Enriching postal addresses with Elastic stack

A presentation at Big Data LDN in in London, UK by David Pilato

David Pilato
David Pilato
@dadoonet

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.

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:

{
  "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:

{
  "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.

Resources

The following resources were mentioned during the presentation or are useful additional information.

Code

The following code examples from the presentation can be tried out live.