By combining the data that is coming from the client side and contains the sensor's metric value for a given metric at a given time, we can construct an enriched record of the following fields:
- sensorId
- sensorType
- customer
- department
- buildingName
- room
- floor
- locationOnFloor
- latitude
- longitude
- time
- reading
Field numbers 1, 11, and 12 are present in the payload sent by the sensor to our application. The remaining fields are looked up or enriched using the SQL query that we saw in the previous section – using the sensorId. This way, we can generate a denormalized sensor reading record for every sensor for every minute.
We have understood what the application is about and what the data represents. As we start developing the application, we will start the solution from the inside out. It is better to attack the problem at hand at the very heart and try to piece together its core. Elasticsearch is at the core of the Elastic Stack, so we will start defining our solution from it's very heart by first building the data model in Elasticsearch. Let's do that in the next section.