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.
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Azure Synapse Analytics Reviews
6 Reviews
Mid-sized Companies (51-1,000 employees)
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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.
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.
VU
Verified User
Consultant in Information Technology (51-200 employees)
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.
VU
Verified User
Professional in Information Technology (501-1000 employees)
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.
VU
Verified User
Consultant in Professional Services (201-500 employees)
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.
VU
Verified User
Professional in Information Technology (201-500 employees)
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.
