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VulcanSQL vs Keen: What are the differences?
VulcanSQL: Build analytics APIs from parameterized SQL. It is an Analytics API generator that helps data engineers to build scalable analytics APIs using only SQL without writing any backend code; Keen: Keen is the platform that gives you powerful in-product analytics fast with minimal development time. Keen is a powerful set of API's that allow you to stream, store, query, and visualize event-based data. Customer-facing metrics bring SaaS products to the next level with acquiring, engaging, and retaining customers.
VulcanSQL and Keen can be primarily classified as "Custom Analytics" tools.
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Learn MorePros of Keen
Pros of VulcanSQL
Pros of Keen
- Very powerful API57
- Easy setup43
- Great Customer Support31
- Customization24
- Built by developers for developers24
- Dashboards19
- Developer Friendly18
- It's awesome12
- Developer logging11
- Heroku Add-on10
- Github Integration6
- Saved queries5
- Segment Integration4
- Data Collection from any source2
- Very easy to get started. Loads of potential!1
- Good API1
Pros of VulcanSQL
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Cons of Keen
Cons of VulcanSQL
Cons of Keen
- Limited concurrent queries1
Cons of VulcanSQL
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What is Keen?
Keen is a powerful set of API's that allow you to stream, store, query, and visualize event-based data. Customer-facing metrics bring SaaS products to the next level with acquiring, engaging, and retaining customers.
What is VulcanSQL?
It is an Analytics API generator that helps data engineers to build scalable analytics APIs using only SQL without writing any backend code.
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What companies use Keen?
What companies use VulcanSQL?
What companies use Keen?
What companies use VulcanSQL?
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What tools integrate with Keen?
What tools integrate with VulcanSQL?
What tools integrate with VulcanSQL?
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What are some alternatives to Keen and VulcanSQL?
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Bitdeli
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Ahoy
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Snowplow
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Iteratively
Iteratively helps teams capture reliable product analytics they can trust. It eliminates the most common causes of error during the definition and implementation of tracking plans, and cuts down on the time it takes to correctly instrument the product. As a result, folks that consume product analytics get exactly what they spec'd out and can rely on the incoming data knowing it is trustworthy and accurate.