Azure Synapse Analytics is described as the former Azure SQL Data Warehouse, evolved, and as a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives users the freedom to query data using either serverless or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
$4,700
per month 5000 Synapse Commit Units (SCUs)
Microsoft SQL Server
Score 8.7 out of 10
N/A
Microsoft SQL Server is a relational database.
$1,418
Per License
Pricing
Azure Synapse Analytics
Microsoft SQL Server
Editions & Modules
Tier 1
$4,700
per month 5,000 Synapse Commit Units (SCUs)
Tier 2
$9,200
per month 10,000 Synapse Commit Units (SCUs)
Tier 3
$21,360
per month 24,000 Synapse Commit Units (SCUs)
Tier 4
$50,400
per month 60,000 Synapse Commit Units (SCUs)
Tier 5
$117,000
per month 150,000 Synapse Commit Units (SCUs)
Tier 6
$259,200
per month 360,000 Synapse Commit Units (SCUs)
Subscription
$1,418.00
Per License
Enterprise
$13,748.00
Per License
Offerings
Pricing Offerings
Azure Synapse Analytics
Microsoft SQL Server
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Azure Synapse Analytics
Microsoft SQL Server
Considered Both Products
Azure Synapse Analytics
Verified User
Contributor
Chose Azure Synapse Analytics
SQL Data Warehousing is much easier to manage if you already have SQL Server experience and analysts who are familiar with its interface. We are currently piloting using NoSQL and Hadoop type databases but it is difficult to get set up properly. Additionally, we have to …
When talking about structured storage, the big three currently are SQL Server, MySQL and Oracle. You can also toss in PostgreSQL into the mix. From a straight forward relational storage stand point, any of these tools will work, and work well. However, SQL Server is superior in …
It's well suited for large, fastly growing, and frequently changing data warehouses (e.g., in startups). It's also suited for companies that want a single, relatively easy-to-use, centralized cloud service for all their data needs. Larger, more structured organizations could still benefit from this service by using Synapse Dedicated SQL Pools, knowing that costs will be much higher than other solutions. I think this product is not suited for smaller, simpler workloads (where an Azure SQL Database and a Data Factory could be enough) or very large scenarios, where it may be better to build custom infrastructure.
Microsoft SQL is ubiquitous, while MySQL runs under the hood all over the place. Microsoft SQL is the platform taught in colleges and certification courses and is the one most likely to be used by businesses because it is backed by Microsoft. Its interface is friendly (well, as pleasant as SQL can be) and has been used by so many for so long that resources are freely available if you encounter any issues.
Quick to return data. Queries in a SQL data warehouse architecture tend to return data much more quickly than a OLTP setup. Especially with columnar indexes.
Ability to manage extremely large SQL tables. Our databases contain billions of records. This would be unwieldy without a proper SQL datawarehouse
Backup and replication. Because we're already using SQL, moving the data to a datawarehouse makes it easier to manage as our users are already familiar with SQL.
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
Microsoft SQL Server Enterprise edition has a high cost but is the only edition which supports SQL Always On Availability Groups. It would be nice to include this feature in the Standard version.
Licensing of Microsoft SQL Server is a quite complex matter, it would be good to simplify licensing in the future. For example, per core vs per user CAL licensing, as well as complex licensing scenarios in the Cloud and on Edge locations.
It would be good to include native tools for converting Oracle, DB2, Postgresql and MySQL/MariaDB databases (schema and data) for import into Microsoft SQL Server.
We understand that the Microsoft SQL Server will continue to advance, offering the same robust and reliable platform while adding new features that enable us, as a software center, to create a superior product. That provides excellent performance while reducing the hardware requirements and the total cost of ownership of our solution.
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.
SQL Server mostly 'just works' or generates error messages to help you sort out the trouble. You can usually count on the product to get the job done and keep an eye on your potential mistakes. Interaction with other Microsoft products makes operating as a Windows user pretty straight forward. Digging through the multitude of dialogs and wizards can be a pain, but the answer is usually there somewhere.
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.
We managed to handle most of our problems by looking into Microsoft's official documentation that has everything explained and almost every function has an example that illustrates in detail how a particular functionality works. Just like PowerShell has the ability to show you an example of how some cmdlet works, that is the case also here, and in my opinion, it is a very good practice and I like it.
Other than SQL taking quite a bit of time to actually install there are no problems with installation. Even on hardware that has good performance SQL can still take close to an hour to install a typical server with management and reporting services.
In comparing Azure Synapse to the Google BigQuery - the biggest highlight that I'd like to bring forward is Azure Synapse SQL leverages a scale-out architecture in order to distribute computational processing of data across multiple nodes whereas Google BigQuery only takes into account computation and storage.
[Microsoft] SQL Server has a much better community and professional support and is overall just a more reliable system with Microsoft behind it. I've used MySQL in the past and SQL Server has just become more comfortable for me and is my go to RDBMS.
Licensing fees is replaced with Azure subscription fee. No big saving there
More visibility into the Azure usage and cost
It can be used a hot storage and old data can be archived to data lake. Real time data integration is possible via external tables and Microsoft Power BI
Increased accuracy - We went from multiple users having different versions of an Excel spreadsheet to a single source of truth for our reporting.
Increased Efficiency - We can now generate reports at any time from a single source rather than multiple users spending their time collating data and generating reports.
Improved Security - Enterprise level security on a dedicated server rather than financial files on multiple laptop hard drives.
