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Apache Flink vs Google Cloud Dataflow

May 17, 2021

Introduction

Big data processing is a challenging task that involves the execution of complex data workflows on large datasets. However, with the right tools, it becomes more than possible. In this blog post, we'll compare two powerful big data processing frameworks: Apache Flink and Google Cloud Dataflow.

Apache Flink

Apache Flink is an open-source distributed computing platform that enables efficient and high-speed data processing. Flink's core component is its runtime engine, which allows for the processing of streaming data and batch data using the same programming models.

Google Cloud Dataflow

Google Cloud Dataflow is a fully-managed service for developing and executing data processing pipelines. Built using Apache Beam, Dataflow is designed to simplify the process of creating and running big data workflows.

Comparison

When comparing Apache Flink versus Google Cloud Dataflow, several differences and similarities can be highlighted.

Architecture

Both Apache Flink and Google Cloud Dataflow are designed to handle large-scale data processing, but they have different architectural approaches.

Apache Flink uses a distributed stateful streaming architecture, which performs computations on data in real-time. On the other hand, Google Cloud Dataflow uses decoupled processing and state storage, which allows it to be more scalable and fault-tolerant.

Performance

While both Apache Flink and Google Cloud Dataflow can process large datasets, Apache Flink has demonstrated lower latency and higher throughput when it comes to processing real-time data. However, when it comes to batch processing, Dataflow can handle larger datasets more efficiently.

Ease of Use

Both frameworks have their own APIs and tools for developers. While Apache Flink can be integrated with other big data tools such as Hadoop, Dataflow has more user-friendly interfaces and is known for its ease of use.

Pricing

Apache Flink is open-source software and free of charge. Google Cloud Dataflow pricing is based on the number and length of the running jobs, as well as data ingestion and egress rates.

Conclusion

Overall, Apache Flink and Google Cloud Dataflow have their own strengths and weaknesses. For real-time data processing, Apache Flink provides lower latency and higher throughput, while Google Cloud Dataflow is more scalable for batch processing. Ease of use can be subjective and depends on personal preferences.

In the end, choosing between these two platforms depends on the specific use case and requirements of the project.

References

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