Azure Synapse vs Fabric: 9 Things You Should Know (2025)

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 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.

Check out this video on Microsoft Azure Synapse Analytics for a complete overview of its capabilities and 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 this table below for a quick overview of Azure Synapse vs Fabric.

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.

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. 

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.

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.

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, but Microsoft Fabric has it embedded across more workloads out-of-the-box (e.g. Copilot AI Assistant chat in pipelines, SQL, and dataflows). Azure Synapse Analytics has some support (Azure ML Studio and Power BI have their own Copilots) but Microsoft Fabric aims to unify AI assistance everywhere.

6) Azure Synapse vs Fabric — Real-Time Analytics

Streaming and real-time analytics are handled differently in each platform.

Azure Synapse Real-Time Analytics

Azure Synapse offers real-time insights mainly via Azure Data Explorer (ADX) and Synapse Link features. For example, Azure Synapse Link for Cosmos DB or other databases continuously pulls data into Synapse (SQL or Spark) or into Azure Data Explorer pools. You can also use Apache Spark Structured Streaming jobs in Synapse to process Event Hub or IoT Hub data in real-time. But remember that these pieces (Stream Analytics, Event Hub, Data Explorer) are separate services that you might have to wire together. Azure Synapse Studio does not have a dedicated “streaming pipeline” interface; you typically manage it via Azure Data Factory or custom jobs.

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Microsoft Fabric Real-Time Analytics

Microsoft Fabric introduces the Real-Time Intelligence workload to unify streaming analytics. Real-Time Intelligence (RTI) in Microsoft Fabric combines Azure Data Explorer under the hood with a friendly UI and built-in no-code connectors. The Real-Time Hub in Microsoft Fabric lets anyone in your org register streams of data (clicks, sensors, logs) and run queries/analytics on it. It automatically handles ingestion, transformation, storage and visualization of “data in motion”. You can define triggers (with Data Activator) to take actions (alerts, emails, Teams messages) on events. All of this is governed by the Microsoft Fabric data catalog. In short, Microsoft Fabric’s Real Time Intelligence is an end-to-end streaming solution baked into the analytics platform, whereas Azure Synapse Analytics requires stitching multiple Azure services together.

7) Azure Synapse vs Fabric — ML, AI & Copilot Integration

Azure Synapse and Fabric platforms now embrace AI, but Microsoft Fabric was built for it from day one.

Azure Synapse ML, AI Integration

Azure Synapse has supported ML in various ways. You can run Azure ML pipelines from Azure Synapse Studio or use Synapse ML (MMLSpark) in Spark to build, track, and deploy models. Azure Synapse Analytics also introduced features like serverless endpoint SQL PREDICT calls for SQL and has Azure ML capabilities in notebooks.

As of now, Microsoft has not announced a dedicated Copilot AI Assistant experience for Azure Synapse Analytics. However, Copilot AI Assistant in Azure is generally available and integrates with various Azure services. For example, Power BI and Azure Data Studio have their own Copilot features (like “Copilot for SQL” or “Copilot for Power BI” in preview).

But remember that AI support in Azure Synapse is somewhat siloed: Azure Synapse Analytics can call out to Azure OpenAI or Azure ML, but there isn’t a unified in-product assistant across all of Synapse’s workflows.

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Microsoft Fabric ML, AI, and Copilot Integration

Microsoft Fabric aims to weave AI and ML capabilities more deeply and pervasively into its unified platform. Microsoft Fabric includes a dedicated "Data Science" experience designed for an end-to-end machine learning workflow. It provides various tools for data preparation, training ML models (using Spark ML, scikit-learn, TensorFlow, PyTorch, etc. within notebooks), tracking experiments (which can integrate with the Azure Machine Learning Model Registry), deploying models, and scoring data. Microsoft Fabric also offers AutoML capabilities, both through a code-first approach and a low-code user interface.

Microsoft Fabric also provides prebuilt Azure AI services, allowing you to use certain Azure AI services (specifically Azure OpenAI Service, Azure AI Language, and Azure AI Translator) directly within Microsoft Fabric without needing to provision these services separately in Azure or manage API keys.

Microsoft Fabric deeply integrates AI assistants across workloads. Microsoft Fabric offers Copilot AI Assistant experiences within every interface:

• Data Factory Copilot AI Assistant (assists in creating or modifying pipelines and generating SQL queries).
• Data Warehouse Copilot AI Assistant (provides a chat interface for SQL in Microsoft Fabric, enabling T-SQL generation and query optimization).
• Data Activator Copilot AI Assistant (helps define triggers on streaming data).
• Data Science Copilot AI Assistant (aids in writing Python or Spark code).
• Power BI Copilot AI Assistant (offers functionalities from Power BI, now integrated into Microsoft Fabric for report creation).

8) Azure Synapse vs Fabric — Data Security & Governance

Security and governance are critical, and both Azure Synapse vs Fabric platforms leverage Azure’s ecosystem.

Azure Synapse Data Security and Governance Model

Azure Synapse Analytics offers a multi-layered security model, leveraging many standard Azure security features:

➥ For Network Security:

You can deploy your Synapse workspace into a Managed Virtual Network for network isolation from the public internet.  

Private Endpoints allow you to access your Synapse workspace and its SQL pools securely from your virtual network using private IP addresses.

Data Exfiltration Protection helps prevent unauthorized copying of data out of your Synapse environment.

Firewall rules can be configured to control access to your SQL pool endpoints and the workspace itself from specific IP addresses.

Azure Synapse Analytics also respects Network Security Group (NSG) rules if deployed within your VNet subnets.

➥ For Access Control:

Azure Role-Based Access Control (RBAC) is used at the Azure resource level to manage who can create, delete, or manage the Synapse service itself and its main components like SQL pools, Spark pools, and Integration Runtimes.  

Synapse RBAC roles (like Synapse Administrator, Synapse SQL Administrator, Synapse Spark Administrator, Synapse Contributor, Synapse Artifact User, etc.) provide more fine-grained permissions within the Synapse workspace. These control who can create or run notebooks, pipelines, SQL scripts, and access different compute resources.  

SQL permissions (using standard T-SQL GRANT and DENY statements) are used to control access to data within your Dedicated SQL pools and Serverless SQL pools (e.g., access to specific tables, views, or schemas).  

Azure Active Directory (Microsoft Entra ID) is deeply integrated for authentication and identity management. You can use Microsoft Entra ID users and groups to grant access at all these levels. Azure Synapse Analytics also supports configuring Microsoft Entra-only authentication for SQL pools, disabling SQL logins.

➥ For Data Protection:

Transparent Data Encryption (TDE) automatically encrypts data at rest for your SQL pools. Data is encrypted in transit using TLS/SSL. Within SQL pools, you can implement Column-Level Security (control who can see certain columns), Row-Level Security (RLS) (control who can see certain rows based on user context), and Dynamic Data Masking (obscure sensitive data for non-privileged users).

Azure Key Vault integration is recommended for securely managing secrets like connection strings and keys used by pipelines or code.  

➥ For Threat Detection & Monitoring:

Integration with Azure Monitor provides metrics and logs for performance monitoring and operational insights. For SQL pools, features like SQL Auditing (tracks database events), SQL Threat Detection (identifies anomalous database activities), and Vulnerability Assessment (helps discover and remediate security misconfigurations) are available.

➥ For Data Governance (Microsoft Purview Integration)

Your Azure Synapse workspace can be registered and scanned by Microsoft Purview (the broader Azure data governance service). Microsoft Purview can then capture metadata from your Synapse assets (like SQL tables, views, Spark tables, pipelines) and map out data lineage (how data flows through your Synapse processes).

Azure Synapse Analytics's security model is granular and leverages broader Azure security constructs. It relies heavily on standard, well-understood Azure security features like Azure RBAC, Microsoft Entra ID, Virtual Networks, Azure Key Vault Integration, and Azure Monitor, applying them to its specific components.

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Microsoft Fabric Security and Governance Model

Microsoft Fabric, being a SaaS (Software as a Service) platform, approaches security and governance with a more built-in and abstracted philosophy:

➥ For OneLake Security:

OneLake is built on ADLS Gen2, it inherits many of its underlying security capabilities. Access to data in OneLake is primarily governed through Fabric workspace roles (Admin, Member, Contributor, Viewer). These roles determine what users can do with the items (like Lakehouses, Warehouses, reports) within a workspace, and by extension, the data associated with those items in OneLake.

Beyond workspace roles, Microsoft Fabric allows for item sharing, which provides more granular, item-level permissions. You can share specific reports, lakehouses, or warehouses with users or groups who may or may not have a role in the workspace, and define what they can do with that specific item.

Whenever you are using OneLake Shortcuts to external data, Microsoft Fabric respects the security and permissions of the target data source.

➥ Network Security:

As a SaaS (Software as a Service) service, much of the network infrastructure security is managed by Microsoft.

• Support for Azure Private Link for secure, private connections to Microsoft Fabric is an evolving area, aiming to provide similar network isolation capabilities as PaaS (Platform as a Service) services.
• The On-Premises Data Gateway is used to securely access data sources that reside in your on-premises network.  

➥ Access Control:

• Microsoft Entra ID is used for user authentication and identity management.
• Permissions are primarily managed through workspace roles and item sharing as described above.  
• For the SQL endpoints of Lakehouses and Warehouses, you can also manage data access using familiar T-SQL GRANT and DENY statements, much like in SQL Server.

➥ Data Protection:

• Data stored in OneLake is encrypted at rest. By default, Microsoft manages the encryption keys, but there is preview support for using customer-managed keys (CMK) for greater control.  
• Data is also encrypted in transit.
• Sensitivity labels defined in Microsoft Purview can be applied to Microsoft Fabric items and data, and these labels can be enforced across the platform.  

➥ Built-in Microsoft Purview Governance: 

Microsoft Fabric is described as having "Purview built-in." In practice, this means:  

• Automated Data Discovery & Cataloging: When your Fabric tenant is scanned by Purview (or through its native integration), metadata from your Fabric items (datasets, reports, lakehouses, pipelines, etc.) is automatically captured and made available in the Purview Data Map and Unified Catalog.  
• Automatic Lineage Tracking: Microsoft Fabric automatically tracks data lineage across its various items. For example, it can show how data flows from a Dataflow Gen2, into a Lakehouse table, and then into a Power BI report. There are some current limitations, for instance, around cross-workspace lineage for non-Power BI items and lineage involving notebooks and pipelines.  
• Information Protection: Sensitivity labels that you define in Microsoft Purview are recognized within Microsoft Fabric and can be inherited or applied to your Fabric data and items.
• Microsoft Purview Hub within Fabric: Microsoft Fabric provides a centralized "Purview Hub" where users can get an overview of governance activities, data health, and compliance related to their Fabric assets.

➥ Centralized Administration:

The Microsoft Fabric admin portal is where administrators can manage tenant-level settings, capacities, workspaces, and various governance features. 

9) Azure Synapse vs Fabric — Pricing Model

We've made it to the last section. Now it's time to explore the differences between Azure Synapse and Fabric, particularly when it comes to costs.

Let’s cut straight to it: when you compare Azure Synapse vs Fabric—Pricing Model, the biggest cost drivers aren’t just sticker prices. They’re the patterns you deploy, how long your workloads run, and the storage you stack up.

Azure Synapse Pricing Model

Azure Synapse's pricing model splits costs across various components. This approach lets you tailor your spending to specific workload requirements, from big data analytics to pre-purchase savings.

Keep in mind that all prices here are estimates in US dollars for the US East 2 region and are quoted on a monthly basis. Actual pricing might vary based on your specific agreement, purchase timing, or regional and currency differences.

1) Pre-Purchase Plans: Synapse Commit Units (SCUs)

If you have predictable Azure Synapse consumption, pre-purchase plans can save you a good chunk of change. Azure Synapse Analytics Commit Units (SCUs) are blocks of consumption you buy upfront. You can use these SCUs across most Synapse services, excluding storage. When you commit to a certain usage level, you get tiered discounts compared to the standard pay-as-you-go rates.

Here are some of the pre-purchase pricing details:

Note: Purchased SCUs remain valid for 12 months. You consume them at each service's retail price until they run out or the term ends.

2) Data Integration Pricing: Pipelines and Data Flows

Azure Synapse Analytics offers robust data integration for building hybrid ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines. Data integration costs depend on a few factors.

a) Data Pipelines

Data Pipelines orchestrate and execute data movement and transformation. Pricing is based on activity runs and integration runtime hours.

b) Data Flows

Data Flows in Azure Synapse let you build complex data transformations visually and at scale. Pricing here is based on cluster execution and debugging time, billed per vCore-hour.

Note: Data Flows need a minimum cluster size of 8 vCores to run. Execution and debugging times are billed per minute and rounded up.

c) Operation Charges

Beyond just running pipelines, operations like creating, reading, updating, deleting, and monitoring Data Pipelines also add to your overall data integration cost.

Note: You get the first 1 million operations per month for free. After that, operations cost a fixed rate per 50,000 operations.

3) Data Warehousing

Azure Synapse Analytics offers two main paths for data warehousing: serverless and dedicated SQL pools. This flexibility helps you optimize costs and performance based on your specific workload.

a) Serverless SQL Pool

Serverless SQL pools let you query data directly in your Azure Data Lake Storage without needing to provision resources ahead of time. This pay-per-query model works well for ad-hoc analysis and data exploration.

Your cost is solely based on the amount of data each query processes. Data Definition Language (DDL) statements, which are just metadata operations, don't cost anything. There's a minimum charge of 10 MB per query, and data processed gets rounded up to the nearest 1 MB.

Note: This pricing applies only to querying data. Storage costs for Azure Data Lake Storage are billed separately.

b) Dedicated SQL Pool

Dedicated SQL pools, previously called SQL DW, provide reserved compute resources for intensive data warehousing workloads. They deliver high query performance and predictable scalability. You can choose pay-as-you-go or reserved capacity for these.

Dedicated SQL Pool Pay-as-you-go Pricing (Monthly)

DWUs, or Data Warehousing Units, measure the compute resources allocated to your Dedicated SQL pool. More Data Warehouse Units (DWUs) mean more compute power, suitable for demanding tasks. Dedicated SQL pools also include adaptive caching, which helps optimize performance for workloads with consistent compute needs.

Dedicated SQL Pool Reserved Capacity Pricing (Monthly)

c) Data Storage, Snapshots, Disaster Recovery, and Threat Detection for Dedicated SQL Pools

Beyond compute, Dedicated SQL Pools have other charges for data storage, disaster recovery, and security features.

• Data Storage & Snapshots: This includes the size of your data warehouse plus 7 days of incremental snapshots for protection and recovery. You pay only for the volume of data stored, not storage transactions.
• Geo-redundant Disaster Recovery: For business continuity, this feature replicates your data warehouse to a secondary region. It costs extra per GB per month for the geo-redundant storage.
• Azure Defender for SQL: For added security, Azure Defender for SQL offers threat detection. Its pricing aligns with the Azure Security Center Standard tier, billed per protected SQL Database server (node) per month. You can try it for 60 days free. See Microsoft Defender for Cloud pricing for more details.

4) Big Data Analytics Pricing: Apache Spark Pools

Azure Synapse Analytics includes Apache Spark pools for large-scale data processing like data engineering, data preparation, and machine learning. Spark pool usage is billed per vCore-hour.

Memory-optimized pools are generally good for everyday Apache Spark workloads. GPU-accelerated pools are built for computationally intensive tasks, especially in machine learning.

Spark pool usage is billed per minute, rounded up to the nearest minute.

5) Log and Telemetry Analytics (Azure Synapse Data Explorer)

Azure Synapse Data Explorer works great for interactive exploration of time-series, log, and telemetry data. Its architecture separates compute and storage, allowing for independent scaling and cost optimization.

Azure Synapse Data Explorer billing is rounded up to the nearest minute.

6) Azure Synapse Link

Azure Synapse Link connects operational data with analytics, helping you avoid time-consuming ETL processes. Here's how its pricing breaks down for SQL, Cosmos DB, and Dataverse.

a) Azure Synapse Link for SQL

Azure Synapse Link for SQL can move data from your SQL databases automatically, bypassing traditional ETL.

b) Azure Synapse Link for Cosmos DB

Pricing for Synapse Link for Cosmos DB relies on analytical storage transactions within Azure Cosmos DB. You'll need to check Azure Cosmos DB's pricing for full details.

c) Azure Synapse Link for Dataverse

Azure Synapse Link for Dataverse comes included with Microsoft Power Platform and certain Microsoft 365 licenses. It provides valuable analytical capabilities for users of these platforms. While the feature itself is free from a Dataverse perspective, any underlying Azure services it utilizes (like Azure Data Lake Storage Gen2 or Synapse Workspace compute) will still incur costs.  See licensing overviews for more details.

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Microsoft Fabric Pricing Model

Microsoft Fabric offers a unified analytics platform, and its pricing model simplifies things quite a bit. You can try Microsoft Fabric for free to explore its capabilities. Like Azure Synapse Analytics, prices are estimates and can change based on your agreement with Microsoft, purchase date, and currency exchange rates. Prices are primarily calculated in US dollars.

1) Capacity Pricing: The Core of Microsoft Fabric Costs

Microsoft Fabric uses a shared pool of compute capacity. This single pool supports all Fabric workloads, from data modeling to business intelligence.This capacity-based model simplifies purchasing, letting you use Fabric Capacity Units (CUs) flexibly without needing to pre-allocate them for individual services. This pooled approach can reduce costs by ensuring you avoid idle workloads, as different Fabric experiences can share the same underlying compute. You can also scale your capacity up or down as needed. A centralized dashboard helps monitor usage and costs.

Note: Microsoft Fabric costs scale roughly linearly with the number of CUs.The reservation option can offer a significant discount (around 40%) compared to pay-as-you-go rates, but you pay for the capacity regardless of actual usage. If you buy a reserved capacity, it applies as a discount to your Fabric capacity.

How Microsoft Fabric Workloads Consume Capacity?

Here's an important distinction: all Fabric experiences; be it Data Factory pipelines, Spark notebooks in Data Engineering, running SQL queries in a Data Warehouse, real-time analytics with KQL databases, or processing data in Data Science and Power BI; they all draw from this single pool of purchased Fabric Capacity Units. Microsoft Fabric meters this consumption in "Capacity Unit Seconds" (CU(s)). So, your bill reflects how many CUs were busy for how many seconds. Even if your capacity is set at F2 (2 CUs), a query can briefly consume more CUs, using up your available CU(s) faster.

2) OneLake Storage: The Single Data Store

OneLake acts as a centralized storage solution for all your data within Microsoft Fabric. It simplifies purchasing by automatically provisioning storage. A key advantage is that all analytical engines in Microsoft Fabric can access a single copy of your data, cutting down on data movement or duplication. It also integrates with existing third-party storage systems and uses open data formats, making data accessible to various analytical tools.

Storage Pricing:

Note: If you delete a workspace, you'll still be charged for its OneLake storage during a retention period, which you can set from 7 to 90 days. Additionally, while writing data to OneLake is typically free, accessing this data outside Microsoft Fabric or moving it to another platform can incur network egress charges.

3) Mirroring: Near Real-Time Data Replication

Mirroring lets you replicate operational databases directly into OneLake in near real-time, helping to avoid complex ETL processes. When you use mirroring, you get free storage for these replicas up to a certain limit, based on your purchased compute capacity SKU.

Free Mirroring Storage Limits:

Note: This free mirroring storage only applies to purchased capacities, not free trials. If you pause your Fabric capacity, you'll be charged for the mirrored data's storage based on standard OneLake pricing.

4) Power BI Licensing

Microsoft Fabric's compute capacity handles much of the heavy lifting for Power BI, like data model processing and report rendering. However, you might still need Power BI user licenses for content creation and consumption in certain situations.

For Fabric capacities below F64 (meaning F2 to F32), users who create or view shared content generally need a Power BI Pro (around $10 per user per month) or Premium Per User (PPU, around $20 per user per month) license. Once you have a larger capacity, like F64 or higher (which is equivalent to a Power BI Premium P1 capacity), Power BI Premium features kick in. At this scale, report consumers usually no longer need individual Pro licenses; only report authors or developers would. This means that for a large number of users, investing in a bigger Fabric SKU might be more cost-effective than buying many individual licenses.

Azure Synapse vs Fabric—Pros & Cons

Let's boil it down to the good and the not-so-good for each.

Azure Synapse Pros

• Azure Synapse has been around since 2019 and its Dedicated SQL Pools (formerly SQL DW) power many large-scale data warehouses in production today.
• Azure Synapse gives you direct control over compute and network settings, letting you fine-tune VM sizes, DWUs and virtual network links for custom security or performance needs.
• Azure Synapse provides optimized, purpose-built engines – MPP for warehousing, Spark pools for big data, Serverless SQL for ad hoc queries, and Data Explorer for time-series logs.
• Azure Synapse’s pay-per-use Serverless SQL Pools and the ability to pause Dedicated SQL Pools help you cut costs when demand is low.
• Azure Synapse Pipelines build on Azure Data Factory’s connectors, so you can move data from hundreds of sources – S3, SAP, Oracle or SaaS apps – without extra plugins.
• Azure Synapse Analytics provides robust T-SQL support in both its dedicated and serverless SQL pools, which is a big plus for SQL-savvy teams.

Azure Synapse Cons

• Azure Synapse Analytics forces you to juggle multiple compute contexts (SQL DW, Serverless SQL, Spark, Data Explorer) plus separate storage accounts, which can get complex.
• Azure Synapse Analytics users often move or copy data between engines – for example, from Data Lake Storage into Dedicated SQL – adding latency and admin overhead.
• Azure Synapse Analytics isn’t plug-and-play; you need solid Azure skills to set up networking, managed identities and resource limits without surprises.
• Azure Synapse Analytics leaves you managing storage links yourself; there’s no single logical lakehouse abstraction.
• Azure Synapse Analytics’s cost controls require active monitoring of paused pools and usage across services; bills can spike if you lose track.
• Azure Synapse Analytics notebooks and pipelines don’t migrate to Microsoft Fabric or other platforms without refactoring widget code or pipeline definitions.
• Azure Synapse Analytics lacks a built-in AI assistant or collaborative workspace; you won’t find a Copilot or shared Git integration out of the box.
• Azure Synapse Analytics’s update cadence trails Microsoft Fabric’s rapid rollouts, since Microsoft funnels new analytics features into Microsoft Fabric first.

Microsoft Fabric Pros

• Microsoft Fabric unifies lakehouse, data engineering, integration, BI and AI into one SaaS portal; no separate services to stitch together.
• Microsoft Fabric uses OneLake as a single logical data lake that you don’t manage; it handles storage provisioned on ADLS Gen2 under the covers.
• Microsoft Fabric adopts a unified capacity model: you buy CUs (Fabric Capacity Units) once and all workloads – warehouse, lakehouse, Spark, pipelines – draw from them.
• Microsoft Fabric embeds Power BI as a first-class citizen; Direct Lake mode delivers near-real-time dashboard performance on lakehouse data.
• Microsoft Fabric makes collaboration easy; analysts, engineers and data scientists share workspaces, notebooks, datasets and governance in one place.
• Microsoft Fabric surfaces Copilot AI assistants across notebooks, SQL, Power BI and pipelines, speeding up data prep and analysis tasks.
• Microsoft Fabric is Microsoft’s strategic focus for analytics; new features land here first, from AI-driven insights to enhanced security.

Microsoft Fabric Cons

• Microsoft Fabric launched in 2023, so some features still sit in preview or haven’t matched Synapse’s depth – look before you rely on new components.
• Microsoft Fabric’s SaaS (Software as a Service) nature hides infra details; you can’t tweak VM sizes or network peering for specialized workloads.
• Microsoft Fabric needs careful CU capacity planning; mixed workloads can hit throttling or drive up costs if you misjudge shared resources.
• Microsoft Fabric applies a “Fabric way” across tools; if you’re rooted in Azure Synapse Analytics or Data Factory patterns, you must rethink pipelines, notebooks and access models.
• Microsoft Fabric migration requires refactoring Synapse SQL scripts and pipelines to fit Microsoft Fabric’s APIs and governance, which can be a heavy lift.
• Microsoft Fabric’s connector ecosystem is growing fast but still trails Azure Data Factory’s 200+ connectors in some niche or on‑prem cases.

Conclusion

And that’s a wrap! So, what's the verdict on Azure Synapse vs. Fabric? It comes down to a choice between two options. Synapse is the more traditional platform - flexible, but also a bit disconnected. Microsoft Fabric, on the other hand, is a next-gen service - streamlined, but also pretty opinionated about how things should be done. What works best for you is determined on your specific needs. Do you want a lot of control or a seamless, integrated experience? Microsoft's analytics strategy is clear; for the time being, they're completely focused on Fabric. They're not just adding features; they're entirely changing the way analytics services function by merging the best of Synapse, Azure Data Factory, and Power BI into a single service.

In this article, we have covered:

• What is Azure Synapse Analytics?
• What is Microsoft Fabric?
• What Is the Difference Between Azure Synapse and Fabric?
  • Azure Synapse vs Fabric—Architecture
  • Azure Synapse vs Fabric—Data Storage Models
  • Azure Synapse vs Fabric—Compute Engine
  • Azure Synapse vs Fabric—Ecosystem 
  • Azure Synapse vs Fabric—Analytics Workloads
  • Azure Synapse vs Fabric—Real-Time Analytics
  • Azure Synapse vs Fabric—ML, AI & Copilot
  • Azure Synapse vs Fabric—Security & Gov
  • Azure Synapse vs Fabric—Pricing Model
• Azure Synapse vs Fabric — Pros & Cons

… and so much more!

FAQs

What exactly is Azure Synapse?

Azure Synapse Analytics is a PaaS (Platform as a Service) analytics solution that combines data warehousing, big data processing, data integration, and machine learning functions into a single connected platform. It provides dedicated and serverless compute options for handling large-scale analytical workloads.

Is Azure Synapse an ETL tool?

Not precisely. Synapse is a full analytics platform that includes ETL/ELT capabilities via its built-in pipelines (Azure Data Factory). You can build ETL processes in Azure Synapse Studio (copy activities, data flows, SQL transforms), but Synapse is more than ETL: it also offers warehousing, big data processing (Spark), and BI connection. In essence, Synapse contains ETL tools, but it isn’t only an ETL tool like standalone Azure Data Factory is.

Is Azure Synapse same as Databricks?

No. Databricks is a separate cloud service focused on Apache Spark. While Synapse has Spark pools (and Microsoft Fabric has a Spark workload), those are not the same environment as Azure Databricks. Databricks offers its own managed Spark runtime with different pricing and notebooks. Synapse and Fabric use Spark behind the scenes, but they include other engines (SQL, Kusto) and billing models. You might use Databricks side-by-side with Synapse/Fabric for certain Spark-heavy use cases, but they are distinct products.

Is Microsoft Fabric free?

Microsoft Fabric is not free outside its trial. Microsoft offers a 60-day free trial capacity for Fabric. The trial gives you 64 compute units (F64 capacity) and 1 TB of OneLake storage at no cost. After the trial, you must purchase Fabric capacity (CUs) and pay for storage.

Is Microsoft Fabric designed to replace Azure Synapse Analytics?

No, not at this time. Microsoft has stated that Synapse will continue to be supported. Microsoft Fabric is a new offering that overlaps much of Synapse’s functions, but there is no current mandate to retire Synapse. Many Synapse features (especially specialized or older ones) are not yet in Microsoft Fabric. You can use both in parallel. Over time, new projects may favor Fabric’s unified model, but existing Synapse investments remain valid. TL:DR: Microsoft Fabric does not automatically replace Synapse; it’s another option.

Can Synapse pipelines be migrated to Microsoft Fabric?

Yes, but with caveats. Microsoft Fabric’s pipelines (Data Factory in Microsoft Fabric) are very similar to Synapse pipelines, so data movement and simple transformations can move by recreating the pipelines in Fabric. Microsoft provides migration guidance and tools for moving copy activities, notebook/Spark job activities, and more. However, some features cannot move directly: for example, Azure Data Factory Mapping Data Flows and SSIS packages don’t run in Microsoft Fabric. The recommended approach is to leave those in Synapse/Azure Data Factory and invoke them from Fabric via the Execute Pipeline activity. In practice, migration is mostly manual: you export/import JSON definitions or rebuild them. But most connectors and simple tasks should work similarly.

Which has better real-time analytics support?

For ease of real-time analytics, Microsoft Fabric has the edge due to its built-in Real-Time Intelligence service. Microsoft Fabric offers an end-to-end streaming solution (Azure Data Explorer + visual dashboards + triggers) in one place. Synapse can handle real-time data via Azure Data Explorer or Spark streaming, but it needs connecting additional Azure services. For heavy-duty streaming volumes, Synapse/Azure Data Explorer might scale higher, but Microsoft Fabric’s approach is quicker to adopt for most use cases. In summary, Fabric’s Real Time Intelligence makes streaming analytics more accessible, while Synapse gives more low-level control.

Does Microsoft Fabric support all the data connectors and integration options available in Synapse?

Yes. Microsoft Fabric Data Factory supports nearly all the same connectors that Synapse/Azure Data Factory does for core Azure services. For example, you can connect to Azure Blob, ADLS Gen2, Azure SQL Database, Synapse Analytics, Cosmos DB, and many SaaS (Software as a Service) services in Fabric pipelines, just as in Synapse pipelines. Microsoft’s connector parity table shows most connectors (Blob, SQL, Cosmos, etc.) are in Fabric. A few connectors were missing at launch (for instance, Databricks Delta Lake or Google BigQuery), but Microsoft Fabric is gradually adding more. So if your data source was supported in Synapse, it’s very likely supported in Microsoft Fabric too.