No. of Recommendations: 1
Trying to understand Elastic better, I am trying to find examples of where Elastic can have a differentiator/advantage over other companies? What I'm reading is Elastic was built from the ground up for full text search. They are now applying that to big data.

When I look for examples of search in big data, I keep finding examples of where Elastic is used as a site search engine rather than search being used in unstructured data for example.

Here is the best example I have found. It still seems like an example of enterprise or site search, but they are calling it a big data problem. What I'm trying to figure out is, if Elastic was built from the ground up for full-text search, if there is a rise of IoT (this causes a huge amounts of big data) are there cases where full-text search engine or Elastic would be the ideal solution for that:

Maruti Techlabs is using Elasticsearch for improving the user experience in searching data of used car parts for our client based in Austin, Texas. A potential customer can find ‘used parts’ for his car on this portal. A huge amount of data (around 42 million data) affects the usability of the system performance and query response time. If a search requires data entities from a large data set, you could see a significant drag in query performance. Standard tools like Relational Database Management Systems (RDBMS) are not suited for real-time big data analysis and dynamic conditions leading to time-outs. Thus, a complex search involves a mix of traditional databases from numerous vendors consisting of structured and unstructured data. For this client, Maruti Techlabs chose Elasticsearch as the secondary data layer component. We have separate services for data import and result computation. So when data from vendors is maintained in SQL server it is simultaneously fed into Elasticsearch. Using Elasticsearch query response time was significantly reduced from 7.06 seconds to 4.75 seconds. Scalability is another additional benefit of this new architecture. Leveraging Elasticsearch to build the data infrastructure has made it easier to linearly scale as new data nodes are added in the future.

How is being good at search an advantage if they are going into data analytics?
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