LexisNexis Presenter Says Solr Is Essential to Handling Data at Scale

LexisNexis spent much of the last year rebuilding its search abilities and migrating to Solr. The ongoing project manages more…

LexisNexis spent much of the last year rebuilding its search abilities and migrating to Solr. The ongoing project manages more than 3 petabytes of data and has helped LexisNexis develop a data-driven culture through integration with other critical home-grown functions to improve search.

LexisNexis’ goals were to:

• Build a best-in-class search experience by migrating search to an open source engine.
• Deliver dramatically faster search performance for products
• Enable a faster path to delivery of search improvements.

 Their activities to accomplish these goals is in progress and includes:

• Moving to a modern open source search engine that enables faster innovation, is cloud friendly and has lower operational costs.
• Implementing architecture that ensures search response time under a second and is noticeably faster than the competition.
• Deploying development continuously with higher quality and frequency, enable faster A/B testing, experimentation and automation.

They’re seeing some success – ingesting content at scale with a continuous integration and deployment pipeline that allows them to check data and statistics in real time.

Part of their process is to test – constantly checking data and statistics on ingestion rates and relevancy of search results.

Dennis Chaney, the program manager, says that these changes are already driving innovation – through cost savings and faster development and testing cycles – all to serve customers better.