Azure Synapse Analytics Professional, Scientific, and Technical Services 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

3 Reviews

Professional, Scientific, and Technical ServicesInformation Technology & Services3

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 (Information Technology & Services company, 51-200 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 (Information Technology & Services company, 201-500 employees)

Vetted Review

4 years of experience

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

Loading Reviews List....