UPDATE: API data sub-flow README

app/api_data/README.md

@@ -1 +1,51 @@
-# Weather API Data to Duckdb
+# Weather API Data to Duckdb:
+
+## Implementation Steps:
+
+1- after creating MotherDuck account, and save token to `.env` file, create a database for ML projects, schema for this project, and table to save hourly API data to motherduck:
+```SQL
+-- create database
+CREATE DATABASE IF NOT EXISTS ml_apps;
+-- use ml_apps
+USE ml_apps;
+-- create schema
+CREATE SCHEMA IF NOT EXISTS weather_forecasting;
+-- create hourly weather api data
+CREATE OR REPLACE TABLE ml_apps.weather_forecasting.hourly_weather_data(
+  location_id INTEGER,
+  reading_timestamp TIMESTAMP_NS,
+  temperature FLOAT NOT NULL,
+  tz VARCHAR(50)
+);
+```
+for more information about duckdb visit the documentation: [duckdb](https://duckdb.org/docs/archive/0.9.2/)
+2- use API url to get the hourly data, and pass parameters to specify the locations, and date, parameters used:
+```JSON
+{
+    "latitude": 30.052723,
+    "longitude": 31.190199,
+    "start_date": start dat,
+    "end_date": end date,
+    "hourly": "temperature_2m",
+    "timezone": "Africa/Cairo",
+}
+```
+this parameters used to get the hourly temperature in cairo every day. 
+to validate the paramaters, before send the request, use pydantic base model to validate the object data:
+```python
+class URLParams(BaseModel):
+    latitude: float
+    longitude: float
+    start_date: str
+    end_date: str
+    hourly: Union[str, List[str]]
+    timezone: str
+``` 
+3- create a sub-flow to get the data from API, transform it if needed, insert it into database, and remove the old records to maintain storage.
+take a look at: [sub-flow](weather_data_flows.py)
+4- add the sub-flow to main [main-flow](../../pred_flow.py)
+5- test it using 
+```bash
+make run-pred-flow
+```
+6- finally, we need to create a schedualed job to run everyday using github actions: [schedualed-job](../../.github/workflows/trigger_pred_flow.yml)