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Azure Stream Analytics vs Google Cloud Dataflow comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

Review summaries and opinions

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

ROI

Sentiment score
7.4
Azure Stream Analytics offers quick solutions with a 10% ROI, ideal for simple setups without major upfront costs.
Sentiment score
7.2
Many startups find Google Cloud Dataflow's ROI unclear, yet it offers significant time savings of around 70 percent.
 

Customer Service

Sentiment score
6.8
Azure Stream Analytics' support is responsive with effective resolution, but satisfaction varies due to SLA, language barriers, and demand.
Sentiment score
7.9
Google Cloud Dataflow provides strong service and updates, but accessing technical support is slow and can be challenging.
There is a big communication gap due to lack of understanding of local scenarios and language barriers.
Any time I needed assistance, they were helpful.
The fact that no interaction is needed shows their great support since I don't face issues.
Whenever we have issues, we can consult with Google.
 

Scalability Issues

Sentiment score
7.8
Azure Stream Analytics offers scalable, flexible, and affordable cloud solutions suitable for diverse organizational needs and varying workloads.
Sentiment score
7.2
Google Cloud Dataflow excels in scalability, with flexible autoscaling and custom options, though some users suggest improvements.
Maintenance requires a couple of people, however, it's not a full-time endeavor.
Azure Stream Analytics is scalable, and I would rate it seven out of ten.
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
 

Stability Issues

Sentiment score
6.7
Azure Stream Analytics is generally stable with minor glitches; users report improvements and effective support for complex issues.
Sentiment score
8.3
Google Cloud Dataflow is highly stable and reliable, consistently receiving high user ratings for its flawless performance.
They require significant effort and fine-tuning to function effectively.
The job we built has not failed once over six to seven months.
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
 

Room For Improvement

Azure Stream Analytics users face high costs, limited integration, inadequate support, and issues with connectivity, customization, and scalability.
Google Cloud Dataflow needs improved Kafka integration, error logging, and community support, with easier setup and better Python SDK features.
A cost comparison between products is also not straightforward.
Although customers can invite Microsoft Taiwan office staff for introductions, there are not many useful case references, suggesting room for improvement in market support.
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
Dealing with a huge volume of data causes failure due to array size.
 

Setup Cost

Azure Stream Analytics pricing is competitive but complex, with pay-as-you-go options and varying views on cost-effectiveness.
Google Cloud Dataflow is favored for cost-effectiveness, cheaper than AWS, with users rating pricing between two and seven.
From my point of view, it should be cheaper now, considering the years since its release.
We sell the data analytics value and operational value to customers, focusing on productivity and efficiency from the cloud.
It is part of a package received from Google, and they are not charging us too high.
 

Valuable Features

Azure Stream Analytics provides seamless real-time analytics, integration with Microsoft tools, scalability, and ease of use for real-time decision-making.
Google Cloud Dataflow offers seamless integration, cost-effectiveness, and scalability with robust support for batch and streaming processes.
It is quite easy for my technicians to understand, and the learning curve is not steep.
Clients can choose and subscribe to the service items they need, making it more flexible than IBM solutions, especially in data analytics or data governance.
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
The integration within Google Cloud Platform is very good.
 

Categories and Ranking

Azure Stream Analytics
Ranking in Streaming Analytics
3rd
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
26
Ranking in other categories
No ranking in other categories
Google Cloud Dataflow
Ranking in Streaming Analytics
7th
Average Rating
7.8
Reviews Sentiment
7.3
Number of Reviews
12
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Azure Stream Analytics is 10.4%, down from 12.7% compared to the previous year. The mindshare of Google Cloud Dataflow is 7.4%, up from 7.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

SantiagoCordero - PeerSpot reviewer
Native connectors and integration simplify tasks but portfolio complexity needs addressing
There are too many products in the Azure landscape, which sometimes leads to overlap between them. Microsoft continuously releases new products or solutions, which can be frustrating when determining the appropriate features from one solution over another. A cost comparison between products is also not straightforward. They should simplify their portfolio. The Microsoft licensing system is confusing and not easy to understand, and this is something they should address. In the future, I may stop using Stream Analytics and move to other solutions. I discussed Palantir earlier, which is something I want to explore in depth because it allows me to accomplish more efficiently compared to solely using Azure. Additionally, the vendors should make the solution more user-friendly, incorporating low-code and no-code features. This is something I wish to explore further.
Jana Polianskaja - PeerSpot reviewer
Build Scalable Data Pipelines with Apache Beam and Google Cloud Dataflow
As a data engineer, I find several features of Google Cloud Dataflow particularly valuable. The ability to test solutions locally using Direct Runner is crucial for development, allowing me to validate pipelines without incurring the costs of full Dataflow jobs. The unified programming model for both batch and streaming processing is exceptional - requiring only minor code adjustments to optimize for either mode. This flexibility extends to language support, with robust implementations in both Java and Python, allowing teams to leverage their existing expertise. The platform's comprehensive monitoring capabilities are another standout feature. The intuitive interface, Grafana integration, and extensive service connectivity make troubleshooting and performance tracking highly efficient. Furthermore, seamless integration with Google Cloud Composer (managed Airflow) enables sophisticated orchestration of data pipelines.
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Top Industries

By visitors reading reviews
Computer Software Company
16%
Financial Services Firm
14%
Manufacturing Company
9%
Retailer
5%
Financial Services Firm
18%
Manufacturing Company
12%
Retailer
12%
Computer Software Company
11%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

Which would you choose - Databricks or Azure Stream Analytics?
Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...
What is your experience regarding pricing and costs for Azure Stream Analytics?
I have no problem with pricing. We sell the data analytics value and operational value to customers, focusing on productivity and efficiency from the cloud, rather than just the infrastructure or p...
What needs improvement with Azure Stream Analytics?
There is a lack of technical support from Microsoft's local office, particularly in Taiwan. We often have to learn online, and language can be a communication barrier since not many IT staff can sp...
What do you like most about Google Cloud Dataflow?
The product's installation process is easy...The tool's maintenance part is somewhat easy.
What is your experience regarding pricing and costs for Google Cloud Dataflow?
Google Cloud Dataflow costs are primarily driven by compute resources (worker type and count) and data volume. However, other factors like pipeline complexity also contribute significantly to the t...
What needs improvement with Google Cloud Dataflow?
Apache Beam represents a powerful data processing solution that deserves wider recognition in the broader tech community. This technology offers remarkable capabilities for handling data at scale, ...
 

Also Known As

ASA
Google Dataflow
 

Overview

 

Sample Customers

Rockwell Automation, Milliman, Honeywell Building Solutions, Arcoflex Automation Solutions, Real Madrid C.F., Aerocrine, Ziosk, Tacoma Public Schools, P97 Networks
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Find out what your peers are saying about Azure Stream Analytics vs. Google Cloud Dataflow and other solutions. Updated: March 2025.
845,406 professionals have used our research since 2012.