is set up containers or folders for raw, curated, etc. You manage access via storage account ACLs or firewalls. Azure Synapse Analytics itself does not provide global data governance; you need to connect it to Microsoft Purview for cataloging if needed (we will cover this section in a later section). Data stored in Parquet or Delta can be queried by both SQL and Spark, but managing files and tables is up to you. Microsoft Fabric, on the other hand, is fully tied to OneLake and OneLake only. You don’t worry about accounts or containers; simply upload data to lakehouses or link external sources. Microsoft Fabric automatically handles metadata registration of tables and files. All Fabric services (SQL, Spark, Data Activator, etc.) read and write the same data format with no duplication. Security labels and lineage flow through OneLake under the hood.

3) Azure Synapse vs Fabric — Compute Engine Architecture

The compute engine architecture dictates how data processing occurs, influencing performance, scalability, and cost. Both Azure Synapse vs Fabric offer powerful compute options, but their underlying structures and management models differ.

Azure Synapse Compute Engine Architecture

Azure Synapse Analytics offers a diverse set of compute engines, allowing you to pick the right tool for the job, but it largely adheres to a provisioned or semi-managed model. You typically define and manage the scale of these resources, providing a high degree of control.

Here is what Azure Synapse provides:

➥ Dedicated SQL Pools (formerly SQL Data Warehouse) – this is a massively parallel columnar database that you provision with a fixed number of DWUs or vCores. It separates compute from storage and automatically distributes queries across nodes. You can pause/resume it to save cost.

➥ Serverless SQL Pools – a pay-per-query model where you can run T-SQL over files (Parquet, CSV) in the lake without provisioning a cluster. It scales on-demand and you pay per TB scanned.

➥ Apache Spark Pools – managed Spark clusters (autopurging VM workers) for big-data processing and machine learning. You code in PySpark, Scala, or .NET.

➥ Azure Data Explorer (Kusto) – sometimes used with Azure Synapse Analytics via Synapse Link or integration; allows real-time, log/telemetry analysis with KQL queries. (Azure Synapse Analytics itself doesn’t natively run Azure Data Explorer; you spin up a Kusto pool separately if needed.)

➥ Pipelines Integration Runtime – for data integration work, Azure Synapse Analytics uses Azure Data Factory under the hood, including its own parallel compute for mapping data flows.

Azure Synapse’s compute engine requires careful management. You need to adjust resources, scaling policies, and performance. Often, a dedicated team with platform engineering skills is essential. They help guarantee smooth operations and control costs across various compute options.

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Microsoft Fabric Compute Engine Architecture

Microsoft Fabric flips the script on compute management with its Unified Capacity Model.

Instead of provisioning separate types of compute engines, you purchase Fabric Capacity. This capacity is measured in Fabric Capacity Units (CUs) and comes in different SKU sizes (like F2, F4, all the way up to F2048, and also P SKUs if you’re coming from Power BI Premium).

This single pool of Capacity Units (CUs) is then shared dynamically across all the different Microsoft Fabric experiences you use ... whether you’re running a Spark job in Data Engineering, a SQL query in your Data Warehouse, a KQL query in Real-Time Intelligence, or refreshing a Power BI dataset. Microsoft Fabric takes care of allocating resources from this shared pool to the engine that needs it at that moment.

Under the hood, Microsoft Fabric still has specialized engines:

• A Spark Engine powers the Data Engineering (Notebooks, Spark Job Definitions) and Data Science experiences.
• A SQL Engine (based on the Polaris query engine technology) drives the Data Warehouse experience and the SQL Endpoint of the Lakehouse. It’s optimized for running T-SQL queries over the Delta Lake data in OneLake.
• A KQL Engine is used by the Real-Time Intelligence experience (for KQL Databases and KQL Querysets) to handle streaming data and log analytics.
• An Analysis Services Engine (the same one that powers Power BI Premium) is used for Power BI datasets, including those in Direct Lake mode.

All these engines operate in a serverless manner. While you’ve bought the overall capacity, you’re not managing individual clusters for each engine type. Microsoft Fabric handles the underlying infrastructure and the scaling of these engines within the limits of your purchased capacity.

To handle bursts and make sure things stay fair, Microsoft Fabric uses smoothing and throttling. Smoothing helps average out your compute usage over a set period, like 5 minutes for interactive jobs or 24 hours for background ones. This way, temporary spikes aren’t a big deal. If your usage keeps exceeding your purchased capacity even with smoothing, Microsoft Fabric may start throttling your jobs. This means they might slow down or get turned down altogether.

🔮 Azure Synapse vs Fabric TL;DR: All Microsoft Fabric compute runs on the shared Capacity Units (CUs) you purchase. Compute isn’t locked per workload; if your Data Factory pipelines aren’t running, those CUs can be used by Spark or SQL, etc. This “one pool for all” model allows Microsoft Fabric to shuffle resources fluidly. On the other hand, in Azure Synapse, each engine is carved out separately. Azure Synapse Analytics lets you independently scale each engine; for example, you can increase DWUs for the SQL pool only, separate from the Spark cluster.

4) Azure Synapse vs Fabric — Data Integration & Ecosystem

Getting data in, transforming it, and connecting to other services; that’s what data integration is all about. Azure Synapse and Microsoft Fabric approach this differently; here’s how they compare.

Azure Synapse Integration and Ecosystem

Azure Synapse uses Pipelines (based on Azure Data Factory) for ETL/ELT orchestration. You can create data pipelines with copy activities, data flow transformations, lookups, stored procedure calls, etc. In Azure Synapse Studio, you get the Azure Data Factory GUI and activities identical to Azure Data Factory. Azure Synapse Analytics supports both Mapping Data Flows (visual Spark transformations) and Synapse SQL pipelines.

Synapse pipelines ship with 90+ built-in connectors: databases (SQL Server, Oracle, Teradata), SaaS (Software as a Service) apps (Salesforce, SAP), file stores (S3, FTP), REST endpoints, and more. You can push data from on-premises via a self-hosted Integration Runtime or tap into cloud sources over managed VNet endpoints.

Azure Synapse Analytics is, as you’d expect, deeply integrated with the broader Azure ecosystem. This includes:

• Azure Data Lake Storage Gen2 (For Storage)
• Azure Machine Learning (For developing, training, and deploying ML models).
• Power BI (For business intelligence and reporting).
• Microsoft Entra ID (formerly Azure Active Directory) (For authentication and authorization).
• Azure DevOps (For CI/CD pipelines for your analytics solutions).
• Azure Stream Analytics (For real-time data ingestion).

Azure Synapse Analytics’s ecosystem is very Azure-centric and component-based. It primarily integrates with other Azure PaaS (Platform as a Service) and IaaS services. These integrations are powerful, but they often involve explicitly configuring "linked services" and understanding the boundaries and interaction points between Azure Synapse Analytics and each external Azure service. This offers a lot of capability within the Azure world but might require a bit more setup and management for each integration compared to a more deeply embedded SaaS (Software as a Service) model.

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Microsoft Fabric Integration and Ecosystem

Microsoft Fabric aims to make data integration and ecosystem connections feel more built-in.

Microsoft Fabric includes Data Factory (in Microsoft Fabric) as its integration service. Microsoft Fabric Data Factory is effectively the same engine as Azure Data Factory, so it supports the same connectors for most Azure sources, like:

• Dataflows Gen2 — These use the familiar Power Query interface for visual data transformation, offering over 300 transformations. This is great for users who are already comfortable with Power Query in Power BI or Excel.
• Data Pipelines — These are for orchestrating more complex data workflows. You can use them to refresh your Dataflows Gen2, run notebooks or scripts, and implement control flow logic like loops and conditional execution.
• Copy Jobs / Fast Copy — Microsoft Fabric includes a simplified way to quickly move data from a wide range of sources into OneLake, designed to be easy to use.
• Connectors — Microsoft Fabric Data Factory aims to provide access to hundreds of connectors. For on-premises data, it uses the On-premises Data Gateway (the same one used by Power BI and other services). It’s worth noting that while the goal is parity with Azure Data Factory, there are some conceptual differences in how connections and data sources are handled (like Fabric Data Factory doesn’t have the "dataset" concept in the same way Azure Data Factory does; it uses "connections" more directly).

Microsoft Fabric comes with OneLake Shortcuts and Mirroring, which are fundamental to Fabric’s integration strategy. As we discussed earlier, OneLake Shortcuts provide a way to virtually access data in external storage locations (like ADLS Gen2 or S3) without physically ingesting it. Mirroring, on the other hand, replicates data from operational databases into OneLake in near real-time, keeping it fresh for analytics. Both significantly reduce the need for traditional ETL to simply get data into the platform.

Microsoft Fabric is also designed for deep and often automatic integration with its own components and other Microsoft services.

• OneLake
• Power BI
• Azure Machine Learning
• Azure AI Services (Prebuilt)
• Microsoft Purview
• Microsoft 365
• Broader Azure Services
• Third-party Services

Microsoft Fabric’s ecosystem is designed to break down barriers and make integration feel effortless. As a SaaS (Software as a Service) platform with OneLake at its heart, many of the integrations are tightly woven, eliminating the need for manual connections. The platform’s deep connections to Purview, its Direct Lake mode for Power BI, and its unified capacity model are prime examples. By streamlining these integrations, you can significantly simplify the process of building end-to-end analytical solutions.

**🔮 Azure Synapse vs Fabric TL;DR:**Microsoft Fabric’s ecosystem is more unified: everything is built into one UI with shared assets in OneLake. For instance, Microsoft Fabric pipelines can easily connect to the OneLake lakehouses or the Fabric Warehouse, since they’re first-class citizens. Azure Synapse Analytics can also orchestrate loading into its SQL pools or Data Lake, but often you have to manage ADLS separately. Both systems integrate with broader Azure services. Here is a quick rundown:

➥ Pipeline Integration — Microsoft Fabric Data Factory ≈ Synapse/Azure Data Factory. Most activities and triggers (time, event) work similarly. New Fabric features include built-in Email/Teams activities and deployment pipelines for CI/CD. Azure Synapse Analytics pipelines can continue to be used or migrated.

➥ Mapping Flows — Azure Synapse Analytics supports Azure Data Factory mapping data flows; Microsoft Fabric does not. Instead, Microsoft Fabric uses PowerQuery (Dataflows) for transformations. Microsoft suggests leaving complex mapping flows in Azure Data Factory/Synapse and invoking them from Microsoft Fabric if needed.

➥ Connectors — Microsoft Fabric pipelines support the same broad set of Azure-centric connectors as Synapse. For example, both can read/write Azure Blob, SQL DB/MI, Cosmos DB, ADLS Gen2, etc. Some less-common connectors (BigQuery, SAP OLAP, etc.) may only be in Synapse/Azure Data Factory for now.

➥ Governance & Catalog — Azure Synapse Analytics has a linked Power BI service and can connect to Microsoft Purview for the data catalog. Microsoft Fabric has built-in governance (data catalog, lineage) across all workloads with Microsoft Purview under the hood. In Microsoft Fabric, pipelines and data assets automatically become part of the tenant catalog. Azure Synapse Analytics requires manual Microsoft Purview registration.

➥ Ecosystem Tools — Azure Synapse and Microsoft Fabric allow notebooks (Synapse notebooks or Git-based notebooks; Microsoft Fabric notebooks in Data Engineering and Data Science). Azure Synapse Analytics can use Azure ML studio (links out), whereas Microsoft Fabric includes ML integration in the portal.

5) Azure Synapse vs Fabric — Analytics Workload Support

Both Azure Synapse vs Fabric aim to support all modern analytics workloads (batch SQL, BI reporting, big data, etc.), but the way they bundle them differs.

Azure Synapse Analytics Workload Support

Azure Synapse is essentially a data analytics platform in one package. It natively handles:

➥ SQL Analytics — You can run T-SQL queries on dedicated or serverless pools. Azure Synapse Analytics integrates with Power BI for reporting, and you can use SQL for both data warehousing and interactive analytics.

➥ Big Data (Spark) — Spark pools handle large-scale data prep, machine learning (with MLlib), and processing unstructured data.

➥ Data Explorer — With Synapse Link, you can query time-series and log data using Kusto (KQL) alongside your other data.

➥ Notebooks and BI — Azure Synapse Studio provides notebooks and a basic set of built-in charts/dashboards. For enterprise BI, many users connect Azure Synapse Analytics to Power BI.

➥ Machine Learning — Azure Synapse Analytics offers integration with Azure ML; you can invoke ML models or train using Synapse Spark. There’s also SynapseML (MMLSpark) for distributed ML.

➥ Data Science — Azure Synapse Analytics has notebooks and Python, but lacks some “point-and-click” data science UI – it’s mostly code-driven.

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Microsoft Fabric Analytics Workload Support

Microsoft Fabric covers more via separate “workloads”:

➥ Synapse SQL Endpoint — Microsoft Fabric’s SQL analytics (Warehouse) handles typical warehousing queries. It’s T-SQL compatible and integrates directly with Power BI. Basically, Microsoft Fabric’s SQL endpoint is a renamed Synapse SQL.

➥ Data Engineering (Spark) — Same Spark as Synapse, with Microsoft Fabric’s notebooks for PySpark/Scala.

➥ Data Science — Microsoft Fabric adds a dedicated ML interface with built-in support for Python/R notebooks, MLflow tracking, and Git integration. It’s meant to streamline data science workflows end-to-end. It still runs on Spark under the hood.

➥ Power BI — Power BI is fully native to Fabric (a workload), so reporting and semantic models live in the same environment. Synapse simply integrates with Power BI externally.

➥ Real-Time Analytics — Microsoft Fabric’s Real-Time Intelligence (previously part of Synapse) now lives here with a GUI and event triggers. Azure Synapse Analytics has Data Explorer and streaming via Spark, but Microsoft Fabric bundles it with monitoring and no-code rules.

➥ Copilot AI Assistant Integration — Both platforms have begun integrating [Copilot AI Assistant](https://copilot.microsoft.com