Google Cloud Dataflow Reviews

Name: Google Cloud Dataflow
Brand: Google
Rating: 4.0 (13 reviews)

Vendor: Google

4.0 out of 5

13 reviews
92% willing to recommend

188 followers

Start review

What is Google Cloud Dataflow?

Google Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

Get the Google Cloud Dataflow Buyer's Guide and find out what your peers are saying about Google Cloud Dataflow, Databricks, Confluent and more!

Google Cloud Dataflow is the #6 ranked solution in Streaming Analytics tools. PeerSpot users give Google Cloud Dataflow an average rating of 8.0 out of 10. Google Cloud Dataflow is most commonly compared to Databricks: Google Cloud Dataflow vs Databricks. Google Cloud Dataflow is popular among the large enterprise segment, accounting for 74% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 17% of all views.

Buyer's Guide

Google Cloud Dataflow

April 2025

Get the report

Helped 849,963 peers since 2012

Featured Google Cloud Dataflow reviews

Jana Polianskaja

Data Engineer at Accenture

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.

Read full review

Amitabha Chakraborty

Data Analyst Manager at a retailer with 10,001+ employees

The solution's setup process is a learning curve for us. It is a bit tricky overall. We receive vendor assistance in terms of infrastructure and expertise to build it. The time taken for deployment depends on the status of the environment, security and governance setups, etc. It takes a few hours to days to complete. Also, it is self-maintained, saving us the cost of it.

Read full review

Arpan Kushwaha

Associate Consultant (Data Engineer) at MediaAgility

We use Google Cloud Dataflow mainly for batch pipelines, like migrating workload from on-premise data movement to BigQuery or Storage Bucket The most valuable features of Google Cloud Dataflow are scalability and connectivity. Google Cloud Dataflow should include a little cost optimization. I…

Read full review

Google Cloud Dataflow mindshare

As of May 2025, the mindshare of Google Cloud Dataflow in the Streaming Analytics category stands at 7.1%, down from 7.2% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Streaming Analytics

PeerResearch reports based on Google Cloud Dataflow reviews

Type	Title	Date
Category	Streaming Analytics	May 1, 2025	Download
Product	Reviews, tips, and advice from real users	May 1, 2025	Download
Comparison	Google Cloud Dataflow vs Databricks	May 1, 2025	Download
Comparison	Google Cloud Dataflow vs Amazon Kinesis	May 1, 2025	Download
Comparison	Google Cloud Dataflow vs Azure Stream Analytics	May 1, 2025	Download

Title	Rating	Mindshare	Recommending
Databricks	4.1	14.6%	96%	89 interviews Add to research
Confluent	4.1	8.2%	95%	23 interviews Add to research

Valuable Features

Google Cloud Dataflow features integration, user-friendly interface, cost-effectiveness, and flexibility for multiple programming languages. Built on Apache Beam, it offers robust documentation, scalability, and connectivity. It supports batch and streaming processing, enabling seamless transition between the two. Its built-in monitoring tools, Grafana integration, and Google Cloud Composer orchestration enhance performance tracking and pipeline management. Event stream processing, data cleansing, transformation, and machine learning model preparation are sophisticated and beneficial capabilities.

"Google's support team is good at resolving issues, especially with large data."
"The integration within Google Cloud Platform is very good."
"I would rate the overall solution a ten out of ten."

Room for Improvement

Google Cloud Dataflow could improve with simpler Kafka integration and better user experiences. Error logging should be enhanced to simplify debugging, and reduced startup times are desirable. Aligning features between Java and Python SDKs is necessary, while technical assistance needs refinement. Cost optimization is important, authentication is complex, and scalability concerns exist. Integration with IT services is needed, along with improved schema design for NoSQL data. Additionally, wider recognition of Apache Beam's capabilities is encouraged.

"I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns."
"Occasionally, dealing with a huge volume of data causes failure due to array size."
"Promoting the technology more broadly would help increase its adoption."

ROI

Many users experienced cost savings with Google Cloud Dataflow, describing it as valuable for its cost. Some noticed significant time savings, approximately 70 percent for their organizations. A startup emphasized that it's too early to determine its full impact on their data architecture, but they have started observing benefits.

Pricing

Google Cloud Dataflow pricing is influenced by factors like compute resources, data volume, and pipeline complexity. Users generally find it cost-effective, with mixed perceptions ranging from affordability to moderate expense compared to other streaming engines. Some rate it near average on the cost scale. As part of Google's broader package, it's deemed competitive with flexibility and scalability in usage. Pricing varies based on organizational needs and payment structures.

"The solution is not very expensive."
"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a seven to eight out of ten."
"On a scale from one to ten, where one is cheap, and ten is expensive, I rate Google Cloud Dataflow's pricing a four out of ten."

Popular Use Cases

Organizations use Google Cloud Dataflow primarily for batch and streaming data processing. They implement data pipelines using Python, integrate with Pub/Sub and BigQuery for comprehensive data analytics, and migrate workloads from on-premise to cloud. Dataflow facilitates event stream processing, real-time alerts, data cleansing, and machine learning model preparation. Retailers and eCommerce platforms leverage it for processing large data batches, enabling efficient data-flow management and data-driven decision-making across multiple departments.

Service and Support

Google Cloud Dataflow support receives mixed feedback. Some customers find technical support hard to reach with slow response times, while others appreciate their quick assistance and effective issue resolution, especially with large data. Proactive updates and the ability to consult directly with Google contribute to positive experiences. Organizations with dedicated managers find it easier to handle technical issues. Overall, customer service and support are perceived as very good by many users.

Deployment

Users find Google Cloud Dataflow's initial setup to range from simple to somewhat complex. Some note a straightforward process due to its serverless nature, while others mention the need for background knowledge of Google Cloud, necessitating setup for billing, permissions, and accounts. Assistance from vendors and IT staff might be needed for infrastructure and security considerations. Deployment times vary, from minutes using CI/CD to taking days with comprehensive setups. Overall, ratings for setup ease vary widely.

Scalability

Google Cloud Dataflow is highly regarded for scalability, efficiently handling data processing needs. Organizations appreciate its cloud-based design and ability to manage large workloads with flexibility. The platform's dynamic resource features allow users to optimize performance and cost, supporting various business needs. Many rate its scalability between eight and nine out of ten, noting it scales well with growing demands and user requirements. It is favored for dynamic scaling capabilities and fine-tuning options.

Stability

Google Cloud Dataflow is widely acknowledged as stable, benefiting from Google's infrastructure. Many users have experienced consistent performance with minimal issues over extended periods. Ratings generally fall between eight and nine out of ten for stability, attributing high marks to reliability and automatic scaling features. Some have noted occasional errors with long-running jobs but find that consultation within teams helps address these. Stability assessments range between six and seven out of ten in a few instances.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Google Cloud Dataflow Buyer's Guide for additional reliable information.