Top 10 Google Cloud Dataflow Alternatives

Name: Google Cloud Dataflow
Brand: Google
Rating: 4.0 (14 reviews)

Vendor: Google

4.0 out of 5

14 reviews
93% willing to recommend

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Top Google Cloud Dataflow Competitors

Discover the top alternatives and competitors to Google Cloud Dataflow based on the interviews we conducted with its users. The top alternative solutions include Databricks, Qlik Talend Cloud, and Confluent. The alternatives are sorted based on how often peers compare the solutions. Google Cloud Dataflow surpasses its competitors by offering seamless integration with Google Cloud services, auto-scaling capabilities, and real-time data processing, ensuring efficient and cost-effective management of extensive data pipelines.

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Databricks

4.1 out of 5

Databricks

4.1 out of 5

Databricks excels in analytics and large-scale data processing, offering robust machine learning capabilities and integration with SQL and Python. In comparison, Google Cloud Dataflow is valued for its flexibility and cost-effectiveness, benefiting from Apache Beam for easy language compatibility and scalability.

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Pricing

Databricks typically involves higher setup costs compared to Google Cloud Dataflow, making Dataflow potentially more appealing for those seeking lower initial expenses. In contrast, Databricks might justify costs with its comprehensive features and support offerings.

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Qlik Talend Cloud

4.0 out of 5

Qlik Talend Cloud

4.0 out of 5

Qlik Talend Cloud excels in handling large data volumes with robust integration features, appealing to those needing extensive data management. In comparison, Google Cloud Dataflow offers scalable, user-friendly solutions within the Google ecosystem, ideal for businesses seeking flexible programming language support.

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Pricing

Qlik Talend Cloud offers flexible setup cost options, while Google Cloud Dataflow's setup costs are higher, focusing on enterprise-level integration and scalability. The differences highlight varied target audiences and budget considerations for each platform.

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Confluent

4.1 out of 5

Confluent

4.1 out of 5

Confluent offers seamless Apache Kafka integration, making it ideal for real-time data streaming. In comparison, Google Cloud Dataflow excels with scalability and powerful data transformation tools, justifying its higher cost for complex workflows and benefiting from its comprehensive integration within the Google Cloud ecosystem.

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Pricing

Confluent incurs setup costs that users find straightforward, while Google Cloud Dataflow requires a more complex initial setup. Confluent's simplicity in setup contrasts with Dataflow's detailed configuration, highlighting key differences in user readiness and implementation effort.

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Apache Kafka

4.1 out of 5

Apache Kafka

4.1 out of 5

Google Cloud Dataflow excels in both stream and batch processing with smooth Google Cloud integration. In comparison, Apache Kafka stands out for real-time streaming with a robust event-driven architecture, making it ideal for those seeking high throughput and fault-tolerant data storage.

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Pricing

Google Cloud Dataflow typically incurs a higher setup cost than Apache Kafka, highlighting a significant pricing difference. In some cases, Apache Kafka's lower setup cost may appeal to budget-conscious users seeking data processing solutions.

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Apache Flink

3.9 out of 5

Apache Flink

3.9 out of 5

Google Cloud Dataflow excels in integration with Google's ecosystem, offering automated scaling and managed service. In comparison, Apache Flink is chosen for robust real-time processing and flexible windowing, appealing to teams needing low-latency operations and high throughput in complex data environments.

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Google Cloud Dataflow requires initial setup costs, unlike Apache Flink, which offers a more straightforward setup with zero upfront expenses.

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Google Cloud Dataflow requires initial setup costs, unlike Apache Flink, which offers a more straightforward setup with zero upfront expenses.

Spring Cloud Data Flow

3.9 out of 5

Spring Cloud Data Flow

3.9 out of 5

Google Cloud Dataflow excels in seamless integration with Google's ecosystem for scalable data processing. In comparison, Spring Cloud Data Flow's microservices-based approach offers flexibility in deployment across environments, appealing to buyers needing robust multi-cloud capabilities and adaptable orchestration of data applications.

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Pricing

Google Cloud Dataflow has a higher setup cost compared to Spring Cloud Data Flow, which offers a more affordable initial cost, highlighting a key financial consideration for potential users.

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Google Cloud Dataflow has a higher setup cost compared to Spring Cloud Data Flow, which offers a more affordable initial cost, highlighting a key financial consideration for potential users.

Azure Stream Analytics

3.9 out of 5

Azure Stream Analytics

3.9 out of 5

Google Cloud Dataflow excels in scalable real-time processing and integration for complex needs. In comparison, Azure Stream Analytics stands out with built-in features and seamless Azure integration, making it ideal for users within the Microsoft ecosystem seeking streamlined deployment and effective ROI.

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Pricing

Google Cloud Dataflow has a higher initial setup cost compared to Azure Stream Analytics, which offers a more cost-effective entry point. This difference attracts users looking to minimize upfront expenses, favoring Azure's economical setup appeal.

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Amazon Kinesis

4.0 out of 5

Amazon Kinesis

4.0 out of 5

Google Cloud Dataflow offers unified stream and batch processing, optimizing workloads with minimal intervention. In comparison, Amazon Kinesis excels in real-time streaming and granular control over data streams. Dataflow's seamless integration benefits Google Cloud users; Kinesis suits those needing advanced data stream management.

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Pricing

Google Cloud Dataflow has minimal setup cost with pay-as-you-go pricing, while Amazon Kinesis requires a more substantial initial investment due to its complex integration process.

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Pricing

Google Cloud Dataflow has minimal setup cost with pay-as-you-go pricing, while Amazon Kinesis requires a more substantial initial investment due to its complex integration process.

PubSub+ Platform

4.3 out of 5

PubSub+ Platform

4.3 out of 5

Google Cloud Dataflow stands out for seamless scalability and integration with other Google Cloud services, ensuring efficient data processing. In comparison, PubSub+ Platform excels in robust messaging and event streaming, offering high availability and multi-protocol support, ideal for event-driven architectures.

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Pricing

Google Cloud Dataflow's setup cost is often perceived as lower, while PubSub+ Platform tends to have higher initial setup costs, reflecting distinct pricing strategies and cost considerations between the two cloud solutions.

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Amazon MSK

3.6 out of 5

Amazon MSK

3.6 out of 5

Google Cloud Dataflow excels in streamlined, scalable data processing with seamless Google Cloud integrations, offering cost-efficient pay-as-you-go pricing. In comparison, Amazon MSK provides comprehensive managed Kafka streaming capabilities, featuring a robust structure suitable for enterprises heavily reliant on real-time data solutions.

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Pricing

Google Cloud Dataflow typically involves a higher initial setup cost, while Amazon MSK tends to have a lower entry cost, highlighting a potential consideration for budget-conscious users.

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Pricing

Google Cloud Dataflow typically involves a higher initial setup cost, while Amazon MSK tends to have a lower entry cost, highlighting a potential consideration for budget-conscious users.

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