Compare - Great Expectations VS Google Cloud Dataflow
Here’s the difference between Great Expectations and Google Cloud Dataflow. The comparison is based on pricing, deployment, business model, and other important factors.
About Great Expectations
Great Expectations provides open-source enterprise data management solutions. It helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. It maintains up-to-date data documentation by rendering expectations directly into clean, human-readable documentation. Users can join to GitHub and Slack community groups.
About Google Cloud Dataflow
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.
Comparison Table
| Overview | ||
|---|---|---|
| Categories | Data Quality Monitoring | Data Streaming |
| Stage | Early Stage | Late Stage |
| Target Segment | Mid size | Enterprise, Mid size |
| Deployment | On Prem | SaaS |
| Business Model | Open Source | Commercial |
| Pricing | Freemium | Freemium |
| Location | California, US | US |
| Companies using it | ||
| Contact info |
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