Introduction to ElasticSearch

Search engines are now an integral part of people’s everyday lives. We are used to having access to information at the click of a button. However we rarely think how much work goes into this ability to search for information. Search engine software has become extremely advanced in recent years, now using complex algorithms to provide the most relevant information with predictive search and search suggestion capabilities. Many engines can do this in real-time, processing millions of pieces of information at once. 

One of the most advanced search engines on the market today is ElasticSearch. This product is a full-text search and analytics engine. The engine is built on Apache Lucene – a high-performance text search engine library. Essentially ElasticSearch used Lucene software as its complex backbone and built upon it to enable a quick and easy user interface. Moreover, ElasticSearch goes one step further and offers the user not only the ability to search for indexed data, but also the ability to visualise and analyse using components Kibana and Logstash. ElasticSearch also takes advantage of faceting. Faceted search is more advanced comparing to a text search as it enables a user to apply various filters and use a data classification system for better understanding what data assets an organisation has and where. ElasticSearch is schemaless, enabling business users to gain insight and manipulate data in a much quicker and more convenient manner as they work.  As well as other new and innovative products in this space ElasticSearch has the capability to be scaled to hundreds of servers and handle petabytes of structured and unstructured data. Moreover, ElasticSearch operates under the Apache 2 licence making it fully open-source - users can download it, share it and modify it as they see fit.

There are many great use cases for ElasticSearch for organisations that are struggling to search, explore, govern and analyse large volumes of data in a variety of structures. A number of these can be rapidly tackled thanks to the simplicity of the products deployment options and architecture. Indeed many of the core requirements for building a datalake can often be met using this relatively simple toolset. Some of our favourite use cases include analysing log data within an IT departments application landscape in order to identify processing errors, data leakage and predict risks of downtime.  Similarly within a business environment we have found the toolset extremely useful when tackling complex risk and compliance projects where linkages and facts are often distributed and hidden within a variety of documents and databases. 

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