HBase vs UnQLite: What are the differences?
Developers describe HBase as "The Hadoop database, a distributed, scalable, big data store". Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop. On the other hand, UnQLite is detailed as "An Embeddable NoSQL Database Engine". UnQLite is a in-process software library which implements a self-contained, serverless, zero-configuration, transactional NoSQL database engine. UnQLite is a document store database similar to MongoDB, Redis, CouchDB etc. as well a standard Key/Value store similar to BerkeleyDB, LevelDB, etc.
HBase and UnQLite can be categorized as "Databases" tools.
HBase and UnQLite are both open source tools. It seems that HBase with 2.91K GitHub stars and 2.01K forks on GitHub has more adoption than UnQLite with 997 GitHub stars and 102 GitHub forks.
What is HBase?
What is UnQLite?
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Why do developers choose UnQLite?
What are the cons of using HBase?
What are the cons of using UnQLite?
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The final output is inserted into HBase to serve the experiment dashboard. We also load the output data to Redshift for ad-hoc analysis. For real-time experiment data processing, we use Storm to tail Kafka and process data in real-time and insert metrics into MySQL, so we could identify group allocation problems and send out real-time alerts and metrics.