Need advice about which tool to choose?Ask the StackShare community!
Google Cloud Dataflow vs Google Cloud Dataproc: What are the differences?
What is 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.
What is Google Cloud Dataproc? A managed Spark and Hadoop service hosted on Google Cloud Platform. It is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. It helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them.
Google Cloud Dataflow belongs to "Real-time Data Processing" category of the tech stack, while Google Cloud Dataproc can be primarily classified under "Big Data Tools".
Some of the features offered by Google Cloud Dataflow are:
- Fully managed
- Combines batch and streaming with a single API
- High performance with automatic workload rebalancing Open source SDK
On the other hand, Google Cloud Dataproc provides the following key features:
- Spin up an autoscaling cluster in 90 seconds on custom machines
- Build fully managed Apache Spark, Apache Hadoop, Presto, and other OSS clusters
- Only pay for the resources you use and lower the total cost of ownership of OSS
According to the StackShare community, Google Cloud Dataflow has a broader approval, being mentioned in 58 company stacks & 100 developers stacks; compared to Google Cloud Dataproc, which is listed in 5 company stacks and 6 developer stacks.
Pros of Google Cloud Dataflow
- Unified batch and stream processing5
- Fully managed3
- Throughput Transparency1