Microsoft's Blob Storage system on Azure is designed to make unstructured data available to customers anywhere through REST-based object storage.
$0.01
per GB/per month
Azure Synapse Analytics
Score 6.9 out of 10
N/A
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 5,000 Synapse Commit Units (SCUs)
Pricing
Azure Blob Storage
Azure Synapse Analytics
Editions & Modules
Block Blobs
$0.0081
per GB/per month
Azure Data Lake Storage
$0.0081
per GB/per month
Files
$0.058
per GB/per month
Managed Discs
$1.54
per month
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)
Offerings
Pricing Offerings
Azure Blob Storage
Azure Synapse Analytics
Free Trial
Yes
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 Blob Storage
Azure Synapse Analytics
Considered Both Products
Azure Blob Storage
Verified User
Anonymous
Chose Azure Blob Storage
Blob storage lets us control the file source/hosting and retain everything within our Microsoft ecosystem. Blob is less feature-rich than some of the other products. Still, we consider it as a value-added product included within our environment, and alternative products are not …
Azure Blob Storage was used only because we were already using it for other projects, and it has a good reputation for being a reliable cloud provider. It also has widespread regional availability and allows for data replication. It can also be easily accessed via the API or by …
We didn't actually evaluate anything but Azure Blob Storage since it worked so well for backups and Audit logs and the price point was good enough for us.
Azure Blob Storage is the best choice to store files when the app runs in Azure. It also has some advantages over S3, like Shared Access Signatures, that make it easy to control access to files directly via a URL. Azure Blob Storage is very fast and we have not had any major …
S3 seemed to be just as functional as blob storage in our analysis. The only real difference is we already were on the Microsoft platform with 365, and it was an easy system to continue to learn. That was the only real deciding factor between the two that made any difference …
Ease of use and availability to actual usage makes it my first choice. I feel like IBM Cloud is where azure was 5 years ago in terms of implementation and ease of use. AWS has some great functionality but it almost feels like you need to learn how to work under the hood of a …
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, …
When client is already having or using Azure then it’s wise to go with Synapse rather than using Snowflake. We got a lot of help from Microsoft consultants and Microsoft partners while implementing our EDW via Synapse and support is easily available via Microsoft resources and …
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 …
Director, eCommerce Analytics and Digital Marketing
Chose Azure Synapse Analytics
Azure Synapse Analytics stacks up well against the competitors I mentioned above. Technically, Azure SQL Datawarehouse is an upgraded version of the Azure SQL Database. So, the choice to move from one to the other depends on the processing needs of your company. If you need …
We also looked at Oracle Data Warehouse as part of our short list of products to implement as a solution. Oracle's product turned out to have less support by way of easily accessible internet blogs. Oracle was also considerably more expensive and we would have needed to hire …
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 …
Synapse, in comparison has its ups and downs against the competitors. However, where it excels, and builds it's markets is the cheaper costs (compared to Redshift), low code platforms and an in house solution that does not need you to leave the Synapse workspace for end to end …
Databricks is a complete product with new features constantly coming out. This can be both good or bad, with a lot of innovation comes a responsibility to keep your code and pipelines fresh.
Our team evaluated multiple platform as I mentioned above , but we stacks up Azure Synapse Analytics because : 1. Easy UI and Unified platform advantage 2. Tight integrations with MS ecosystem.
Azure Blob Storage is well suited for cases where you are working with different data formats and looking for cost-effective storage solutions based on access frequency. Another area of strength is the encryption of data at rest, and encryption can be managed on your own. However, it may not be appropriate for transferring large data very fast.
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.
Reduced cost. We were able to reduce our storage infrastructure by several hundred terabytes by consolidating redundant copies and dedupe/compression on files.
Extremely high level of redundancy. We can replicate data in a variety of ways that we would never have been capable of on our own hardware.
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
If we are transferring huge amount of data (outbound), it can get quite expensive.
With new features being added constantly, although a good thing, at times it becomes difficult to keep up with the changes. Documentation needs to keep UpToDate and should include best practices.
Performance can be improved especially when it comes to cold storage.
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
Blob storage is fairly simple, with several different options/settings that can be configured. The file explorer has enhanced its usability. Some areas could be improved, such as providing more details or stats on how many times a file has been accessed. It is an obvious choice if you're already using Azure/Entra.
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.
Documentation sometimes appears to be out of date or not fully documented properly with new releases. It is like documentation comes out for a specific version and is quickly out of date. Another issue is documentation is scarce on new releases and only seems to get properly updated (and sometimes is still wrong) once enough people hit the forums to complain.
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.
Blob storage lets us control the file source/hosting and retain everything within our Microsoft ecosystem. Blob is less feature-rich than some of the other products. Still, we consider it as a value-added product included within our environment, and alternative products are not needed for our requirements. Keeping the hosting within our tenant locale is vital to us.
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.
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.
