We performed a comparison between BigQuery and Microsoft Azure Synapse Analytics based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."It's pretty stable. It's fast, and it is able to go through large quantities of data pretty quickly."
"What I like most about BigQuery is that it's fast and flexible. Another advantage of BigQuery is that it's easy to learn."
"BigQuery excels at structuring data, performing predictions, and conducting insightful analyses and it leverages machine learning and artificial intelligence capabilities, powered by Google's Duarte AI."
"I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data."
"When integrating their system into the cloud-based solutions, we were able to increase their efficiency and overall productivity twice compared with their on-premises option."
"It's straightforward to set up."
"The setup is simple."
"The integrated data storage features are good."
"The integrated workspace in Microsoft Azure Synapse Analytics where everything comes together, such as Power BI and Data Factory, is very good. Additionally, the ability to do dedicated SQL pooling is a benefit."
"It is a fantastic product; we are satisfied with its features and performance."
"We can have the dedicated SQL up and running within 15 minutes."
"One of the most valuable features of this solution is its ability to integrate well with other services offered by Azure."
"The most valuable feature of the solution is the analytics and that it can connect with Power BI."
"We find the serverless tool to be the most valuable feature ."
"The platform has multiple valuable use cases. They include performance, compatibility, flexibility, and cost."
"It's feature-rich. It has a wide range of features."
"There is a good amount of documentation out there, but they're consistently making changes to the platform, and, like, their literature hasn't been updated on some plans."
"The initial setup could be improved making it easier to deploy."
"We'd like to see more local data residency."
"The process of migrating from Datastore to BigQuery should be improved."
"Some of the queries are complex and difficult to understand."
"When it comes to queries or the code being executed in the data warehouse, the management of this code, like integration with the GitHub repository or the GitLab repository, is kind of complicated, and it's not so direct."
"I would like to see version-based implementation and a fallback arrangement for data stored in BigQuery storage. These are some features I'm interested in."
"There are many tools that you have to use with BigQuery that are different services also provided for by Google. They need to all be integrated into BigQuery to make the solution easier to use."
"It's pay as you go, so you never know what your bill is going to be beforehand, and that's scary for customers. If you have someone who makes a mistake and the program's a loop that is running all night, you could receive a very expensive bill."
"Right now, we are really struggling with the performance. it's not as good as we had hoped."
"We encountered data processing and transformation issues while working with Apache Spark languages for the product."
"In the future, Microsoft Azure Synapse Analytics has the potential to enhance its capabilities by expanding its connectors, specifically with regard to Oracle solutions, such as operating systems. This would involve a comprehensive approach to adding more connectors for both data input and consumption purposes. By doing so, Microsoft Azure Synapse Analytics would be better equipped to meet the diverse needs of its users and achieve greater efficiency in its performance. The provision of more connectors is definitely a crucial area that needs improvement."
"I am pretty sure that there are areas that need improvement but I just can think of them off the top of my head."
"The security performance and cost are the two things that needs improvement."
"This solution needs to have query caching so that if the same query is run and the results are available, it will return the data from the cache without having to re-run the query."
"Unfortunately, we have had some issues with the dashboard reporting. Sometimes, the data for specific periods would just appear blank on the dashboard. To investigate this, we worked with a Microsoft incident agent and it turned out to be a result of bugs in the platform when dealing with specific types of queries in Azure Data Factory."
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BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews while Microsoft Azure Synapse Analytics is ranked 2nd in Cloud Data Warehouse with 86 reviews. BigQuery is rated 8.2, while Microsoft Azure Synapse Analytics is rated 7.8. The top reviewer of BigQuery writes "Expandable and easy to set up but needs more local data residency". On the other hand, the top reviewer of Microsoft Azure Synapse Analytics writes "No competitors provide the entire solution to one place ". BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse, Vertica and VMware Tanzu Greenplum, whereas Microsoft Azure Synapse Analytics is most compared with Azure Data Factory, SAP BW4HANA, Snowflake, Oracle Autonomous Data Warehouse and Teradata. See our BigQuery vs. Microsoft Azure Synapse Analytics report.
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