Most modern applications generate large amounts of data in order to understand the needs and likes of their customers. However finding meaningful information from within this data is like finding a needle in a haystack. In this session we will look at some solutions that are being used currently for Big Data Search and then take a closer look at one of the frontrunners, Elasticsearch. Github, FourSquare, StumbleUpon, SoundCloud all use ElasticSearch to analyze and search through terabytes of data and millions of search requests.
In Elasticsearch we will be discussing:
- What is ElasticSearch, how it works.
- How ElasticSearch works to analyze data splitting a document into meaningful portions and indexing each of those portions separately. So whenever a new search request comes in, it knows what to find.
- Features and advantages of ElasticSearch like built in sharding defaults, maintaining fail-safe node clusters, automatically adding a new node without having to reboot and so on.
- Out of the box features for today’s applications like faceted search, reverse search using Percolators and pre-built Analyzers.