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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
Google Cloud Dataflow is valued for its efficiency, with organizations reporting notable cost savings and up to 70% time savings.
 

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's customer service varies, with some experiencing slow support while others benefit from proactive communication and dedicated managers.
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 offers robust, seamless scalability with auto-scaling, though some users note cost considerations for expansive usage.
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 rated for stability, being reliable with no performance issues due to Google's robust foundation.
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.
Enhance integration, error management, cost optimization, scalability, and promote broader adoption of Google Cloud Dataflow with improved 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 seen as affordable and competitive, with pricing influenced by machine type and data volume.
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 and scalability with batch/streaming capabilities, supporting multiple languages for flexible, cost-efficient processing.
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 March 2025, in the Streaming Analytics category, the mindshare of Azure Stream Analytics is 11.1%, down from 12.8% compared to the previous year. The mindshare of Google Cloud Dataflow is 7.4%, up from 6.9% 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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842,145 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Computer Software Company
16%
Financial Services Firm
14%
Manufacturing Company
10%
Retailer
6%
Financial Services Firm
17%
Manufacturing Company
12%
Retailer
11%
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?
When I purchased the product, it was comparable to other Azure products. From my point of view, it should be cheaper now, considering the years since its release. The Azure solution is better now, ...
What needs improvement with Azure Stream Analytics?
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 determini...
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.
842,145 professionals have used our research since 2012.