Elasticsearch vs Hadoop: What are the differences?
Developers describe Elasticsearch as "Open Source, Distributed, RESTful Search Engine". Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). On the other hand, Hadoop is detailed as "Open-source software for reliable, scalable, distributed computing". The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Elasticsearch can be classified as a tool in the "Search as a Service" category, while Hadoop is grouped under "Databases".
"Powerful api" is the primary reason why developers consider Elasticsearch over the competitors, whereas "Great ecosystem" was stated as the key factor in picking Hadoop.
Elasticsearch and Hadoop are both open source tools. It seems that Elasticsearch with 42.4K GitHub stars and 14.2K forks on GitHub has more adoption than Hadoop with 9.27K GitHub stars and 5.78K GitHub forks.
According to the StackShare community, Elasticsearch has a broader approval, being mentioned in 2003 company stacks & 979 developers stacks; compared to Hadoop, which is listed in 237 company stacks and 127 developer stacks.
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