Rethinking Our Knowledge Engineering Course of
Once you’re beginning a brand new crew, you are typically confronted with a vital dilemma: Do you stick along with your present manner of working to rise up and operating 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 information science crew centered on forecasting inside bol’s capability steering product crew. Throughout the crew, we regularly joked that “there’s nothing as everlasting as a short lived answer,” as a result of rushed implementations typically result in long-term complications.These fast fixes are likely to turn into everlasting as fixing them later requires important effort, and there are at all times 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 strategy. Initially we noticed a possibility to leverage our present expertise stack. Nevertheless, it shortly grew to become 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 quick options. Our focus was not simply on fast wins however on guaranteeing that our information engineering practices might sustainably assist our information 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 speedy implementation to constructing a stable basis, we might higher leverage our expertise stack and optimize our processes for future success.
We adopted the mantra of “Quick is sluggish, sluggish is quick.”: speeding into options with out addressing underlying points can hinder long-term progress. So, we prioritised constructing a stable basis for our information engineering practices, benefiting our information 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 information engineering processes. We’ll discover how we:
- Leveraged Apache Airflow to orchestrate and handle our information workflows, simplifying advanced processes and guaranteeing easy operations.
- Discovered from previous experiences to establish and get rid of inefficiencies and redundancies that have been holding us again.
- Adopted a layered strategy to information engineering, which streamlined our operations and considerably enhanced our capability to iterate shortly.
- Embraced monotasking in our workflows, bettering readability, maintainability, and reusability of our processes.
- Aligned our code construction with our information construction, making a extra cohesive and environment friendly system that mirrored the best way our information flows.
By the top of this journey, you’ll see how our dedication to doing issues the best manner from the beginning has set us up for long-term success. Whether or not you’re going through comparable challenges or seeking to refine your personal information engineering practices, I hope our experiences and insights will present worthwhile classes and inspiration.
Flow
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.
