Hi,
This weekend, I could not read the second chapter of Designing Data Intensive Applications. I promise to read two chapters and share my notes with you next weekend!
Yesterday, after 1 year and half without participating in “person” races, I attended the Alameda 10-miler. It was great! It felt so good to returning to “normal” in the Covid-19 era! Something that I love about running is the amount of data that we get from it. I was planning to keep my pace at 8 minute per mile and I ended running at 7:45 minute per mile! I have been following the Garmin Coach 5K training and based on my long runs, I was not expecting to get this result. I have to thank my Arete teammates and my brother (IG = @thetechyrunner) who cheered me on from the beginning of the race until the finish line! Their support was my incentive to try to run at my best! Take a look at my Certificate to find out more about my results!
This weekend, I decided to re-take the DataCamp course – Introduction to Airflow with Python. This course has 4 chapters, it is a quick introduction to Airflow main components. The first chapter is about Airflow basic components, what is a Directed Acyclic Graph (DAG), and the main views in the Airflow UI.
My main takeaway from the 1st Chapter is: Data Engineering is complex because it involves designing, managing, and optimizing the data flow to ensure that the organization can access and trust the data. Airflow is a great tool for managing the data flow. It helps us to create, schedule, and monitor workflows (set of steps to accomplish a given data engineering task). Like any tool, it has its pros and cons, but in general, Airflow makes things easier for Data Engineering teams. Take a look at my notes for knowing more about Airflow.
Daniela Fernandez 
Love the notes you take!
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