/ Enriching postal addresses with Elastic stack

Description

> Come and learn how you can enrich your existing data with normalized postal addresses with geo location points thanks to open data and [BANO project](http://bano.openstreetmap.fr/data/). Most of the time postal addresses from our customers or users are not very well formatted or defined in our information systems. And it can become a nightmare if you are a call center employee for example and want to find a customer by its address. Imagine as well how a sales service could easily put on a map where are located the customers and where they can open a new shop... Let's take a simple example: ```json { "name": "Joe Smith", "address": { "number": "23", "street_name": "r verdiere", "city": "rochelle", "country": "France" } } ``` Or the opposite. I do have the coordinates but I can't tell what is the postal address corresponding to it: ```json { "name": "Joe Smith", "location": { "lat": 46.15735, "lon": -1.1551 } } ``` In this live coding session, I will show you how to solve all those questions using the Elastic stack with a lot of focus on Logstash and Elasticsearch.

Session 🗣 Introductory and overview ⭐ Track: AI, ML, Bigdata, Python

Slides