Azure Synapse Analytics Reviews & Insights

Score6.9 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

5 Reviews

Enterprises (1,001+ employees)

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

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Synapse Analytics Review

Rating: 8 out of 10

Incentivized

August 12, 2025

Use Cases and Deployment Scope

We use it as a primary driver of our DWH solution. We use the spark cluster to process a very large amount of data on a daily schedule. Using Synapse notebooks has enabled us to reuse a single notebook for historization of the data across multiple sources. All of the sources that land in our S3 bucket(either via push or pull mechanism) we are able to clean, store and transform using Synapse notebooks.

Pros

Large batch load processing
Reusing notebooks across multiple sources
Accepting parameters sent from ADF(seamless integration)

Cons

Its not being actively developed, so no new features
It lags behind Databricks in features it provides
Synapse Orchestrator is not the best(Azure Synapse Pipelines)

Likelihood to Recommend

If you're looking for a spark based platform that integrates well with the rest of the Azure stack, Synapse is a really good choice.

Its a really good for batch loading of massive datasets. (Think SAP ACDOCA and similar).

Its less appropriate if you need to ingest via streaming. There aren't a lot of options for streaming or API data to be handled.

Borna Marković

Data Engineer in Information Technology at BDR (Wholesale, 1001-5000 employees)

Vetted Review

2 years of experience

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Why Azure Synapse Analytics became our Go - to - data analytics platform

Rating: 9 out of 10

Incentivized

August 12, 2025

Use Cases and Deployment Scope

In my organization , Azure Synapse Analytics plays a vital role cause it really helps in data integration, transformation and reporting process.
It serves the foundation of our enterprise data ware house from multiple sources including Salesforce , Pardot , HubSpot, Share point and our internal data base etc.
It really helps to consume less time to make reports on Power BI

Pros

Seamless Data fetching across multiple sources
Handle easily large data set on SQL Query
Deep Integration with Power BI for real time dashboards and reports

Cons

They have limited UI responsiveness and usability in Synapse Studio.
Limited custom visualization in synapse
Spark performance is inconsist.

Likelihood to Recommend

It basically handle large data sets across multiple channels for different business units.
Example : we consolidate lead gen, campaign spend, sales pipeline etc.
It also create automated data flow that pulls from various sources like Salesforce , Excel , Pardot and SQL database. It also cleaning , transforming and sorting the data for Power BI dashboards without manual efforts.

Verified User

Analyst in Marketing (Staffing Recruiting company, 10,001+ employees)

Vetted Review

2 years of experience

SQL Data Warehouse- everything you need to know

Rating: 9 out of 10

Incentivized

July 22, 2016

Use Cases and Deployment Scope

SQL Data Warehouse is being used to hold all of our summary level reporting. The data is loaded using SSIS and transformed into a star schema. SQL does a great job mapping all of the OLAP values and providing efficient structures to house all of the reporting data. We then use a reporting tool to build cubes and publish the data

Pros

It is very cost-effective
Development time needed was much less in comparison to other systems
Played very nicely with our ETL and OLAP reporting tools

Cons

More features would be a plus
I would like to see Microsoft offer some diagramming tools for data warehouse
I believe processing time and speed could always be improved

Likelihood to Recommend

SQL Data Warehouse is always well suited in a Microsoft SQL environment. When you are using tools like SSIS, SSAS and SSRS, SQL Data Warehouse fits in nicely as the OLAP backend.

Some challenges faced for this product are in very large expansive environments where the transact databases might be coming from different sources like Oracle or Sybase.

Samir Patel, PMP

SQL Development Team Lead in Information Technology at Allianz Asset Management (Investment Management, 1001-5000 employees)

Vetted Review

5 years of experience

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Better than querying off your OLTP

Rating: 9 out of 10

Incentivized

July 15, 2016

Use Cases and Deployment Scope

We use it to store large amounts of SQL data for our predictive analytics and big data modeling. We use it across several team but I cannot say it is used for the entire organization as my department operates relatively independently of the rest of the organization. We have an extremely large data sets and need to store it in a way that makes it accessible and fast.

Pros

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.

Cons

It takes some time to setup a proper SQL Datawarehouse architecture. Without proper SSIS/automation scripts, this can be a very daunting task.
It takes a lot of foresight when designing a Data Warehouse. If not properly designed, it can be very troublesome to use and/or modify later on.
It takes a lot of effort to maintain. Businesses are continually changing. With that, a full time staff member or more will be required to maintain the SQL Data Warehouse.

Likelihood to Recommend

It is very well suited for big data analytics. Predictive modeling, optimization, and other large scale analysis benefit from using a properly defined SQL Data Warehouse. It is also suited for simple business intelligence such as building historical and active dashboards.

Verified User

Contributor in Information Technology (Information Technology and Services company, 10,001+ employees)

Vetted Review

4 years of experience

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Synapse Analytics The go to solution for medium scaled warehousing

Rating: 6 out of 10

Incentivized

August 18, 2025

Use Cases and Deployment Scope

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

Paras Jasal

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

Vetted Review

4 years of experience

View profile