Rethinking Our Knowledge Engineering Course of
Whenever you’re beginning a brand new group, you are usually confronted with a vital dilemma: Do you stick together with your current manner of working to stand up and working shortly, promising your self to do the refactoring later? Or do you’re 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 group centered on forecasting inside bol’s capability steering product group. Inside the group, we regularly joked that “there’s nothing as everlasting as a brief answer,” as a result of rushed implementations usually result in long-term complications.These fast fixes are likely to grow to be everlasting as fixing them later requires important effort, and there are at all times extra instant 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 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 robust groundwork earlier than diving into instant 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 objectives and that we may 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 speedy implementation to constructing a stable basis, we may higher leverage our know-how stack and optimize our processes for future success.
We adopted the mantra of “Quick is gradual, gradual is quick.”: dashing into options with out addressing underlying points can hinder long-term progress. So, we prioritised constructing a stable 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 easy operations.
- Realized from previous experiences to determine and eradicate inefficiencies and redundancies that have been holding us again.
- Adopted a layered strategy to knowledge engineering, which streamlined our operations and considerably enhanced our potential 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 top of this journey, you’ll see how our dedication to doing issues the correct manner from the beginning has set us up for long-term success. Whether or not you’re dealing with related challenges or seeking to refine your personal knowledge engineering practices, I hope our experiences and insights will present helpful classes and inspiration.
Drift
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 course. When explaining Airflow to non-technical stakeholders, we regularly use the analogy of cooking recipes.