Rethinking Our Knowledge Engineering Course of
While you’re beginning a brand new crew, you are typically confronted with an important dilemma: Do you stick along with your current approach of working to stand up and working shortly, promising your self to do the refactoring later? Or do you are taking the time to rethink your strategy from the bottom up?
We encountered this dilemma in April 2023 once we launched a brand new knowledge science crew centered on forecasting inside bol’s capability steering product crew. Inside the crew, 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 change into everlasting as fixing them later requires important effort, and there are all the time extra speedy points demanding consideration. This time, we had been decided to do issues correctly from the beginning.
Recognising the potential pitfalls of sticking to our established approach of working, we determined to rethink our strategy. Initially we noticed a chance to leverage our current know-how stack. Nevertheless, it shortly turned clear that our processes, structure, and general strategy wanted an overhaul.
To navigate this transition successfully, we recognised the significance of laying a powerful groundwork earlier than diving into speedy options. Our focus was not simply on fast wins however on making certain that our knowledge engineering practices might sustainably assist our knowledge science crew’s long-term targets and that we might ramp up successfully. This strategic strategy allowed us to handle underlying points and create a extra resilient and scalable infrastructure. As we shifted our consideration from fast implementation to constructing a strong basis, we might higher leverage our know-how 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.
- Realized from previous experiences to determine and eradicate inefficiencies and redundancies that had been holding us again.
- Adopted a layered strategy to knowledge engineering, which streamlined our operations and considerably enhanced our capability to iterate shortly.
- Embraced monotasking in our workflows, enhancing 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 best way our knowledge flows.
By the tip of this journey, you’ll see how our dedication to doing issues the precise approach from the beginning has set us up for long-term success. Whether or not you’re going through related challenges or trying to refine your individual knowledge engineering practices, I hope our experiences and insights will present precious classes and inspiration.
Glide
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 route. When explaining Airflow to non-technical stakeholders, we frequently use the analogy of cooking recipes.