In this project, we leverage a dataset obtained from the Environmental Protection Agency (EPA) via their website.
The dataset, named egrid2022_data, contains valuable information related to energy generation, emissions, and power plants.
Our focus lies on a specific subset of this data, including the tables labeled as UNT22, GEN22, PLNT22, and US22. These tables serve as the foundation for creating a view named “Unadjusted Emissions”.
However, in real-world scenarios access to the main tables will be restricted by database administrators due to sensitive information and government policies. The primary objective of this project is to establish a real-world scenarios with data engineering process for developing a Monitoring Tracker. Here’s our step-by-step approach:
1- Workspace and Data Warehouse Creation:
We begin by setting up a workspace where all project-related activities will take place.
Next, we create a robust data warehouse capable of storing large volumes of data. This warehouse will serve as the central repository for our dataset.
2- Data Loading and Transformation:
Using gen2 (presumably a data loading tool), we load the relevant data into our data warehouse.
We ensure that the data is properly transformed and organized for efficient querying and analysis.
3- Pipeline Setup:
To facilitate seamless data movement, we establish a pipeline connecting the data warehouse to a lakehouse (a hybrid storage system combining data lake and data warehouse features).
The pipeline ensures that updates to the warehouse are reflected in the lakehouse.
4- Notebook Integration:
We open a notebook environment and connect it to the lakehouse.
This connection allows us to access the data and perform exploratory analysis using tools like Spark SQL, R, or Python.
By following this approach, we aim to create an efficient and scalable solution for monitoring emissions data.
The “Unadjusted Emissions” view, provided by the data administrator, this is our starting point for analyses and actionable insights.
Project name
Adjusting Unadjusted Emissions with AI Solution
Description
In this project, we leverage a dataset obtained from the Environmental Protection Agency (EPA) via their website.
The dataset, named egrid2022_data, contains valuable information related to energy generation, emissions, and power plants.
Our focus lies on a specific subset of this data, including the tables labeled as UNT22, GEN22, PLNT22, and US22. These tables serve as the foundation for creating a view named “Unadjusted Emissions”.
However, in real-world scenarios access to the main tables will be restricted by database administrators due to sensitive information and government policies. The primary objective of this project is to establish a real-world scenarios with data engineering process for developing a Monitoring Tracker. Here’s our step-by-step approach:
1- Workspace and Data Warehouse Creation: We begin by setting up a workspace where all project-related activities will take place. Next, we create a robust data warehouse capable of storing large volumes of data. This warehouse will serve as the central repository for our dataset.
2- Data Loading and Transformation: Using gen2 (presumably a data loading tool), we load the relevant data into our data warehouse. We ensure that the data is properly transformed and organized for efficient querying and analysis.
3- Pipeline Setup: To facilitate seamless data movement, we establish a pipeline connecting the data warehouse to a lakehouse (a hybrid storage system combining data lake and data warehouse features). The pipeline ensures that updates to the warehouse are reflected in the lakehouse.
4- Notebook Integration: We open a notebook environment and connect it to the lakehouse. This connection allows us to access the data and perform exploratory analysis using tools like Spark SQL, R, or Python.
By following this approach, we aim to create an efficient and scalable solution for monitoring emissions data. The “Unadjusted Emissions” view, provided by the data administrator, this is our starting point for analyses and actionable insights.
Project Repository URL
https://github.com/DallasBaba/Adjusting-Unadjusted-Emissions-with-AI-Solution
Project video
https://youtu.be/W79WktWjWNQ
Team members
https://github.com/Aminatadesola