Azure Synapse Analytics (Azure SQL Data Warehouse) Reviews

15 Ratings
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Score 8.4 out of 100

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Reviews (1-4 of 4)

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November 07, 2020
Prabhu Sundararaj | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
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Our clients belong to the energy and utility sector. I am a data engineer involved in migrating on-premises SQL data warehouse to Azure Synapse Analytics (Azure SQL Data Warehouse) to reduce costs and improve the data availability. Our existing on-premises system fetches enormous, unstructured data from HDFS and it required a tremendous amount of time to process the data. The effective polybase method in Azure Synapse Analytics (Azure SQL Data Warehouse) helps us satisfy our clients.
  • Massively parallel processing system reduces the bottleneck while loading the data from the source
  • Dynamic data masking helps us to secure the data
  • Automated indexing and alerts help us to rectify issues before they reach the business
  • Limited T-SQL query library
  • It is not suitable for OLTP environment
  • Doesn't support cross-database queries
When using ELT loading source data into blob storage, we can directly load into the data warehouse with help of ploybase. The assively parallel processing engine helps us to save data directly in logical and physical partitions without going to control nodes. This prevents bottleneck while loading data from source to database.
I am happy, the Azure team has strong customer support. They have an effective customer forum and weekly summits to encourage and establish their features and they support us in deep dives into the tool to utilize many benefits from it. They have 24/7 support and whenever we have issue they promptly resolve the tickets.
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September 18, 2019
Anonymous | TrustRadius Reviewer
Score 9 out of 10
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Verified User
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We use SQL Data Warehouse to load the operations and audit data for our company and produce analytical and operational reports out of it.
  • Easy to manage and support a wide variety of data types.
  • High Performance and throughput.
  • Reduced latency time.
  • Easy to manage objects.
  • Automated query tuning.
  • Better indexing methods.
Functions well as a transactional data store. It is less suited for analytical purposes.
I like their Support team.
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July 22, 2016
Samir Patel, PMP | TrustRadius Reviewer
Score 9 out of 10
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Verified User
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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
  • 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
  • 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

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.

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July 15, 2016
Anonymous | TrustRadius Reviewer
Score 9 out of 10
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Verified User
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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.
  • 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.
  • 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.
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.
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Azure Synapse Analytics (Azure SQL Data Warehouse) Scorecard Summary

What is Azure Synapse Analytics (Azure SQL Data Warehouse)?

Azure Synapse Analytics is described as the former Azure SQL Data Warehouse, evolved, and as a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives users the freedom to query data using either serverless or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
Categories:  Data Warehouse

Azure Synapse Analytics (Azure SQL Data Warehouse) Technical Details

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