Azure Synapse Analytics Reviews & Insights

Score6.8 out of 10

52 Reviews and Ratings

Community insights

TrustRadius Insights for Azure Synapse Analytics are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

Convenience of Data Integration Tools: Users appreciate the ease of accessing various data integration tools within Azure Data Factory, including low-code DataFlows and full-code Spark in a centralized orchestrator. Code-Free ETL Work Option: The platform's code-free ETL work option simplifies the process of building, scheduling, and monitoring complex data pipelines according to users. AI Integration Functionality: Users find the AI integration seamlessly integrated into the platform, enhancing their data integration processes. Advantageous Data Pipeline Creation: Some users have found creating data pipelines that connect multiple workspaces and external sources beneficial. OnPrem Data Capture Management: Users value the capability to manage connections and create runtimes for onPrem data capture. Efficient Integrated Solution: The efficiency of combining components like Spark MPP cluster, MPP SQL Servers, and ADFs under one roof is highly praised by users.

Azure Synapse Analytics Reviews

2 Reviews

Engineering

Synapse Analytics The go to solution for medium scaled warehousing

Rating: 6 out of 10

Incentivized

August 18, 2025

Use Cases and Deployment Scope

We usually deal with large scale data migrations. Synapse, at times, fits in perfectly with a fabric lakehouse-warehouse solution or a standard data warehousing solution bringing and collating data from multiple data sources into a data arehouse in the form of Synapse. While there are multiple trends in the data space involving lakehouses and delta lake and what not, Synapse still holds its place best when warehouses are talked about. With flexibility of external tables and serverless workloads for faster data reads, to the scalability of database tables with transactional and analytical use cases, Synapse can serve a wide array of use cases and rightfully so.

Pros

Data Warehousing
Data Engineering
Data Marts
Data Analytics

Cons

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

Likelihood to Recommend

Usually, there is a huge overlap between use cases that suit Synapse also suiting databricks, Fabric Warehouse etc. However, the best suited use cases for Synapse or those involving mostly ELT and data warehousing. For example, If you have data lying around in isolated databases, data that is clean but perhaps not curated, would serve as a perfect use case for Synapse to jump in and have the best suited solution. You could use a plethora of Synapse Pipelines' connectors to simply extract the data from these isolated Databases, load it into staging tables, apply basic refinement to push it into dbo tables and perform analytics on top of this. With intuitive UI and powerful dynamic expression, it's an accelerated metadata driven framework knocking at your door waiting to happen with just a few drag and drops and some metadata table magic.

Paras Jasal

Data Engineer in Engineering at Celebal Technologies (Computer Software, 1001-5000 employees)

Vetted Review

4 years of experience

View profile

Cloud data simplified: a great all-in-one tool for all your data analytics needs

Rating: 8 out of 10

Incentivized

December 20, 2022

Use Cases and Deployment Scope

Our data warehouse was growing at a 1TB/year rate, and we needed a solution that would be both cheap and effective.
Previously we were using Azure SQL Database with its JSON capabilities and various Azure serverless services to manage our data, but at that growth rate, time and cost were becoming limiting factors.

Pros

Build, schedule and monitor complex data pipelines (Azure Data Factory component)
Access your data lake using the familiar T-SQL syntax and TDS-enabled tools (SSMS, ADS, ...). This is especially useful for business people that are used to a specific workflow.
Support a wide range of data transformation tools, from low-code (DataFlows) to full-code (Spark), all integrated in a single central orchestrator (Azure Data Factory-like)
Provide all these services as a single very convenient package, without the need to know beforehand all the configuration behind

Cons

There's no support for Synapse Serverless objects (e.g., views) in SSDT - the VCS-friendly approach to schema deployments from Microsoft. SSDT is available for almost all other SQL Server and Azure SQL products, including Synapse Dedicated SQL Pools.
There are lots of ways to accomplish the same task, and it's not very clear which one is best suited for a given scenario other than trial and error. Also, some scenarios (e.g., efficient management of late arrivals) don't have a clear solution path.
I think it would be cool to have a tighter integration of the product with the Azure Data Studio client, not only for connecting to SQL Serverless or Dedicated Pools. For example, PySpark development and debugging would be much easier if done from ADS.

Likelihood to Recommend

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.

Verified User

Engineer in Engineering (Retail company, 51-200 employees)

Vetted Review

1 year of experience

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Cloud data simplified: a great all-in-one tool for all your data analytics needs

Rating: 8 out of 10

Incentivized

December 20, 2022

Use Cases and Deployment Scope

Pros

Build, schedule and monitor complex data pipelines (Azure Data Factory component)
Access your data lake using the familiar T-SQL syntax and TDS-enabled tools (SSMS, ADS, ...). This is especially useful for business people that are used to a specific workflow.
Support a wide range of data transformation tools, from low-code (DataFlows) to full-code (Spark), all integrated in a single central orchestrator (Azure Data Factory-like)
Provide all these services as a single very convenient package, without the need to know beforehand all the configuration behind

Cons

There's no support for Synapse Serverless objects (e.g., views) in SSDT - the VCS-friendly approach to schema deployments from Microsoft. SSDT is available for almost all other SQL Server and Azure SQL products, including Synapse Dedicated SQL Pools.
There are lots of ways to accomplish the same task, and it's not very clear which one is best suited for a given scenario other than trial and error. Also, some scenarios (e.g., efficient management of late arrivals) don't have a clear solution path.
I think it would be cool to have a tighter integration of the product with the Azure Data Studio client, not only for connecting to SQL Serverless or Dedicated Pools. For example, PySpark development and debugging would be much easier if done from ADS.

Likelihood to Recommend

Verified User

Engineer in Engineering (Retail company, 51-200 employees)

Vetted Review

1 year of experience