Overview:

In this session, we will delve into the essential building blocks of data engineering, placing a spotlight on the discovery process. From framing the problem statement to navigating the intricacies of exploratory data analysis (EDA) using Python, VSCode, Jupyter Notebooks, and GitHub, you'll gain a solid understanding of the fundamental aspects that drive effective data engineering projects.

Agenda:

1. Introduction:

- Unveiling the importance of the discovery process in data engineering.

- Setting the stage with a real-world problem statement that will guide our exploration.

2. Setting the Stage:

- Downloading and comprehending sample data to kickstart our discovery journey.

- Configuring the development environment with VSCode and Jupyter Notebooks.

3. Exploratory Data Analysis (EDA):

- Delving deep into EDA techniques with a focus on the discovery phase.

- Demonstrating practical approaches using Python to uncover insights within the data.

4. Code-Centric Approach:

- Advocating the significance of a code-centric approach during the discovery process.

- Showcasing how a code-centric mindset enhances collaboration, repeatability, and efficiency.

5. Version Control with GitHub:

- Integrating GitHub seamlessly into our workflow for version control and collaboration.

- Managing changes effectively to ensure a streamlined data engineering discovery process.

6. Real-World Application:

- Applying insights gained from EDA to address the initial problem statement.

- Discussing practical solutions and strategies derived from the discovery process.

Key Takeaways:

- Mastery of the foundational aspects of data engineering.

- Hands-on experience with EDA techniques, emphasizing the discovery phase.

- Appreciation for the value of a code-centric approach in the data engineering discovery process.

Upcoming Talks:

Join us for subsequent sessions in our Data Engineering Process Fundamentals series, where we will delve deeper into specific facets of data engineering, exploring topics such as data modeling, pipelines, and best practices in data governance.