Here’s the difference between Google Cloud Dataflow and Apache Spark. The comparison is based on pricing, deployment, business model, and other important factors.
Google Cloud Dataflow is a cloud-based data processing service for both batch and real-time data streaming applications. It enables developers to set up processing pipelines for integrating, preparing and analyzing large data sets, such as those found in Web analytics or big data analytics applications. The Cloud Dataflow software expands on earlier Google parallel processing projects, including MapReduce, which originated at the company. Cloud Dataflow is designed to bring to entire analytics pipelines the style of fast parallel execution that MapReduce brought to a single type of computational sort for batch processing jobs.
Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
Overview | ||
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Categories | Data Streaming | Data Modelling and Transformation |
Stage | Late Stage | Late Stage |
Target Segment | Enterprise, Mid size | Mid Size, Enterprise |
Deployment | SaaS | On Prem |
Business Model | Commercial | Open Source |
Pricing | Freemium | Freemium |
Location | US | US |
Companies using it | ||
Contact info |