diff --git a/main.py b/main.py
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+++ b/main.py
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+import requests
+import pandas as pd
+import os
+from google.cloud import storage
+from google.cloud import bigquery
+import datetime
+import json
+import settings
+
+
+def fetch_api_data(url: str) -> dict:
+    """Fetches data from the specified API URL and returns the JSON response.
+
+  Args:
+      url: The URL of the API endpoint.
+
+  Returns:
+      The JSON data parsed from the API response, or raises an exception on error.
+
+  Raises:
+      requests.exceptions.HTTPError: If the API request fails.
+  """
+
+    response = requests.get(url)
+    response.raise_for_status()  # Raise exception for non-200 status codes
+
+    return response.json()
+
+
+def get_weather_data(locations: dict, api_key: str) -> dict:
+    """ Fetches current and forecasted weather data for multiple locations.
+
+  Args:
+      locations (dict): A dictionary containing location information.
+          Each key represents a location name, and the value is another dictionary
+          with 'lat' and 'lon' keys for latitude and longitude.
+      api_key (str): Your OpenWeatherMap API key.
+
+  Returns:
+      tuple: A tuple containing two dictionaries, one for current weather data
+          keyed by location name, and another for forecasted weather data
+          keyed by location name.
+
+  Raises:
+      requests.exceptions.RequestException: If an error occurs during API requests.
+  """
+
+    base_url = "https://api.openweathermap.org/data/2.5/"
+    weather_data = {"current": {}, "forecast": {}}
+
+    for location_name, location in locations.items():
+        try:
+            current_url = f"{base_url}weather?lat={location['lat']}&lon={location['lon']}&appid={api_key}&units=metric"
+            forecast_url = f"{base_url}forecast?lat={location['lat']}&lon={location['lon']}&appid={api_key}&units=metric"
+
+            weather_data["current"][location_name] = fetch_api_data(current_url)
+            weather_data["forecast"][location_name] = fetch_api_data(forecast_url)
+        except requests.exceptions.RequestException as e:
+            print(f"Error fetching data for location {location_name}: {e}")
+
+    return weather_data
+
+
+def transform_current_weather_data(data_dict: dict) -> pd.DataFrame:
+    """Transforms weather API data into a Pandas DataFrame suitable for BigQuery.
+
+  Args:
+      data_dict (dict): A dictionary containing weather data.
+      is_current_weather (bool, optional): If True, assumes data represents
+          current weather and selects the first element. Defaults to False.
+
+  Returns:
+      pd.DataFrame: A Pandas DataFrame containing the transformed weather data.
+  """
+
+    # Preprocess the 'weather' column (in some cases the API returns a list instead of a dictionary)
+    if isinstance(data_dict['weather'], list):
+        data_dict['weather'] = data_dict['weather'][0]
+
+    # Flatten data structure
+    flattened_data = {}
+    for key, value in data_dict.items():
+        if isinstance(value, dict):
+            for sub_key, sub_value in value.items():
+                flattened_data[f"{key}_{sub_key}"] = sub_value
+        else:
+            flattened_data[key] = value
+
+    data_df = pd.DataFrame([flattened_data])
+    if 'dt' in data_df.columns:
+        data_df['dt_txt'] = pd.to_datetime(data_df['dt'], unit='s')
+        data_df['dt_txt'] = data_df['dt_txt'].dt.strftime('%Y-%m-%d %H:%M:%S')
+
+    return data_df
+
+
+def convert_weather_api_dict_to_dataframe(data_dict: dict) -> pd.DataFrame:
+    """
+  Converts a nested dictionary containing weather data from the Weather API to a Pandas DataFrame.
+
+  Args:
+      data_dict (dict): The dictionary containing the weather data.
+
+  Returns:
+      pd.DataFrame: A DataFrame representing the weather data.
+  """
+
+    extracted_data = {}
+    for key, value in data_dict.items():
+        if isinstance(value, dict):
+            for sub_key, sub_value in value.items():
+                extracted_data[f"{key}_{sub_key}"] = sub_value
+        else:
+            extracted_data[key] = value
+
+    return pd.DataFrame([extracted_data])
+
+
+def transform_forecasted_weather_data(data_dict: dict) -> pd.DataFrame:
+    """
+  Transforms the forecasted weather data from the Weather API into a Pandas DataFrame.
+
+  Args:
+      data_dict (dict): The dictionary containing the forecasted weather data.
+
+  Returns:
+      pd.DataFrame: A DataFrame containing the transformed forecasted weather data.
+  """
+
+    city_dict = data_dict['city']
+    city_df = convert_weather_api_dict_to_dataframe(city_dict)
+
+    forecasts_dict = data_dict['list']
+    forecast_df = pd.DataFrame()
+    for forecast_item in forecasts_dict:
+        forecast_item['weather'] = forecast_item['weather'][0]
+        forecast_item_df = convert_weather_api_dict_to_dataframe(forecast_item)
+        forecast_df = pd.concat([forecast_df, forecast_item_df], ignore_index=True)
+
+    # Merge forecast_df with city_df into a single DataFrame and return the result
+    # Since city_df has only one row, we use 'cross' join type to combine each row from forecast_df with the single row from city_df.
+    return forecast_df.merge(city_df, how='cross')
+
+
+def upload_json_to_gcs(json_data: dict, bucket_name: str, folder_path: str) -> None:
+    """Uploads a JSON object to Google Cloud Storage.
+
+  Args:
+      json_data (dict): The JSON data to upload.
+      bucket_name (str): The name of the bucket to upload the data to.
+      folder_path (str): The folder path within the bucket to store the data (optional).
+  """
+
+    if settings.is_local_environment:
+        os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = settings.cloud_storage_service_account
+
+    client = storage.Client()
+
+    try:
+        bucket = client.get_bucket(bucket_name)
+    except:
+        bucket = client.create_bucket(bucket_name)
+
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+
+    filename = f"{timestamp}.json"
+
+    object_path = os.path.join(folder_path, filename)  # Using os.path.join for cleaner path handling
+
+    blob = bucket.blob(object_path)
+    blob.upload_from_string(json.dumps(json_data).encode("utf-8"), content_type="application/json")
+
+    print(f"Uploaded {filename} to {bucket_name}/{object_path}")
+
+
+def upload_df_to_bigquery(dataframe: pd.DataFrame, project_id: str, dataset_id: str, table_name: str):
+    """Uploads a pandas DataFrame to a BigQuery table.
+
+  Args:
+      dataframe (pd.DataFrame): The pandas DataFrame to upload.
+      project_id (str): Your GCP project ID.
+      dataset_id (str): The ID of the BigQuery dataset where the table will be created.
+      table_name (str): The name of the BigQuery table to create.
+
+  Returns:
+      None
+  """
+    if settings.is_local_environment:
+        os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = settings.bigquery_service_account
+
+    client = bigquery.Client()
+    dataset_id = f"{project_id}.{dataset_id}"
+
+    dataset = bigquery.Dataset(dataset_id)
+    dataset.location = "europe-west8"
+    try:
+        dataset = client.create_dataset(dataset, timeout=30)  # Make an API request.
+        print("Created dataset {}.{}".format(client.project, dataset.dataset_id))
+    except:
+        print("Dataset already exists")
+
+    table_id = f"{dataset_id}.{table_name}"
+
+    job_config = bigquery.LoadJobConfig(
+        autodetect=True,
+        write_disposition='WRITE_TRUNCATE',  # 'WRITE_APPEND',
+        create_disposition='CREATE_IF_NEEDED'  # ,
+        # range_partitioning = bigquery.RangePartitioning(
+        #    field="id", # [Important!] Partition by location id to store only the latest forecast for each location
+        #    range_=bigquery.PartitionRange(interval=1),
+        # )
+    )
+
+    print("Created a BigQuery job_config variable")
+
+    try:
+        job = client.load_table_from_dataframe(dataframe, table_id, job_config=job_config)
+        job.result()  # Wait for the job to complete.
+        print("Saved data into BigQuery")
+    except Exception as e:
+        print(dataframe.dtypes)
+        print(table_id)
+        print(job_config)
+        print(e)
+        raise e
+
+
+def main(request: dict) -> str:
+    """
+  This function retrieves weather data for specified locations, transforms it
+  into DataFrames for current and forecast weather. It then, uploads the raw JSON data
+  to Google Cloud Storage and the transformed data to BigQuery
+
+  Args:
+      request (json): A dictionary containing the body of the POST request that triggered the Cloud Function
+
+  Returns:
+      A string message "200, Success" indicating successful execution of the function
+  """
+
+    try:
+        request_body = request.get_json()
+    except:
+        request_body = json.loads(request)
+
+    api_key = settings.api_key
+
+    locations_dict = {
+        'Thessaloniki, GR': {'lat': '40.6403', 'lon': '22.9439'},
+        'Paris, FR': {'lat': '48.85341', 'lon': '2.3488'},
+        'London, GB': {'lat': '51.50853', 'lon': '-0.12574'},
+        'Dubai, AE': {'lat': '25.276987', 'lon': '55.296249'},
+        'Los Angeles, US': {'lat': '34.0522', 'lon': '-118.2437'},
+    }
+
+    weather_data = get_weather_data(locations_dict, api_key)
+
+    current_weather = pd.DataFrame()
+    forecast_weather = pd.DataFrame()
+
+    for location, data in weather_data['current'].items():
+        transformed_data = transform_current_weather_data(data)
+        current_weather = pd.concat([current_weather, transformed_data], ignore_index=True)
+
+    for location, data in weather_data['forecast'].items():
+        transformed_data = transform_forecasted_weather_data(data)
+        forecast_weather = pd.concat([forecast_weather, transformed_data], ignore_index=True)
+
+    bucket_name = settings.bucket_name
+
+    for location, data in weather_data['current'].items():
+        folder_path = f"current_weather/{location}"
+        upload_json_to_gcs(data, bucket_name, folder_path)
+
+    for location, data in weather_data['forecast'].items():
+        folder_path = f"forecasted_weather/{location}"
+        upload_json_to_gcs(data, bucket_name, folder_path)
+
+    upload_df_to_bigquery(dataframe=current_weather, project_id=settings.project_id, dataset_id=settings.dataset_id,
+                          table_name='current_weather')
+    upload_df_to_bigquery(dataframe=forecast_weather, project_id=settings.project_id, dataset_id=settings.dataset_id,
+                          table_name='forecasted_weather')
+
+    return '200, Success'
+
+
+if __name__ == "__main__":
+    data = {}
+    payload = json.dumps(data)
+    print(main(payload))
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diff --git a/settings.py b/settings.py
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--- /dev/null
+++ b/settings.py
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+# This variable will be True only when running the code from our local machine
+is_local_environment = True # Set to False before deploying to Cloud Functions
+
+cloud_storage_service_account = "cloud-storage-admin-service-account.json"
+bigquery_service_account = "bigquery-admin-service-account.json"
+
+api_key = 'KEY'
+project_id = 'ID'
+dataset_id= 'ID'
+bucket_name = 'NAME'
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diff --git a/trigger_cloud_function.py b/trigger_cloud_function.py
new file mode 100644
index 0000000..1595e80
--- /dev/null
+++ b/trigger_cloud_function.py
@@ -0,0 +1,18 @@
+
+import requests
+
+url = "URL"
+
+# Optional: Add data to be sent in the POST request body (as a dictionary)
+data = {"": ""}
+
+response = requests.post(url, json=data)
+
+if response.status_code == 200:
+  print("POST request successful!")
+  print(response.text)
+  # Access the response content (optional)
+  # print(response.text)
+else:
+  print(f"Error: {response.status_code}")
+  print(response.text)
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