Google Cloud Dataflow vs Hadoop: What are the differences?
Google Cloud Dataflow: A fully-managed cloud service and programming model for batch and streaming big data processing. Google Cloud 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; Hadoop: Open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Google Cloud Dataflow belongs to "Real-time Data Processing" category of the tech stack, while Hadoop can be primarily classified under "Databases".
Hadoop is an open source tool with 9.27K GitHub stars and 5.78K GitHub forks. Here's a link to Hadoop's open source repository on GitHub.
According to the StackShare community, Hadoop has a broader approval, being mentioned in 237 company stacks & 127 developers stacks; compared to Google Cloud Dataflow, which is listed in 32 company stacks and 8 developer stacks.