Plugin information
| Version | 1.0.0 |
|---|---|
| Author | namR |
| Released | 2021-08-03 |
| Last updated | 2021-08-03 |
| License | Apache Software License |
| Source code | Gitlab |
| Reporting issues | Gitlab |
namR store – Data enrichment plugin
Whether you are a business analyst or data engineer, this plugin recipe offers an easy access to the namR store API and data
In this documentation we are sharing all the needed information to work with this plugin recipe :
- Requirements
- Deployment
- Access Token
- Getting started
- namR store
- Contributions
Need support ? Contact [email protected]
Requirements
This plugin has been developped under DSS v7.0.2.
- DSS v7.0.X or above
- Administrator right
- Access token to namR store (see below)
Access token
Our sales team will be glad to get you an acces token to namR store (trial ou premium) [email protected]
In the context of namR store, a token might have three scopes restrictions:
- Geographical scope, eg. one municipality – Clichy insee code 92024
- Data scope: data related to a specific entity, eg. to building characteristics: building construction period, building height
- Limitations and quotas
Getting started
1. Flow
At recipe creation, you will be ask to provide 3 datasets:
- 1 input dataset
- 2 output datasets
Before running the JOB, ACCESS TOKEN must be filled in the settings.
2. Datasets description
Input dataset
This part describes input dataset provided by users.
It requires at least two fields :
Output datasets
This part describes the datasets created by this plugin recipe
Data
This dataset provides all fields available for each row of the input dataset. It, also, gives extra informations an API calls (query and status) for each row processed.
Metadata
This dataset provides meta informations on delivered fields.
namR store
Each row found in the input dataset are processed by the namR store engine with the following pipelines :
Step1: Geocoding
Geocoding is the process of geolocalized a text-based described of a location (address or place name).
For each row, our in-house geocoder will geolocalized your address and provide an internal unique identifier.
Null values are returned for addresses out of the token geographical scope. (see above)
Step2: Link between addresses and entities
Then each address is linked to the requested entities (eg. building).
Step3: Fetch informations
Token data scope are returned for each entity identified.
eg. building -> building height, building construction period …
Step4: Return information
Each information available on the data scope will be returned on a denormalized way.
The above description provide a very simple overview of the namR store engine.
