Expanding Elastic: Learn how anyone can leverage heterogeneous compute to extend and accelerate Elasticsearch Pat McGarry, Vice President of Engineering Al Leyva, Director of Product Management for Analytics Integration Ryft March 7, 2017 @Ryft 

Analytics at the Speed of Your Business § Challenges: § Exponential data growth § Traditional text searching is hindered with complex indexing and transformation requirements § Answers are delayed as legacy architectures require hours or days to find insights Solutions: § Deploy heterogeneous compute on-premise, hybrid or in the cloud § Abstract away the complexities of powerful FPGA-based compute technology § Real-time insights with no indexing or data preparation latency Fast data growth is creating the largest business threats & opportunities since the Internet 

Accelerate and Extend Elasticsearch with Ryft Leverage powerful FPGA- and x86-based heterogeneous compute resources to gain immediate insight without indexing Reduce need for data indexing and transformation, accelerate searches and extend Elasticsearch capabilities to: § Accelerate workflows with the ability to deploy pre-index and post-index searches § Speed search and analysis across unstructured data and JSON, XML, LOGs, CSV, TSV and other files with no transformation § Increase the power of edit distance with user selectable changes to large (>2) distance requests for Fuzzy Hamming or Levenshtein searches. § Enhance wildcard searches to include leading wildcard characters 

Flexible Deployment On-Premise, in the Cloud or in Hybrid Environments Kibana Layer Elasticsearch Layer Lucene Layer ES to Lucene Ryft ONE / AWS F1 Instance ES Plugin mechanism routes requests (fuzziness & metric) The Elastic Stack implements a distributed, JSON-based search and analytics engine: ES to Ryft Primitive Elasticsearch on Ryft can be deployed in your environment, on-premise, hybrid or in the cloud: Deploy via the new Amazon F1 platform. Deploy on-premise or in hybrid environments with the Ryft ONE accelerator. 

Speed search and analysis across unstructured data and JSON, XML, LOGs, CSV, TSV files with no ETL • Using Elasticsearch command • Un-indexed human genome data • Match query with Levenshtein search • Edit distance of 4 

Increase edit distance values beyond 2 using Fuzzy Hamming or Levenshtein searches powered by Ryft • Using Elasticsearch command • Match phrase query with Levenshtein search • With edit distance of 6 • No re-indexing necessary Pharmaceutical Research Pharmaceutical Scien ce r deleted 

Enhance wildcard searches to include leading wildcard characters powered by Ryft • Using Elasticsearch command • Using XML pcap file • Looking for IP addresses xx.0.90.xx • No ETL or indexes necessary 

Thank you! Join us at booth G3 for Q&A or a live demo.