Elasticsearch vs TensorFlow: What are the differences?
Elasticsearch: 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); TensorFlow: Open Source Software Library for Machine Intelligence. TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Elasticsearch can be classified as a tool in the "Search as a Service" category, while TensorFlow is grouped under "Machine Learning Tools".
"Powerful api" is the primary reason why developers consider Elasticsearch over the competitors, whereas "High Performance" was stated as the key factor in picking TensorFlow.
Elasticsearch is an open source tool with 41.9K GitHub stars and 14K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.
Instacart, Slack, and Stack Exchange are some of the popular companies that use Elasticsearch, whereas TensorFlow is used by Uber Technologies, 9GAG, and VSCO. Elasticsearch has a broader approval, being mentioned in 1976 company stacks & 936 developers stacks; compared to TensorFlow, which is listed in 195 company stacks and 126 developer stacks.