Azure Synapse Analytics vs. Azure SQL Database

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

Your upcoming app can be built faster on a fully managed SQL database and can be moved into Azure with a few to no application code changes. Flexible and responsive server less computing and Hyperscale storage can cope with your changing requirements and one of the main benefits is the reduction in costs, which is noticeable.

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

• Scalability is #1: if it used to be an almost no-win endeavour to try to modernize your server or migrate to other hardware, with Azure SQL Database it becomes a press of a button.
• All the tools simply work after you are on Azure SQL Database.
• The applications do not need changes in order to start using Azure SQL Database.
• Hybrid Cloud scenarios will work.
• Clustering and failover - already there.
• You can start monitoring the use and extract performance insights in a new way in Azure.

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

• A little slow on processing complex or large Views. We use a lot of Views to feed our BI system, and the processing time could see some improvement, IMHO.
• Additional monitoring components would be nice too, automating some built in performance measurement tools would be a nice feature.
• Price can always be improved as well. It’s not bad, but room for improvement.

Incentivized

This is best solution as a DBA one could expect from a service provider and as a cloud service, it removes all your hassles.

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

It just works!

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

We give the support a high rating simply because every time we've had issues or questions, representatives were in contact with us quickly. Without fail, our issues/questions were handled in a timely matter. That kind of response is integral when client data integrity and availability is in question. There is also a wealth of documentation for resolving issues on your own.

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

Oracle Database is "the" serious database. There really is no competition in that field. SQL Database would be a serious competitor through the ease of implementation and the "no maintenance," but since it's too expensive for "normal" use (medium to small applications), it just priced itself out of the market, so to speak. Nevertheless, we do have 2 or 3 large applications that are highly integrated in azure, and for those it's just too easy to use SQL Database instead of the on premise Oracle Database with VPN gateways etcetera.

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

• We don't need a dedicated SQL dba because so many of the database maintenance operations are managed. A huge positive not only in budget but time constraints.
• The ability to scale quickly is the biggest positive as our data needs change constantly.
• Easy to migrate from legacy tools and systems, saving us on the need for redevelopment.

Incentivized