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.

Reviews

12 Reviews

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 (51-200 employees)

Vetted Review

1 year of experience

Azure Synapse Does It All

Rating: 8 out of 10

Incentivized

March 17, 2022

Use Cases and Deployment Scope

As a consulting company, we implement data warehouse solutions for our clients. We use Azure Synapse for enterprises data warehouse implementations. Data from various internal sources like sales, finance and operations are integrated into Synapse via Azure Data Factory and Data Lake. It’s used as reporting data source for Microsoft Power BI as well.

Pros

Data integration via poly base
Data distribution
Create table as select
Resource allocation via user groups (for production ETL and report users)

Cons

Integrating external 3rd party data sources is very easy in Snowflake and it’s missing in Azure Synapse
Master data services and data quality services are missing in Azure Synapse. They are useful features present in on Orem Sql server
Resource usage reports (top 10 expensive queries, most frequently run queries, etc) are a feature that can be added in Azure Synapse. It’s present in an on-prem SQL server. DMVs are there but viewing it visually as a report is more helpful.

Likelihood to Recommend

Big Data load are made simple using polybase feature. You just have to create external tables to connect to any data source files (of any format) in Azure Data Lake. There is no need for map-reducing that is done in Hadoop clusters. You just need to know sql to do data integration.

Verified User

Consultant in Information Technology (51-200 employees)

Vetted Review

5 years of experience

Bring together Big Data analytics and enterprise data warehousing!!!

Rating: 10 out of 10

Incentivized

March 10, 2022

Use Cases and Deployment Scope

We've been using Azure Synapse Analytics to create data pipelines for onPrem/onCloud ETL processing where the transform data will store inside the Azure Data Lake for further processing using PowerBI.

Pros

Create data pipelines to connect with multiple data workspace(s) and external data
Ability to connect with Azure Data Lake (sequentially) for data warehousing
Being able to manage connections and create integration runtimes (for onPrem data capture)

Cons

Thus far haven't seen any complications and/or any missing features

Likelihood to Recommend

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.

Verified User

Professional in Information Technology (501-1000 employees)

Vetted Review

1 year of experience

Satisfied DBA/Data engineer

Rating: 10 out of 10

Incentivized

November 30, 2021

Use Cases and Deployment Scope

I am an independent developer using Azure Synapse for other companies. My company is machinery production oriented (i.e. automotive companies) so I'm used to [utilizing] the Synapse for statistics-driven quality control, some logistics stuff, etc.

Pros

The combination of SQL/unstructured data
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

Cons

I have no idea right now. But... Synapse Analytics are typically seen as batch-processing of source data. What about tighter cooperation with streaming features like Event Hubs?

Likelihood to Recommend

The most frequent answer to questions like this should be... IT DEPENDS. Synapse Analytics has some role in its DNA. It's not dedicated [to] tasks like some OLTP with many reads, however. When we are talking about Azure Synapse, we are talking about modernized BI stuff with great capabilities to involve big data processing to reach deeper insights [into] our data.

Vladimír Mužný

Owner, Developer at DROPMAN s.r.o. (1-10 employees)

Vetted Review

100 years of experience

View profile

Azure Synapse Analytics - more powerful SQL for data warehousing

Rating: 8 out of 10

Incentivized

October 21, 2021

Use Cases and Deployment Scope

Azure Synapse Analytics is being used for data Warehousing - Azure Data Factory to pull in the initial data from source to Data Lake, then Spark notebook to process from raw (bronze) to staging (silver) in Synapse dedicated pools, then stored procedures in Synapse dedicated pool to process from staging to reporting (gold).

Pros

fast query results
integrated systems
one application/area for all processes

Cons

Delta Lake doesn't have full capabilities yet
spark doesn't yet have delta live tables
coding differences from Databricks' spark aren't well documented

Likelihood to Recommend

Azure Synapse Analytics is well suited to Data Warehouse scenarios with large data tables because of its distributed computing. If most tables have fewer than 1 million rows, then the cost of Synapse is not worthwhile - regular Azure SQL or Azure Analysis Services could suffice. If most tables have more than 1 million rows, then it's worthwhile to get the additional speed for querying large data sets.

Verified User

Consultant in Professional Services (201-500 employees)

Vetted Review

5 years of experience

Modern Database

Rating: 8 out of 10

Incentivized

August 25, 2021

Use Cases and Deployment Scope

We use Azure Synapse Analytics (Azure SQL Data Warehouse) to hold all our daily sales data to serve reports. Without any storage constraint, we save large datasets and process them in a matter of time, thanks to the Azure lake storage support and Massive Parallel processing capabilities. It supports major file formats like Avro, Parquet and many more.

Pros

Easy to Manage data
Blazing fast query processing
Supports Modern fileformats

Cons

Documentation and Usecases
Pricing
Admin capabilities

Likelihood to Recommend

Enterprises which require to manage huge datasets and need support to bigdata capabilities in a cost efficient way. Enterprises that process real-time data for their analysis like streaming data and IOT data. Combining Azure Synapse Analytics and Data lake storage provides a better performance and cost effective way to manage a huge dataset.

Verified User

Professional in Information Technology (201-500 employees)

Vetted Review

4 years of experience

Plays with Power BI nicely

Rating: 8 out of 10

Incentivized

August 3, 2021

Use Cases and Deployment Scope

Microsoft Azure Synapse Analytics (formally Azure SQL Data Warehouse) is being used as our marketing data warehouse. We are pulling data down from a number of different API's such as Facebook ads, Google ads and Google analytics, and then pumping that information back into the Azure Synapse Analytics Warehouse on a daily basis.

Pros

They unify many data sources easily
There is some "code free" ETL work it enables
There is some AI integration that works nice

Cons

The cost structure is difficult to understand
The job scheduling capabilities aren't easy to use
The logging metrics aren't easy to see

Likelihood to Recommend

Azure Synapse Analytics is very well suited for companies that are using the Microsoft Power BI analytics tool (business intelligence). The reason being, you don't need to provide a data gateway to move data from your database to the reporting service online if you are using this type of database. This is a huge win for processing data.

Scott Kennedy

Director, eCommerce Analytics and Digital Marketing in Marketing at Franklin Sports (201-500 employees)

Vetted Review

1 year of experience

View profile

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 (1001-5000 employees)

Vetted Review

5 years of experience

View profile

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 (10,001+ employees)

Vetted Review

4 years of experience

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 (1001-5000 employees)

Vetted Review

4 years 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 (51-200 employees)

Vetted Review

1 year of experience