Azure vs Fabric: A Comprehensive Comparison Guide

Azure and Fabric are two popular cloud computing platforms that offer a range of services and tools for building, deploying, and managing applications. Azure is a comprehensive cloud platform developed by Microsoft, while Fabric is a cloud-based platform for building and managing applications.

Azure offers a wide range of services, including computing, storage, networking, and database services, as well as artificial intelligence and machine learning capabilities. Fabric, on the other hand, provides a platform for building and managing applications, with a focus on ease of use and rapid development.

One of the key differences between Azure and Fabric is their pricing models. Azure offers a pay-as-you-go pricing model, where users only pay for the resources they use, while Fabric offers a fixed pricing model, where users pay a flat fee per application.

Microsoft Fabric is an end-to-end SaaS offering that brings together several data and analytics workloads under one roof. These workloads include Data Factory, Synapse Data Warehouse, Synapse Data Engineering, Synapse Data Science, Synapse Real-Time Analytics, Power BI, and Data Activator.

Fabric is built on an open, lake-centric design called OneLake, which offers full-service capabilities including data movement, data lakes, data engineering, data integration, data science, real-time analytics, and business intelligence.

Azure Synapse Analytics, on the other hand, is a PaaS for enterprise data warehousing, integration, and analytics, built on top of ADLS (Azure Data Lake Storage) Gen2, which can be seen as a precursor to OneLake.

Significant gaps exist between Microsoft Fabric and Azure Synapse Analytics. One of the most notable gaps is the lack of a direct or automatic upgrade path from Synapse to Fabric. You'll need to manually migrate and modify code such as notebooks, SQL scripts, and pipelines to enable it to run on Fabric.

Some features that aren't supported in Fabric include Mapping Data Flows, which will be replaced by Dataflows Gen 2 (Power Query). This might be a significant barrier to migration for organizations that have invested heavily in the low-code tooling for data wrangling.

The OPENROWSET syntax, which enables SQL queries to directly address structured data files in the lake, is also not supported in Fabric. However, Fabric provides similar functionality through the "Default Warehouse" feature, which can be leveraged in a similar manner to "SQL Serverless" on Synapse.

Here are some specific limitations of Fabric compared to Synapse:

These gaps might require significant effort to migrate from Synapse to Fabric, especially if you've invested heavily in features like Mapping Data Flows or OPENROWSET syntax.

Improved Integration is a key area where Fabric shines. It offers out-of-the-box integration with Azure resources, saving time and effort.

With Fabric, you don't need to create an instance of Azure Machine Learning to register your machine learning models and log experiments. Fabric provides an MLFlow endpoint by default, making it a convenient solution.

Fabric's integration with Power BI is also noteworthy. While there was some integration between Synapse and Power BI, Fabric takes it to the next level. Datasets can be created with ease directly on top of the lakehouse, and models and measures can be created from within the Fabric UX.

This streamlined process is a big step forward, allowing data engineers to work more efficiently. They can transform raw data into analytical data without needing to leave Fabric, making it a more natural workflow.

Here are some key benefits of Fabric's integration with Power BI:

Overall, Fabric's improved integration with Azure resources and Power BI makes it a more attractive option for those looking to simplify their data engineering workflow.

Microsoft Fabric and Azure Synapse Analytics are both powerful tools for data management and analytics, but they have distinct differences.

Microsoft Fabric is a SaaS offering that brings together several data and analytics workloads under one roof, including Data Factory, Synapse Data Warehouse, and Power BI.

Fabric is built on an open, lake-centric design called OneLake, which allows for a streamlined solution that's easy to connect, onboard, and operate.

One key difference between Fabric and Synapse is in data storage. Fabric doesn't have a dedicated SQL pool or relational storage, instead using delta lake format within OneLake.

Synapse, on the other hand, is built on top of ADLS (Azure Data Lake Storage) Gen2.

Here's a summary of the key differences in architecture:

Fabric and Synapse also have different workspaces. Fabric uses a Power BI-based interface organized around personas, while Synapse uses Synapse Studio.

The features of Fabric and Synapse also have some differences, including the lack of support for SQL Endpoint functions like OPENROWSET in Fabric, and the replacement of Mapping Data Flows with the Power Query experience in Synapse.

Fabric provides an MLFlow endpoint by default, making it easier to set up machine learning models, while Synapse requires the creation of an instance of Azure Machine Learning.

In terms of costs, Fabric will be charged on a capacity base, while Synapse uses a pay-per-query model for the Serverless SQL pool.

It's worth noting that Fabric is still under development, and the official pricing list has not been released yet.

Ultimately, the choice between Fabric and Synapse will depend on your specific needs and use case. If you're looking for a broad data management solution, Fabric may be the better choice, while Synapse is a more focused tool for analytics.

Microsoft Fabric is designed to improve the developer experience, with features like faster Spark infrastructure setup, enhanced collaboration tools, and local development capabilities in VS Code. This is a significant shift from Synapse Studio, which Fabric abandons in favor of a new UX that builds on the Power BI experience.

The new UX organizes assets around workspaces, but this can be a challenge for those accustomed to Synapse Studio. Microsoft has made significant improvements to developer experience during the private preview, and they're likely to continue taking feedback to make the experience as productive as possible.

Fabric's time-to-spin-up Spark infrastructure is significantly faster, taking around 20-30 seconds compared to 3-4 minutes on Synapse. This is a notable improvement for developers who need to set up infrastructure quickly.

The costs of Fabric vs Synapse Analytics are still under the clouds, but it seems that Fabric will be charged on a capacity base. This means companies pay a certain amount of money for a dedicated set of resources. While this might make sense for large organizations, small-medium size companies might find it less appealing, especially considering the pay-per-query model of the Serverless SQL pool in Synapse.

Here's a rough estimate of the costs:

Keep in mind that these are rough estimates and need to be confirmed. It's essential to do a detailed cost analysis to determine which option makes more sense for your business.

Migrating your workloads from Synapse to Fabric requires careful consideration. There isn't a direct or automatic upgrade path, so you'll need to manually modify your code, including notebooks and pipelines.

You'll encounter three common limitations in Fabric: limited T-SQL support, limited data type support, and limitations on updates to Warehouse tables. These limitations will require adjustments to your existing queries and data types.

To prepare for migration, it's essential to understand what Synapse workloads are ripe for migration. Organisations with predominantly Synapse Spark workloads and using it to project data for analysis in Power BI will likely find the migration process more straightforward.

If you're looking to migrate Synapse workloads, Onelake can streamline the process by allowing organisations to migrate workloads without the underlying data.

Onelake shortcuts the migration process by giving organisations this option.

Organisations with predominantly Synapse Spark workloads will likely find migration to Microsoft Fabric more straightforward.

These workloads are often used to project data for analysis in Power BI, making the migration process even more efficient.

There isn't a direct or automatic upgrade path from Azure Synapse Analytics to Microsoft Fabric, so you'll need to consider it as a manual migration involving code modifications.

This means you'll need to adjust your T-SQL commands, data types, and updates to Warehouse tables, as Fabric has limitations in these areas.

You'll need to modify your code, including notebooks and pipelines, to work with Fabric's features.

Fabric doesn't support OPENROWSET() function, which means you'll need to adjust your queries that contain OPENROWSET syntax.

Synapse Link, a feature that allows querying data from Cosmos DB and/or Microsoft Dataverse, is not available in Fabric yet.

Synapse Studio, a web-based user interface for managing and developing Synapse stuff, is not supported in Fabric and has been replaced by workspaces.

Here are some key limitations to consider when migrating from Synapse to Fabric:

Onelake shortcuts can help streamline the migration process by allowing you to migrate Synapse workloads without the underlying data.

Azure Synapse Analytics and Microsoft Fabric are both powerful tools for data warehousing and analytics, but they have some key differences. Microsoft Fabric is an end-to-end SaaS offering that brings together several data and analytics workloads under one roof.

Fabric offers full-service capabilities including data movement, data lakes, data engineering, data integration, data science, real-time analytics, and business intelligence. It's built on an open, lake-centric design called OneLake, which stores warehouse data in delta lake format.

Azure Synapse Analytics, on the other hand, is a PaaS for enterprise data warehousing, integration, and analytics. It was launched as a one-stop shop for all warehousing and analytics workloads and includes several tools bundled together in a platform called Synapse Studio.

One of the main differences between Fabric and Synapse is their data storage approach. Fabric stores data in OneLake, while Synapse uses ADLS (Azure Data Lake Storage) Gen2.

Here's a brief comparison of the two:

Fabric also offers a more streamlined solution for data security, governance, and compliance, backed by a shared platform. This can make it easier to connect, onboard, and operate compared to Synapse.

Hive Metastore (HMS) plays a crucial role in workload migration and analytics.

Both Azure Synapse Spark and Fabric Spark support internal HMS.

Fabric Spark also supports internal HMS in lakehouse mode.

However, there's a key difference when it comes to external HMS: Azure Synapse Spark supports it, while Fabric Spark does not.

This limitation is due to the fact that Fabric Spark doesn't currently support a Catalog API or access to an external HMS.

If you're looking to migrate Azure Synapse Spark catalog HMS metadata to Fabric, you'll need to take this into consideration.

Here's a quick comparison of HMS types:

Spark and Hive Configurations can be a bit tricky, but understanding the basics is crucial for a smooth experience. Azure and Fabric both support Spark and Hive, but the configurations differ.

In Azure, Spark configurations can be managed through the Azure portal, where you can set up Spark clusters with various node sizes and storage options. This makes it easy to scale up or down as needed.

Fabric, on the other hand, uses a more traditional approach to Spark configuration, requiring manual setup of Spark clusters through the command line. This can be more time-consuming, but gives users more control over the configuration process.

Spark Pool is a popular mining pool for Spark, offering a user-friendly interface and competitive rewards. It's a great option for those new to mining.

Spark Pool's fee is 0.86% of the total block reward, which is relatively low compared to other pools. Spark Pool's fee structure is transparent and easy to understand.

Spark Pool supports multiple mining algorithms, including Ethash and POW. This makes it accessible to a wide range of miners. Spark Pool's algorithm support allows for flexibility and adaptability.

Spark Pool has a strong focus on security, with features like multi-signature wallets and regular security audits. This gives users peace of mind and confidence in the platform.

Spark configurations can be applied at different levels: environment level, inline level, and import/export. You can also use API/SDK support for Spark configurations.

You can define multiple Spark configurations at the environment level, which can be assigned to different Spark pools in Azure Synapse. In Fabric, you can use environments for this purpose.

In Azure Synapse, notebooks and Spark jobs both support attaching different Spark configurations inline. You can customize session level configurations in Fabric using the spark.conf.set() setting.

Spark configurations can be imported and exported in Fabric environments. This is a useful feature for managing Spark configurations.

Here's a comparison of Spark configurations in Azure Synapse Spark and Fabric:

Spark libraries can be applied at different levels in Azure Synapse, including workspace, environment, and inline levels. You can't upload/install libraries to a workspace and later assign them to a specific Spark pool.

You can upload/install libraries to an environment, making them available to all notebooks and Spark job definitions running in the environment. Environment-level libraries are a convenient way to share libraries across multiple notebooks and jobs.

Spark libraries can also be specified inline, for example, at the beginning of a notebook session. This allows you to use a library for a specific session without having to upload it to an environment.

Azure Synapse Spark supports libraries at all three levels: workspace, environment, and inline. Fabric Spark also supports inline libraries, but not workspace-level libraries.

Here's a summary of Spark library support across Azure Synapse and Fabric:

Spark job definitions are a crucial part of Spark and Hive configurations. You can bring your .py/.R/jar files to create a Spark job definition.

Spark job capabilities vary between Azure Synapse Spark and Fabric Spark. Here are the key differences:

Fabric supports scheduled runs for a Spark job definition, which is a convenient feature for users.