Enriching Postal Addresses With Elastic Stack
summary
Live coding shows how Elasticsearch, Logstash, Kibana and Filebeat turn messy CSVs into usable location data: a CSV ingest pipeline parses street number, name, zip, city and lat/lon; an index template maps location as a geo_point; queries run distance sorts and a filter-first trick drops latency from seconds to milliseconds. Demo indexes 22 million addresses, enriches ~100k person records via Logstash lookups, builds Kibana Maps, and the pipeline and maps work end to end; repo and configs are provided.
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chapters (17)
- 0:00Introduction
- 1:05Elastic Stack overview
- 2:47BANO address database
- 6:10Setting up Filebeat to ingest address data
- 7:52Ingest pipeline
- 12:31Indexing and testing
- 13:37Search by street/location
- 15:05Index template
- 18:4222M addresses indexed
- 21:12Kibana dashboards
- 23:02Search by user input
- 24:26Closest addresses search
- 25:31Geospatial search optimization
- 27:22Logstash enrichment
- 30:18Corrected data visualization
- 32:27Original vs corrected locations
- 33:56Resources and closing
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