From 978cbc9731830890a44fcca9c5635584d3499fbc Mon Sep 17 00:00:00 2001 From: Aitor Murguzur Date: Wed, 26 Jun 2024 11:37:28 +0200 Subject: [PATCH] Reorganize "Create a schema" and remove the strikethroughs on "Getting the token" --- docs/Examples/POSTMAN/Getting the token.md | 4 ++-- docs/How-to guides/How to create a schema.md | 21 ++++++++++---------- 2 files changed, 12 insertions(+), 13 deletions(-) diff --git a/docs/Examples/POSTMAN/Getting the token.md b/docs/Examples/POSTMAN/Getting the token.md index 2c7731f..477f3f4 100644 --- a/docs/Examples/POSTMAN/Getting the token.md +++ b/docs/Examples/POSTMAN/Getting the token.md @@ -51,8 +51,8 @@ The token you will need to the authorization, is the id_token. - Navigate to the "Body" tab within the request settings. - Select "form-data" as the body type. - Add the following key-value parameters: - - `username`: ~HTTP USER FROM CSV~ - - `password`: ~PASSWORD FROM CSV~ + - `username`: HTTP USER FROM CSV + - `password`: PASSWORD FROM CSV - `grant_type`: password - `client_id`: biotz-platform-devices - `scope`: openid diff --git a/docs/How-to guides/How to create a schema.md b/docs/How-to guides/How to create a schema.md index 9cdd5c2..95b6a4d 100644 --- a/docs/How-to guides/How to create a schema.md +++ b/docs/How-to guides/How to create a schema.md @@ -70,14 +70,9 @@ Timestamp ones, the same as the rest with an extra one: ![Payload Timestamp](img/payload-timestamp.png) -Once the schema is totally represented the ‘save’ button will register the schema. This will create the necessary machinery for the data validation and ingestion, it will also create the needed database structure for the data to be stored. - - -## Data tranformation -The data transformation functionality allows users to apply scaling and offsetting during data ingestion. -Data transformation includes advanced options such as the application of scaling and offsetting. This feature allows users to adjust their data more precisely during the ingestion process, this will be applicable to the following item types. +To the following item types, **data transformation** is applicable: - Integer - Integer, as text @@ -85,16 +80,16 @@ Data transformation includes advanced options such as the application of scaling - Decimal - Decimal, as text +The data transformation functionality allows users to apply scaling and offsetting during data ingestion. +Data transformation includes advanced options such as the application of scaling and offsetting. This feature allows users to adjust their data more precisely during the ingestion process. + #### How to use the Functionality **Step 1: Access the Ingestion Configuration** -To access the data ingestion flows on the Biotz platform and select the "Data Transformation" option, first, navigate to the left-hand side of the main menu on the Biotz platform and select "Setup". From there, choose "Data Ingestion Flows", then, select the appropriate device type, followed by the corresponding message type, and then click on "New Schema" to start defining a new data schema. - Once on the schema definition screen, select the item type you want to transform, ensuring it is one of the following: integer, integer as text, integer as hexadecimal text, decimal, or decimal as text. After selecting the item type, the "fx" icon will appear next to the item name. Click on this icon to access the data transformation options. -
![Creating panels](img/click-to-tranform1.png)
@@ -123,7 +118,9 @@ For example: Scale: 1.5 + Offset: -2 + This means that each data value will be multiplied by 1.5 and then 2 will be subtracted. To add more transformations click on the "add tranformation" button, once the setup is done, click on "save". Once the configuration is saved the "fx" button will show a yellow circle to show that the data transformation formulas are stored. @@ -132,9 +129,11 @@ To add more transformations click on the "add tranformation" button, once the se ![Creating panels](img/click-to-tranform.png) -**Step 3: Apply Transformations** +
+
+ +Once the schema is totally represented the ‘save’ button will register the schema. This will create the necessary machinery for the data validation and ingestion, it will also create the needed database structure for the data to be stored. -Associate the defined transformation rules with the data ingestion flow. ## Create a schema using the text editor