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Corral vs Druid: What are the differences?
Developers describe Corral as "A serverless MapReduce framework written for AWS Lambda". Corral is a MapReduce framework designed to be deployed to serverless platforms, like AWS Lambda. It presents a lightweight alternative to Hadoop MapReduce. On the other hand, Druid is detailed as "Fast column-oriented distributed data store". Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
Corral and Druid can be categorized as "Big Data" tools.
Corral and Druid are both open source tools. Druid with 8.31K GitHub stars and 2.08K forks on GitHub appears to be more popular than Corral with 611 GitHub stars and 16 GitHub forks.
Pros of Corral
Pros of Druid
- Real Time Aggregations15
- Batch and Real-Time Ingestion6
- OLAP5
- OLAP + OLTP3
- Combining stream and historical analytics2
- OLTP1
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Cons of Corral
Cons of Druid
- Limited sql support3
- Joins are not supported well2
- Complexity1
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What is Corral?
What is Druid?
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What companies use Corral?
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What tools integrate with Corral?
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