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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 offers substantial cost savings and efficiencies, with organizations experiencing 70% time savings and clear financial benefits.
 

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 customer support experiences vary from slow to effective, with proactive updates and dedicated managers enhancing service.
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.
Google's support team is good at resolving issues, especially with large data.
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
7.3
Google Cloud Dataflow is highly rated for scalability, handling large data loads seamlessly and offering dynamic resource optimization.
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.
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
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 can handle large data processing for real-time streaming workloads as they grow, making it a good fit for our business.
 

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
8.2
Google Cloud Dataflow is reliable and stable, with automatic scaling and minor errors in complex, long-running tasks.
They require significant effort and fine-tuning to function effectively.
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
The job we built has not failed once over six to seven months.
The automatic scaling feature helps maintain stability.
 

Room For Improvement

Azure Stream Analytics users face high costs, limited integration, inadequate support, and issues with connectivity, customization, and scalability.
Google Cloud Dataflow improves integrations, but faces challenges in SDK features, support, authentication, cost, and scalability.
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.
I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns.
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 cost-effective and competitive, with expenses aligned to usage, often cheaper than AWS.
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, flexibility, scalability, cost-effectiveness, and powerful event stream processing for real-time insights.
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.
We then perform data cleansing, including deduplications, schema standardizations, and filtering of invalid records.
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
8.0
Reviews Sentiment
7.3
Number of Reviews
13
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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847,772 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Computer Software Company
15%
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?
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?
Pricing is normal. It is part of a package received from Google, and they are not charging us too high.
What needs improvement with Google Cloud Dataflow?
I am not sure, as we built only one job, and it is running on a daily basis. Everything else is managed using BigQuery schedulers and Talend. However, occasionally, dealing with a huge volume of da...
 

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.
847,772 professionals have used our research since 2012.