Google Cloud Dataflow vs Informatica Data Engineering Streaming [EOL] comparison

Google and Informatica are both solutions in the Streaming Analytics category. Google is ranked #9 with an average rating of 8.1, while Informatica is ranked #20 with an average rating of 8.0. Google holds a 4.6% mindshare in SA, compared to Informatica’s 2.2% mindshare. Additionally, 93% of Google users are willing to recommend the solution, compared to 100% of Informatica users who would recommend it.

Google Cloud Dataflow

Read 14 Google Cloud Dataflow reviews

2,251 Views
1,891 Comparison Views

93% willing to recommend

Informatica Data Engineerin...

Read 1 Informatica Data Engineering Streaming [EOL] review

788 Views
426 Comparison Views

100% willing to recommend

Google Cloud Dataflow

Informatica Data Engineerin...

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Executive SummaryUpdated on Dec 11, 2025

Google Cloud Dataflow and Informatica Data Engineering Streaming [EOL] compete in data processing and real-time analytics. Google Cloud Dataflow appears to have the upper hand with its integration within Google Cloud Platform, making it more suitable for environments using Google services.

Features: Google Cloud Dataflow integrates smoothly with other Google services, offering advanced data processing capabilities and supporting real-time analytics, useful for demanding environments. Informatica Data Engineering Streaming [EOL] features extensive data transformation capabilities and offers robust data management, catering to complex data handling needs.

Ease of Deployment and Customer Service: Google Cloud Dataflow leverages Google's cloud infrastructure, enabling straightforward deployment and access to ample documentation. Informatica Data Engineering Streaming [EOL] offers a more hands-on approach to support, potentially beneficial for organizations requiring customized assistance.

Pricing and ROI: Google Cloud Dataflow often entails lower initial setup costs combined with rapid scalability, potentially leading to faster ROI within the Google Cloud ecosystem. Informatica Data Engineering Streaming [EOL] may involve higher upfront investments, yet it can yield significant ROI in environments where its robust feature set delivers value.

To learn more, read our detailed Streaming Analytics Report (Updated: December 2025).

Buyer's Guide

Streaming Analytics

December 2025

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Review summaries and opinions

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

Categories and Ranking

Google Cloud Dataflow

Ranking in Streaming Analytics

9th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Informatica Data Engineerin...

Ranking in Streaming Analytics

20th

Average Rating

8.0

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of December 2025, in the Streaming Analytics category, the mindshare of Google Cloud Dataflow is 4.6%, down from 7.8% compared to the previous year. The mindshare of Informatica Data Engineering Streaming [EOL] is 2.2%, up from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Google Cloud Dataflow	4.6%
Informatica Data Engineering Streaming	2.2%
Other	93.2%

Streaming Analytics

Featured Reviews

Jana Polianskaja

Data Engineer at Accenture

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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Reviewer43443

BI Practice Lead at a tech services company with 51-200 employees

Helps with real-time data processing and improves decision-making overall

It improves decision-making overall for the company. Informatica is usually the tool for setting up the data, streaming the data into your data warehouse from your source, transforming the data, and preparing and modeling it into some desired format. It improves the performance. You need to know how to use it and how to implement it, but it improves performance.

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Quotes from Members

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

Pros

"It is a scalable solution."

"The service is relatively cheap compared to other batch-processing engines."

"The best feature of Google Cloud Dataflow is its practical connectedness."

"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."

"I would rate the overall solution a ten out of ten."

"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."

"The integration within Google Cloud Platform is very good."

"The most valuable features of Google Cloud Dataflow are scalability and connectivity."

More Google Cloud Dataflow pros

"It improves the performance."

Cons

"They should do a market survey and then make improvements."

"There are certain challenges regarding the Google Cloud Composer which can be improved."

"The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability."

"The authentication part of the product is an area of concern where improvements are required."

"The technical support has slight room for improvement."

"Occasionally, dealing with a huge volume of data causes failure due to array size."

"Google Cloud Dataflow should include a little cost optimization."

"The solution's setup process could be more accessible."

More Google Cloud Dataflow cons

"Skill requirement is required. There is a learning curve."

Pricing and Cost Advice

"The solution is cost-effective."

"The tool is cheap."

"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."

"Google Cloud is slightly cheaper than AWS."

"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."

"The solution is not very expensive."

"Google Cloud Dataflow is a cheap solution."

"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."

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Manufacturing Company

12%

Retailer

11%

Healthcare Company

Financial Services Firm

28%

Computer Software Company

Retailer

Media Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	2
Large Enterprise	10

No data available

Questions from the Community

What do you like most about Google Cloud Dataflow?

The product's installation process is easy...The tool's maintenance part is somewhat easy.

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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.

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What needs improvement with Google Cloud Dataflow?

It can be improved in several ways. The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability. Implementing AI-based suggest...

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What needs improvement with Informatica Data Engineering Streaming?

Skill requirement is required. There is a learning curve.

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What is your primary use case for Informatica Data Engineering Streaming?

We implement business intelligence solutions, including data warehousing tools, data integration to load data into warehouses, and then creating reports.

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What advice do you have for others considering Informatica Data Engineering Streaming?

Overall, I would rate the solution an eight out of ten. Usually, Informatica is for big clients because of its pricing, and it also requires some skill sets. It requires investment into a proper da...

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Comparisons

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Compared 29% of the time

Apache Flink vs Google Cloud Dataflow

Compared 20% of the time

Apache NiFi vs Google Cloud Dataflow

Compared 11% of the time

Spring Cloud Data Flow vs Google Cloud Dataflow

Compared 6% of the time

AWS Lambda vs Google Cloud Dataflow

Compared 6% of the time

More Google Cloud Dataflow Competitors

Databricks vs Informatica Data Engineering Streaming [EOL]

Compared 17% of the time

Apache Kafka vs Informatica Data Engineering Streaming [EOL]

Compared 17% of the time

Apache Spark Streaming vs Informatica Data Engineering Streaming [EOL]

Compared 15% of the time

Apache Flink vs Informatica Data Engineering Streaming [EOL]

Compared 13% of the time

Magic xpi Integration Platform vs Informatica Data Engineering Streaming [EOL]

Compared 9% of the time

More Informatica Data Engineering Streaming [EOL] Competitors

Product Reports

Buyer's Guide

Google Cloud Dataflow

December 2025

Download Google Cloud Dataflow product report

Buyer's Guide

Streaming Analytics

December 2025

Informatica Data Engineering Streaming [EOL] report

Download Informatica Data Engineering Streaming [EOL] product report

Also Known As

Google Dataflow

Big Data Streaming, Informatica Intelligent Streaming, Intelligent Streaming

Overview

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.

Google

Informatica Data Engineering Streaming [EOL] is a real-time streaming data processing platform that enables organizations to efficiently leverage continuous data insights from large volumes of diverse data sources.

Informatica Data Engineering Streaming [EOL] offers advanced capabilities for processing streaming data, enabling businesses to harness and analyze information as it's generated. It supports scalability and provides high-performance processing essential for data-driven decision-making. This platform empowers dynamic adaptation to ever-evolving data needs, ensuring timely insights.

What are the key features of Informatica Data Engineering Streaming [EOL]?

Scalability: Designed to handle large data volumes with optimized performance.
Seamless Integration: Connects effortlessly with multiple data sources and destinations.
Real-Time Processing: Enables immediate analysis of streaming data as it arrives.
Data Connectivity: Supports a wide range of connectors for enhanced flexibility.
Fault Tolerance: Ensures consistent operation despite hardware failures.

What benefits should users consider in reviews?

Enhanced Efficiency: Streamlines data processing workflows for faster results.
Cost Savings: Reduces infrastructure and operational costs by optimizing resources.
Improved Decision-Making: Provides timely insights for informed business strategies.
Increased Agility: Adapts quickly to changes in data streams and analytics requirements.

In specific industries, Informatica Data Engineering Streaming [EOL] is implemented to transform manufacturing processes by enabling real-time monitoring and predictive maintenance. In finance, it supports fraud detection and transaction processing by delivering real-time data insights. Retail businesses utilize it to enhance customer experience through personalized recommendations and inventory management.

Informatica

Sample Customers

Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale

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