Search technologies drive text analytics : Solr vs. Elasticsearch
With enterprises that produce large quantities of data there is a growing need for better enterprise search solutions. With the availability of Lucene, Solr and Elasticsearch over the last 10 years, dealing with the challenges of finding content these solutions help in more ways than you realize. Whether your company needs a solution for sentiment analysis, text analytics or advanced faceted search technologies, Solr and Elasticsearch provide a great solution to meet multiple requirements.
Understanding how important text mining/analytics and search technologies are for current enterprise-level businesses, you only need to look at the volume of data that is created across the multitude of various content creation platforms. Most businesses employ many different internal and external software solutions for everything from accounting to social media marketing and industry specific examples such as autocad for digital drawings and engineering.
To put this into context in 2011, IDC Digital Universe reported that 1.8 zettabytes of data will be created in that year alone. This much information could fill almost 58 billion, 32GB iPads and the fact that most of this information is being created for people instead of by people (being individuals taking pictures, tweeting), is creating a big data overload.
An Expectation of Access
Since the Google era began, there is also a perception that data and information can and should be immediately available through simple keyword search. Of course in an enterprise that creates billions of pieces of content, being able to provide adequate search technologies to reach every application and software environment that is employed by the business without Solr or Elasticsearch is almost impossible.
Big business has become enamored with the idea that big data can provide answers for issues concerning the company through analytics such as sentiment analysis or text analytics. Being able to understand the opinion of consumers at large and in specific relation to your product, brand or company is of ultimate importance to many boards of directors. When it is estimated that poor data analysis can cost companies 20 to 35% of their operating revenue, the importance of the services can be understood.
Search technologies drive Text Analytics
It is estimated that almost 80% of enterprise relevant information derives from unstructured text based sources. Keeping track of these unstructured sources and extracting the necessary information can only be made available through workflow and search technologies. Search technologies like Lucene/Solr and Elasticsearch have provided businesses with a long term solution to these search issues.
Although both Solr and Elasticsearch are built on the same Apache Lucene search engine, Elasticsearch was developed more recently and is better equipped to deal with the major shift towards cloud computing and big data storage. With the more recent development that Elasticsearch enjoys, there are number of issues that it can handle that Solr has been late to the game. Some of these include:
- Create new indices on the fly.
- Deal with replication and sharding transparently for the client.
- Produce multiple schemata, per document type and make documents updatable.
- Allow defined relationships between documents.
- Retrieve documents by ID in near real time.
Being able to have a search technology with an out-of-the-box solution to search multiple data sources from both structured and unstructured text sources is why solutions that include Elasticsearch are easier to integrate and manage than Solr.
Whether you need text analytics, custom search technologies or sentiment analysis solutions, SearchBlox is able to provide you with a great solution that serves multiple purposes. Find out more by contacting us.