Google Cloud Dataflow Reviews

Name: Google Cloud Dataflow
Brand: Google
Rating: 4.0 (11 reviews)

4.0 out of 5

11 reviews
91% willing to recommend

200 followers

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.

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Google Cloud Dataflow is the #8 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 72% 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

Streaming Analytics

January 2025

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Featured 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.

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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.

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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…

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Google Cloud Dataflow mindshare

As of February 2025, the mindshare of Google Cloud Dataflow in the Streaming Analytics category stands at 7.7%, up from 6.8% compared to the previous year, according to calculations based on PeerSpot user engagement data.

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Key learnings from peers

Last updated Feb 9, 2025

Valuable Features

Google Cloud Dataflow offers crucial features such as seamless integration, flexibility across programming languages, and user-friendliness. Its open-source Apache Beam foundation enhances documentation and support. It enables scalable, customizable environments suitable for diverse needs. Dataflow excels in streaming and pipeline tasks with notable features like pay-as-you-go service, connectivity, and significant programming capacity. It supports local testing without costly jobs and integrates well with tools like Grafana and Airflow Composer for efficient monitoring.

"I would rate the overall solution a ten out of ten."
"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."

Room for Improvement

Google Cloud Dataflow could benefit from better integration with Kafka topics and improved UI experience. Users express difficulty with error logging and debugging, particularly due to missing error logs. Enhanced support for Python SDK features, authentication, scalability, and reduced job startup time are areas for improvement. Integration with IT data flow and reduced costs are desired. Technical support could be improved, and promotion outside Google Cloud Platform might increase adoption. Enhancements to setup accessibility and Composer challenges are also needed.

"Promoting the technology more broadly would help increase its adoption."
"The authentication part of the product is an area of concern where improvements are required."
"They should do a market survey and then make improvements."

ROI

Some startups are in the early stages of building their data architecture and cannot fully assess their return yet. Other organizations observe cost savings and find it worth the investment. Google Cloud Dataflow significantly reduces time expenditure, reportedly cutting time requirements by up to 70 percent for certain entities.

Pricing

Google Cloud Dataflow pricing varies based on company size and payment methods. Users generally find it cost-effective and more affordable than alternatives like AWS, rating its price around two to four out of five. Some rate it higher, from seven to eight out of ten, reflecting differing perspectives on expense. Pricing likely depends on machine type and data volume, with many users finding satisfaction in the cost relative to its features.

"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

Companies utilize Google Cloud Dataflow for running batch jobs, building data pipelines, and processing large data batches. They employ it for distributed data processing, data streaming analytics, and migrating on-premise data to BigQuery or Storage Bucket. Organizations use it for data transmission, storage, and analytics workflows, leveraging its capabilities with tools like PubSub and BigQuery. It's used for constructing data pipelines and connecting data, mainly through Python for analytics use cases.

Service and Support

Customers mention challenges in reaching technical support, noting slow response times. Organizations with dedicated technical managers rely on them for assistance. Some users, however, praise Google for stable services and proactive communication, finding no need to engage with support due to seamless operations. Google provides consistent updates and information, ensuring smooth functionality without direct support interactions.

Deployment

Google Cloud Dataflow's initial setup is generally straightforward and quick, taking from a few minutes to hours, even days, depending on the knowledge level and specific environment requirements. It typically requires understanding of the Google Cloud platform, which can be challenging for newcomers. While some find its serverless nature eases the process, others need vendor support for infrastructure and expertise. Users rate its ease between seven and ten out of ten.

Scalability

Google Cloud Dataflow demonstrates strong scalability on the cloud, efficiently managing increasing data and usage. It supports a varying number of users in organizations, performing well with different numbers and types of nodes. Users find it scalable, rating it highly for its adaptability. Auto-scaling features allow for customized processing demands, although cost management can be a concern. Users appreciate how it scales with organizational growth without requiring proportional increases in engineering resources.

Stability

Users consistently find Google Cloud Dataflow stable, highlighting its strong performance and reliability. Many rate its stability highly, with scores ranging from six to nine out of ten. No significant issues are commonly reported, and it is frequently mentioned that it benefits from being backed by Google's robust infrastructure. Multiple users mention satisfaction with its normal stability, showcasing confidence in its consistent performance.

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

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