Compare - Google Cloud Dataflow VS PipeRider
Here’s the difference between Google Cloud Dataflow and PipeRider. The comparison is based on pricing, deployment, business model, and other important factors.
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.
About PipeRider
With PipeRider you can profile your data sources, create highly customizable data quality assertions, and generate insightful reports. It allows you to define the shape of your data once, and then use the data checking functionality to alert you to changes in your data quality.
Comparison Table
| Overview | ||
|---|---|---|
| Categories | Data Streaming | Data Quality Monitoring |
| Stage | Late Stage | Early Stage |
| Target Segment | Enterprise, Mid size | SMB's, Mid-Size |
| Deployment | SaaS | Open Source |
| Business Model | Commercial | Open Source |
| Pricing | Freemium | Freemium |
| Location | US | Taipei City, Taiwan |
| Companies using it | ||
| Contact info |
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