HBase vs MapD: 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, MapD is detailed as "Open source GPU-Powered Database". Interactively query and visualize massive datasets with the parallel power of GPUs.
HBase and MapD can be categorized as "Databases" tools.
"Performance" is the top reason why over 7 developers like HBase, while over 2 developers mention "Super fast, and the approach taken" as the leading cause for choosing MapD.
HBase and MapD are both open source tools. HBase with 2.91K GitHub stars and 2.01K forks on GitHub appears to be more popular than MapD with 1.91K GitHub stars and 271 GitHub forks.
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What is HBase?
What is MapD?
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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.