Azure Synapse Analytics vs. Microsoft Fabric

In terms of a well-suited scenario - the Azure Synapse can be used to capture data from multiple sources (especially from onPrem sources apart from Dataverse) and update the transformed data based on the given conditions (eg: refresh data based on the specified date/time ranges). Also, the transformed data can simply be transferred to Azure Data Lake for further processing by utilizing other analytics tools such as PowerBI.

Incentivized

I would highly recommend Microsoft Fabric, especially for medium to large enterprises aiming to build a robust, scalable, and secure data analytics platform. It effectively unifies various data workloads, streamlining data integration, engineering, and particularly enhancing our ability to create and share reliable Power BI dashboards. The deep integration with Azure AD for features like Row-Level Security is a significant advantage for data governance.

Incentivized

• The combination of SQL/unstructured data
• Keeping things "complicated, but simple"; [heterogeneous] data formats seen as just SQL tables to business experts used to use Power BI, Excel, and any other traditional SQL-oriented BI tools
• Integration options using "Synapse pipelines", the application of ADFs
• The greatly integrated solution of independent things (Spark MPP cluster, MPP SQL Servers, ADFs) - all sitting under one roof. Great job!
• Integration with super-fast, globally replicated data. I really appreciate the integration of NoSQL databases (namely Core API and Mongo API under Cosmos DB) with purely batch-processed BI data

Incentivized

• Access Control / Security & Governance
• Collaborative Work up to Visualization
• Data Ingestion

Incentivized

• With Azure, it's always the same issue, too many moving parts doing similar things with no specialisation. ADF, Fabric Data Factory and Synapse pipeline serve the same purpose. Same goes for Fabric Warehouse and Synapse SQL pools.
• Could do better with serverless workloads considering the competition from databricks and its own fabric warehouse
• Synapse pipelines is a replica of Azure Data Factory with no tight integration with Synapse and to a surprise, with missing features from ADF. Integration of warehouse can be improved with in environment ETl tools

Incentivized

• Clarity and Documentation on Power BI Integration Changes and Best Practices
• More Flexible and Advanced Scheduled Report Delivery Options
• Enhanced Data Export Control Based on Sensitivity Labels

Incentivized

The data warehouse portion is very much like old style on-prem SQL server, so most SQL skills one has mastered carry over easily. Azure Data Factory has an easy drag and drop system which allows quick building of pipelines with minimal coding. The Spark portion is the only really complex portion, but if there's an in-house python expert, then the Spark portion is also quiet useable.

Incentivized

I've rated Microsoft Fabric's overall usability as a 4, primarily due to its extensive and multifaceted feature set, which can make it challenging to navigate and determine the optimal functionality for a given task.While the breadth of capabilities is a core strength for large enterprises, it often leads to a sense of being "lost" or overwhelmed for teams like ours that do not have highly formalized roles or dedicated specialists for each Fabric "experience" (e.g., Data Engineering, Data Warehousing, Data Science).

Incentivized

Microsoft does its best to support Synapse. More and more articles are being added to the documentation, providing more useful information on best utilizing its features. The examples provided work well for basic knowledge, but more complex examples should be added to further assist in discovering the vast abilities that the system has.

Incentivized

They're all part of the Microsoft Azure family, so they are not exactly competitors. They overlap in functionality, but they're targeted at different levels of customers. Azure Data Factory is an excellent stand-alone PaaS (included in Synapse Analytics) for writing, scheduling, and monitoring pipelines. Azure SQL Database (and all the Azure SQL family) is excellent for traditional, SQL-based data warehouses, especially if you're migrating from on-premises. Combined with Azure Data Factory (that can run SSIS packages), it's a perfect solution for a simple path to the cloud. Azure Databricks is effectively the only internal "competitor" to Synapse Analytics but targeted more to a "platform-agnostic" audience. On the other hand, Synapse is more of a proprietary mix of products that are more tightly related to Microsoft technologies.

Incentivized

Microsoft Fabric integrates data ingestion, engineering, warehousing, and Power BI visualization into one cohesive environment. This "one-stop shop" approach dramatically reduces complexity, minimizes operational overhead, and eliminates the need to integrate disparate tools and manage data across multiple systems. It provides superior scalability for large datasets, supports open data formats, and offers a much broader suite of data engineering and data science capabilities.In essence, Fabric's integrated ecosystem and streamlined operational management were key differentiators, providing a more cohesive, scalable, and efficient solution for our evolving data strategy than combining specialized tools.

Incentivized

• It definitely has a positive impact on ROI. We are able to use it to generate MORE revenue through predictive analytics and pricing optimization.
• Because of the SQL Data Warehouse design, we're able to set up some self service reporting tools which allow our users to generate reports ad hoc instead of having a full time employee creating these by hand.
• Having visibility into the data is very useful for management to make good business decisions.

Incentivized

• Transformation from Monthly Reports to Daily Insights, Accelerating Decision-Making
• Streamlined Reporting: Enhanced Data Consistency and 30% Reduction in Reports Provided
• Empowered Self-Service Analytics Through Elevated Employee Data Literacy

Incentivized