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.
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.
