This README provides an overview of the tasks related to building a data flow for tracking and reporting using historical weather data. The objective is to demonstrate the technical ability to set up the data flow using various tools and technologies.

This task involves building a representative dataflow to demonstrate technical proficiency in data processing. A fictional and nonsensical weather report has been selected as the dataset for this purpose. The goal is to showcase the ability to construct a dataflow.

• Export historical weather data (last 7 days) for the cities of Hamburg, Berlin, and Munich using a Google Cloud Function from a weather API. Save this data in Google BigQuery.
• Utilize dbt (https://www.getdbt.com/) to create a reporting view containing the following data:
  • Cumulative average temperature for the cities of Berlin, Hamburg, and Munich.
  • Cumulative average precipitation amount for the cities of Berlin, Hamburg, and Munich.
  • Devise a fantasy score for the weather in each of the 3 cities (non-cumulative) and calculate it.
• Develop a dashboard in Google Looker Studio that presents the values over time for weather fantasy score, temperature, and precipitation amount.

Through these steps, the task aims to demonstrate the construction of a dataflow, integrating data collection, transformation, and visualization using tools like Google Cloud, dbt, and Looker Studio.

Task 1: Setting Up BigQuery Database and Table

Before exporting historical weather data from the weather API, you need to set up the required BigQuery database and table to store the data.

Steps:

1. Create a BigQuery Project:

• If you don't already have one, create a project in Google Cloud Console and enable the BigQuery API.

2. Create a Dataset:

• Inside your project, create a new dataset to organize your weather data.

3. Design the Table Schema:

• Define the schema of the table that will hold the historical weather data. This schema should match the structure of the data you'll retrieve from the weather API. For example:

city (STRING)
date (DATE)
temperature (FLOAT)
precipitation (FLOAT)
... (other relevant fields)

4. Create the Table:

• Using the defined schema, create a new table within your dataset to store the historical weather data.

Task 2: Export of Historical Weather Data

With the BigQuery database and table set up, you can now proceed to export historical weather data from the weather API and store it in BigQuery using a Google Cloud Function.

Steps:

1. Set up a Google Cloud Function:

• Create a new Google Cloud Function project.
• Configure the necessary permissions and authentication for accessing the weather API.

2. Fetch and Transform Data:

• Write a function to fetch historical weather data for the specified cities from the weather API.
• Transform and format the data to match the schema of the BigQuery table you've created.

3. Insert Data into BigQuery:

• Utilize the BigQuery API to insert the transformed weather data into the appropriate table within your dataset.

Task 3: Create Reporting-View with dbt

In this task, you will use dbt (data build tool) to create a reporting view containing aggregated data.

Steps:

1. Set Up dbt Cloud IDE:

• Access dbt Cloud IDE and create a new project.

2. Define Models and Transformations:

• Create dbt models to aggregate and calculate the required metrics using the data in your BigQuery table.

3. Execute dbt Run:

• Run the dbt transformation process to build the reporting view.

Task 4: Create Dashboard in Google Looker Studio

In this task, you will create a dashboard using Google Looker Studio to visualize the weather data metrics over time. Steps:

1. Set Up Looker Studio:

• Access Google Looker Studio and create a new dashboard project.

2. Create Visualizations:

• Design visualizations to display the metrics calculated by dbt over time.

3. Configure Data Sources:

• Connect Looker Studio to Google BigQuery and ensure access to the dbt-created reporting view.

4. Build Dashboard:

• Assemble the visualizations into a coherent dashboard layout and configure any necessary filters and date ranges for interactive exploration.