BigQuery vs Microsoft Azure Synapse Analytics comparison

Google and Microsoft are both solutions in the Cloud Data Warehouse category. Google is ranked #5 with an average rating of 8.3, while Microsoft is ranked #2 with an average rating of 8.2. Google holds a 9.3% mindshare in CDW, compared to Microsoft’s 9.1% mindshare. Additionally, 100% of Google users are willing to recommend the solution, compared to 94% of Microsoft users who would recommend it.

BigQuery

Read 35 BigQuery reviews

4,323 Views
3,415 Comparison Views

100% willing to recommend

Microsoft Azure Synapse Ana...

Read 90 Microsoft Azure Synapse Analytics reviews

11,528 Views
5,041 Comparison Views

94% willing to recommend

BigQuery

Microsoft Azure Synapse Ana...

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Executive Summary

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.

To learn more, read our detailed BigQuery vs. Microsoft Azure Synapse Analytics Report (Updated: October 2024).

Buyer's Guide

BigQuery vs. Microsoft Azure Synapse Analytics

October 2024

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Helped 816,562 peers since 2012

Categories and Ranking

BigQuery

Ranking in Cloud Data Warehouse

5th

Average Rating

8.2

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Microsoft Azure Synapse Ana...

Ranking in Cloud Data Warehouse

2nd

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2024, in the Cloud Data Warehouse category, the mindshare of BigQuery is 9.3%, up from 6.7% compared to the previous year. The mindshare of Microsoft Azure Synapse Analytics is 9.1%, down from 14.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse

Featured Reviews

Sathishkumar Jayaprakash

Engineer at HTC Global Services (INDIA) Private

Efficient large dataset handling with seamless service integration

BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases. It integrates well with other GCP products, and creating subscriptions in the UI is straightforward. The whole ecosystem of GCP products makes BigQuery beneficial for our data-handling tasks. Additionally, it is more cost-effective compared to alternatives like AWS.

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Sunil Gidwani

Cloud Solution Development Manager at Stryker Corporation

No competitors provide the entire solution to one place

I rate Azure Synapse Analytics eight out of 10. No competitors provide the entire solution to one place like Synapse. For example, a database just focuses moving and manipulating data, etc. But Synapse is like an all-inclusive workspace. I advise other people to go with Databricks Notebook if you need a computation engine. It has a solid SQL storage procedure. Suppose you are dealing with complex transformation logic and manipulation of run-time data flows. In that case, it's better to use Databricks than any Microsoft ADF. DataBricks looks more promising in terms of computing in memory, so we integrated Databricks in Synapse.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The initial setup is straightforward."

"As a cloud solution, it's easy to set up."

"The main thing I like about BigQuery is storage. We did an on-premise BigQuery migration with trillions of records. Usually, we have to deal with insufficient storage on-premises, but in BigQuery, we don't get that because it's like cloud storage, and we can have any number of records. That is one advantage. The next major advantage is the column length. We have some limits on column length on-premises, like 10,000, and we have to design it based on that. However, with BigQuery, we don't need to design the column length at all. It will expand or shrink based on the records it's getting. I can give you a real-life example based on our migration from on-premises to GCP. There was a dimension table with a general number of records, and when we queried that on-premises, like in Apache Spark or Teradata, it took around half an hour to get those records. In BigQuery, it was instant. As it's very fast, you can get it in two or three minutes. That was very helpful for our engineers. Usually, we have to run a query on-premises and go for a break while waiting for that query to give us the results. It's not the case with BigQuery because it instantly provides results when we run it. So, that makes the work fast, it helps a lot, and it helps save a lot of time. It also has a reasonable performance rate and smart tuning. Suppose we need to perform some joins, BigQuery has a smart tuning option, and it'll tune itself and tell us the best way a query can be done in the backend. To be frank, the performance, reliability, and everything else have improved, even the downtime. Usually, on-premise servers have some downtime, but as BigQuery is multiregional, we have storage in three different locations. So, downtime is also not getting impacted. For example, if the Atlantic ocean location has some downtime, or the server is down, we can use data that is stored in Africa or somewhere else. We have three or four storage locations, and that's the main advantage."

"What I like most about BigQuery is that it's fast and flexible. Another advantage of BigQuery is that it's easy to learn."

"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."

"The product's most valuable features include its scalability and the ability to handle complex queries on large datasets."

"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."

"There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot."

More BigQuery pros

"The architecture, including compute and storage, is good."

"The most valuable feature is performance gains."

"The most valuable features are the flexibility and that it's easy to use as an end-user compared to AWS."

"Scaling this solution is easy and the uptime is okay."

"Azure Synapse combines the strengths of SQL technologies for effective enterprise data management."

"The most valuable feature of the solution is the analytics and that it can connect with Power BI."

"I like its performance. It is well-structured and offers a lot of practical options. Because of the visual design of the product, it is very easy to find icons and other visual elements. It took me more than two weeks to understand how it works. I had an intensive training session to understand it, which was enough. It was not hard to understand how it works."

"Synapse Analytics gives you information and reports in real-time."

More Microsoft Azure Synapse Analytics pros

Cons

"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."

"The processing capability can be an area of improvement."

"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."

"I understand that Snowflake has made some improvements on its end to further reduce costs, so I believe BigQuery can catch up."

"They could enhance the platform's user accessibility."

"The price could be better. Compared to competing solutions, BigQuery is expensive. It's only suitable for enterprise customers, not small and medium-sized businesses, as they cannot afford this kind of solution. In the next release, it would be better if they improved their AI bot. Although machine learning and artificial intelligence are doing wonders, there is still a lot of room to enhance them."

"It would be better if BigQuery didn't have huge restrictions. For example, when we migrate from on-premises to on-premise, the data which handles all ebook characters can be handled on-premise. But in BigQuery, we have huge restrictions. If we have some symbols, like a hash or other special characters, it won't accept them. Not in all cases, but it won't accept a few special characters, and when we migrate, we get errors. We need to use Regexp or something similar to replace that with another character. This isn't expected from a high-range technology like BigQuery. It has to adapt all products. For instance, if we have a TV Showroom, the TV symbol will be there in the shop name. Teradata and Apache Spark accept this, but BigQuery won't. This is the primary concern that we had. In the next release, it would be better if the query on the external table also had cache. Right now, we are using a GCS bucket, and in the native table, we have cache. For example, if we query the same table, it won't cost because it will try to fetch the records from the cached result. But when we run queries on the external table a number of times, it won't be cached. That's a major drawback of BigQuery. Only the native table has the cache option, and the external table doesn't. If there is an option to have an external table for cache purposes, it'll be a significant advantage for our organization."

"We'd like to see more local data residency."

More BigQuery cons

"Microsoft Azure Synapse Analytics could improve the section in the solution where you can implement the Python Spark pipelines, it's not the same as in Databricks which would be better."

"An area for improvement in Microsoft Azure Synapse Analytics is its user interface. You can use it for analytical purposes, but its platform should be a little bit more user-friendly. Another small point for improvement in Microsoft Azure Synapse Analytics is its stability. It's good currently, but it could still be improved. Microsoft is combining different tools and technologies into one solution, so in the future, I'm expecting to see even more improvement in Microsoft Azure Synapse Analytics. An additional feature I'd like to see in the next version of Microsoft Azure Synapse Analytics is the drag-and-drop feature. If you're doing some integrations where you can write Scala or you have SPARK programming or SQL, or you're combining different programming, the process should be seamless, and you should be able to drag and drop in Microsoft Azure Synapse Analytics. When doing reporting in the solution, you should also be able to drag and drop. There should be connectors available and a drag-and-drop feature available in the user interface of Microsoft Azure Synapse Analytics, so you won't have to worry about how all processes would work together. You need to be able to drag and drop even from the backend, and having this feature will make the solution more user-friendly."

"The need to improve a little bit in terms of user-friendliness."

"One area that could be improved is the schema management."

"The macro functions, though useful, are not totally user-friendly. Some people have difficulties in learning them."

"Synapse Analytics needs to develop an automation framework because now you have to build a cache yourself. You have to build a pipeline in WhereScape, which does end-to-end pipeline automation well. Microsoft should come up with a framework to save people time. If they developed a tool like WhereScape, it would dramatically reduce development time."

"This is a young product in transition to the cloud and it needs more work before it is both settled as a product and competitive in the market."

"Real-time integration is hard to do in Microsoft Azure Synapse Analytics."

More Microsoft Azure Synapse Analytics cons

Pricing and Cost Advice

"The tool has competitive pricing."

"Price-wise, I think that is very reasonable."

"The product’s pricing could be more flexible for end users."

"BigQuery is inexpensive."

"The price could be better. Usually, you need to buy the license for a year. Whenever you want more, you can subscribe to it, and you can use it. Otherwise, you can terminate the license. You can use it daily or monthly, and we use it based on a project's requirements."

"1 TB is free of cost monthly. If you use more than 1 TB a month, then you need to pay 5 dollars extra for each TB."

"The price is a bit high but the technology is worth it."

"The platform is inexpensive."

More BigQuery pricing and cost advice

"Our license is very expensive"

"The pricing is competitive, but only when you pay upfront. If you pay as you go, it's not as competitive. I'd give pricing a rating of seven out of ten."

"Microsoft Azure Synapse Analytics is an expensive solution and there is a license needed to use it. My company has an enterprise license."

"I understand that Synapse Analytics' pricing is lower than Informatica's."

"There is a cost calculator available online that allows you to input your entire scenario, and it will get back to you with information on what the costs are going to be."

"There's no license required for Microsoft Azure Synapse Analytics. the model is more of a use-based system. You got to pay for computing power and disk storage. Everything has different units and is kept backed up. Microsoft Azure Synapse Analytics uses storage units(SU). This is how everything's computed for cost."

"The price of Microsoft Azure Synapse Analytics can vary. Other solutions are typically fixed prices and a fixed cost regardless of usage, but this solution is based on usage or consumption."

"It just depends on how big your instance is. It could be anywhere from 1,000 to 50,000 per year depending on how big your instance is. You pay based on how big you make your database. Essentially, they charge you per hour of usage."

More Microsoft Azure Synapse Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

16%

Financial Services Firm

14%

Manufacturing Company

12%

Retailer

Educational Organization

44%

Computer Software Company

Financial Services Firm

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about BigQuery?

The initial setup process is easy.

See all answers

What is your experience regarding pricing and costs for BigQuery?

The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity.

See all answers

What needs improvement with BigQuery?

Since I used BigQuery over the GCP cloud environment, I'm not sure whether we can go through internal IDEAs like IntelliJ or DBeaver that we use to connect with databases. Instead of connecting dir...

See all answers

How does Amazon Redshift compare with Microsoft Azure Synapse Analytics?

Amazon Redshift is very fast, has a very good response time, and is very user-friendly. The initial setup is very straightforward. This solution can merge and integrate well with many different dat...

See all answers

What do you like most about Microsoft Azure Synapse Analytics?

The product is easy to use, and anybody can easily migrate to advanced DB.

See all answers

What is your experience regarding pricing and costs for Microsoft Azure Synapse Analytics?

The cost is reasonable for our company. There is no license cost; we pay only for Azure Compute's costs. It is important to manage the cost efficiently on a daily basis.

See all answers

Comparisons

Snowflake vs BigQuery

Compared 39% of the time

Teradata vs BigQuery

Compared 14% of the time

Vertica vs BigQuery

Compared 12% of the time

Oracle Autonomous Data Warehouse vs BigQuery

Compared 11% of the time

VMware Tanzu Data Solutions vs BigQuery

Compared 1% of the time

More BigQuery Competitors

SAP BW4HANA vs Microsoft Azure Synapse Analytics

Compared 17% of the time

Snowflake vs Microsoft Azure Synapse Analytics

Compared 12% of the time

Oracle Autonomous Data Warehouse vs Microsoft Azure Synapse Analytics

Compared 11% of the time

Azure Data Factory vs Microsoft Azure Synapse Analytics

Compared 11% of the time

Teradata vs Microsoft Azure Synapse Analytics

Compared 10% of the time

More Microsoft Azure Synapse Analytics Competitors

Product Reports

Buyer's Guide

BigQuery

November 2024

Download BigQuery product report

Buyer's Guide

Microsoft Azure Synapse Analytics

November 2024

Microsoft Azure Synapse Analytics report

Download Microsoft Azure Synapse Analytics product report

Also Known As

No data available

Azure Synapse Analytics, Microsoft Azure SQL Data Warehouse, Microsoft Azure SQL DW, Azure SQL Data Warehouse, MS Azure Synapse Analytics

Learn More

Google

Microsoft

Overview

BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.

Microsoft Azure Synapse Analytics is an end-to-end analytics solution that successfully combines analytical services to merge big data analytics and enterprise data warehouses into a single unified platform. The solution can run intelligent distributed queries among nodes, and provides the ability to query both relational and non-relational data.

Microsoft Azure Synapse Analytics is built with these 4 components:

Synapse SQL
Spark
Synapse Pipeline
Studio

Microsoft Azure Synapse Analytics Features

Microsoft Azure Synapse Analytics has many valuable key features, including:

Cloud Data Service: WIth Microsoft Azure Synapse Analytics you can operate services (data analytics, machine learning, data warehousing, dashboarding, etc.) in a single workspace via the cloud.
Structured and unstructured data: Microsoft Azure Synapse Analytics supports both structured and unstructured data and allows you to manage relational and non-relational data - unlike data warehouses and lakes that tend to store them respectively.
Effective data storage: Microsoft Azure Synapse Analytics offers next-level data storage with high availability and different tiers.
Responsive data engine: Microsoft Azure Synapse Analytics uses Massive Parallel Processing (MPP) and is designed to handle large volumes of data and analytical workloads efficiently without any problems.
Several scripting languages: The solution provides language compatibility and supports different programming languages, such as Python, Java, Spark SQL, and Scala.
Query optimization: Microsoft Azure Synapse Analytics works well to facilitate limitless concurrency and performance optimization. It also simplifies workload management.

Microsoft Azure Synapse Analytics Benefits

Some of the benefits of using Microsoft Azure Synapse Analytics include:

Database templates: Microsoft Azure Synapse Analytics offers industry-specific database templates that make it easy to combine and shape data.
Better business insights: With Microsoft Azure Synapse Analytics you can expand discovery of insights from all your data and apply machine learning models to all your intelligent apps.
Reduce project development time: Microsoft Azure Synapse Analytics makes it possible to have a unified experience for developing end-to-end analytics, which reduces project development time significantly.
Eliminate data barriers: By using Microsoft Azure Synapse Analytics, you can perform analytics on operational and business apps data without data movement.
Advanced security: Microsoft Azure Synapse Analytics provides both advanced security and privacy features to ensure data protection.
Machine Learning: Microsoft Azure Synapse Analytics integrates Azure Machine Learning, Azure Cognitive Services, and Power BI.

Reviews from Real Users

Below are some reviews and helpful feedback written by Microsoft Azure Synapse Analytics users who are currently using the solution.

PeerSpot user Jael S., who is an Information Architect at Systems Analysis & Design Engineering, comments on her experience using the product, saying that it is “Scalable, intuitive, facilitates compliance and keeps your data secure”. She also says "We also like governance. It looks at what the requirements are for the company to identify the best way to ensure compliance is met when you move to the cloud."

Michel T., CHTO at Timp-iT, mentions that "the features most valuable are the simplicity, how easy it is to create a dashboard from different information systems."

A Senior Teradata Consultant at a tech services company says, "Microsoft provides both the platform and the data center, so you don't have to look for a cloud vendor. It saves you from having to deal with two vendors for the same task."

Sample Customers

Information Not Available

Toshiba, Carnival, LG Electronics, Jet.com, Adobe,

Buyer's Guide

BigQuery vs. Microsoft Azure Synapse Analytics

October 2024

Free Report: BigQuery vs. Microsoft Azure Synapse Analytics

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