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Thursday, October 18 • 2:05pm - 2:45pm
SQL Analytics for Search Engineers

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Building a modern search application takes more than just tuning queries in Solr. Today's search engineer needs a broad set of tools to aggregate user activity to improve query relevance, generate recommendations, and leverage machine learning models for ranking and content enrichment. In addition, search teams are often asked to integrate diverse data sets into the search experience. At Lucidworks, we've combined the power of Spark SQL and Solr to solve a number of common problems that arise in modern search applications using tried and true SQL. In this talk, I'll show how to use SQL to:

-Aggregate documents in Solr to compute metrics for recommendations and query boosting based on user activity.
-Compute ranking experiment outcomes across variants using SQL.
-Wrangle powerful data to join and index documents from NoSQL databases and other popular big data systems like Cassandra and HBase.
-Self-service analytics with BI tools, such as Tableau / Power BI, using JDBC / SQL.
-Leverage Solr's analytics capabilities, such as facets and streaming expressions, to optimize SQL queries.
-Generate predictions from ML models, such as Spark-NLP, using simple SQL functions.

Attendees will come away from this talk with a solid understanding and examples where they can use SQL to complement their Solr skills in building powerful search experiences.

avatar for Tim Potter

Tim Potter

Manager Smart Data at Lucidworks; Apache Solr Committer / PMC, Lucidworks
Timothy Potter is a senior member of the engineering team at Lucidworks and a committer on the Apache Solr project. Previously, Tim was an architect on the Big Data team at a social media analytics company, where he worked on large-scale machine learning, text mining, and social network... Read More →

Thursday October 18, 2018 2:05pm - 2:45pm EDT
Drummond West