Building a Search Engine to Improve User Engagement
summary
Describes building a search engine that combines Amazon Neptune (graph database) with Amazon OpenSearch Service (full-text search) to power social-network–style interactions and analyze user relationships and attributes. Explains using the graph for interaction analysis and OpenSearch to find attribute patterns, deployed with high-availability schemas and integrable as microservices for web and mobile apps.
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chapters (20)
- 0:00Introduction
- 0:56Current Situation
- 2:57What is a Graph?
- 3:25Social Networks—Knowledge Graphs
- 4:31How to Create a Knowledge Graph?
- 5:19Example of a Graph for a Social Network
- 9:54Our Example
- 11:26Query Examples
- 15:27Aggregation Examples
- 17:17Text Search Examples
- 18:22Main Challenges
- 19:12Benefits of OpenSearch
- 24:05Integrating Neptune’s Full-Text Search
- 24:44Full-Text Search Features
- 25:49Pre-configuration
- 27:36Neptune in OpenSearch
- 28:13Gremlin Query and OpenSearch
- 29:30OpenSearch Data Discovery
- 31:12Querying OpenSearch in AWS Lambda
- 32:24OpenSearch Dashboards
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