Rethinking Our Knowledge Engineering Course of
If you’re beginning a brand new group, you are typically confronted with an important dilemma: Do you stick along with your present manner of working to rise up and working rapidly, promising your self to do the refactoring later? Or do you are taking the time to rethink your method from the bottom up?
We encountered this dilemma in April 2023 once we launched a brand new knowledge science group targeted on forecasting inside bol’s capability steering product group. Inside the group, we frequently joked that “there’s nothing as everlasting as a brief resolution,” as a result of rushed implementations typically result in long-term complications.These fast fixes are inclined to develop into everlasting as fixing them later requires vital effort, and there are all the time extra quick points demanding consideration. This time, we have been decided to do issues correctly from the beginning.
Recognising the potential pitfalls of sticking to our established manner of working, we determined to rethink our method. Initially we noticed a chance to leverage our present expertise stack. Nonetheless, it rapidly grew to become clear that our processes, structure, and general method wanted an overhaul.
To navigate this transition successfully, we recognised the significance of laying a powerful groundwork earlier than diving into quick options. Our focus was not simply on fast wins however on making certain that our knowledge engineering practices may sustainably help our knowledge science group’s long-term targets and that we may ramp up successfully. This strategic method allowed us to handle underlying points and create a extra resilient and scalable infrastructure. As we shifted our consideration from speedy implementation to constructing a strong basis, we may higher leverage our expertise stack and optimize our processes for future success.
We adopted the mantra of “Quick is sluggish, sluggish is quick.”: dashing into options with out addressing underlying points can hinder long-term progress. So, we prioritised constructing a strong basis for our knowledge engineering practices, benefiting our knowledge science workflows.
Our Journey: Rethinking and Restructuring
Within the following sections, I’m going to take you alongside our journey of rethinking and restructuring our knowledge engineering processes. We’ll discover how we:
- Leveraged Apache Airflow to orchestrate and handle our knowledge workflows, simplifying advanced processes and making certain clean operations.
- Discovered from previous experiences to determine and get rid of inefficiencies and redundancies that have been holding us again.
- Adopted a layered method to knowledge engineering, which streamlined our operations and considerably enhanced our skill to iterate rapidly.
- Embraced monotasking in our workflows, bettering readability, maintainability, and reusability of our processes.
- Aligned our code construction with our knowledge construction, making a extra cohesive and environment friendly system that mirrored the way in which our knowledge flows.
By the tip of this journey, you’ll see how our dedication to doing issues the precise manner from the beginning has set us up for long-term success. Whether or not you’re going through related challenges or seeking to refine your personal knowledge engineering practices, I hope our experiences and insights will present helpful classes and inspiration.
Float
We rely closely on Apache Airflow for job orchestration. In Airflow, workflows are represented as Directed Acyclic Graphs (DAGs), with steps progressing in a single path. When explaining Airflow to non-technical stakeholders, we frequently use the analogy of cooking recipes.
