While technologies such as Spark, Hadoop, and Solr have come a long way over the past couple of years, companies continue to struggle to convert all this innovation into successful business outcomes. Too often, big data projects run over budget and fail to deliver ROI. Instead, companies are left with a bloated stack of complex technologies that are cumbersome to maintain and are slow to adapt to new business requirements. Once the consultants have left the building, the big data platform fails to keep up with demands for better access to larger and more complex enterprise data sets.

In this talk, Tim presents a better way to go about big data analytics using Lucidworks Fusion. Attendees will come away with actionable insights to solving common big data problems such as scaling data ingest from any source, providing both full-text search and SQL query capabilities for the same data set, and leveraging machine learning. The goal of this talk is to parse through the hype of big data and show how a lean, tightly integrated stack built on Solr and Spark provides all you need to do big data right.
---
Timothy Potter is a senior member of the engineering team at Lucidworks and PMC member of the Apache Lucene/Solr project. At Lucidworks, Tim leads a team that builds tools to empower business analysts and data scientists to search, analyze, and visualize large-scale enterprise data sets using Fusion. Tim is the original designer of the Spark-Solr open source project and actively contributes to a number of open source projects for integrating Solr with Spark, Storm, YARN, and EC2. When not working on Fusion, Tim also focuses on hardening the distributed features in SolrCloud. Tim is the co-author of Solr In Action.

Websitehttps://twitter.com/thelabdude