SBP 6 – implement the difficult first!
In software engineering, there’s a prevailing adage:
This metaphorical advice suggests tackling the most challenging task of your day first, freeing you from the looming dread of facing it later. Similarly, in data engineering, we adapt this notion to our processes, emphasizing the importance of confronting the most complex problems at the outset. It’s not just about getting the hard tasks out of the way; it’s a strategic maneuver with profound implications. Why you should tackle difficult issues first will be explained in the following sections.
The philosophy of tackling the hard tasks first
Tackling the hardest problems first may appear counterintuitive, especially in a discipline as nuanced as data engineering. However, it is deeply rooted in prioritization and risk management.
By dealing with the most challenging parts early, teams can evaluate the feasibility of their methods and proactively handle potential setbacks. This acts as a safeguard, ensuring that projects don’t suffer late-stage disruptions, which tend to be costlier and more time-consuming to rectify.
For data engineers, this methodology is invaluable. Laying down a solid foundational architecture from the get-go – through intricate data integrations or optimized big data processing – not only showcases the team’s expertise but also ensures smoother execution of subsequent tasks. This approach aids in refining data architecture, obtaining early feedback, bolstering system scalability, and assuring stakeholders of the team’s prowess.
How data engineers can prioritize difficult tasks
Discernment is paramount. It starts with requirement analysis, where tasks are sifted based on their complexity. Dependency mapping then pinpoints tasks with numerous dependencies, ensuring future bottlenecks are averted. Risk assessment, although often overlooked, can serve as an early warning system, highlighting tasks that might have hidden challenges.
Implementing difficult data tasks
Certain situations in data engineering vividly underscore this approach’s value. Whether it’s the intricacies of establishing effective real-time data pipelines or ensuring rigorous checks for inconsistent data sources, prioritizing these challenges facilitates smoother navigation of real-time data processing and data quality landscapes.
However, the path is not devoid of obstacles. A concentrated effort on formidable tasks might quickly deplete initial resources and even risk team burnout. Maintaining equilibrium is crucial, ensuring that while tackling giants, smaller yet pivotal tasks aren’t sidelined.
Synergy with other data best practices
This principle aligns seamlessly with other best practices in data engineering. From aligning early problem detection with SLT to drawing synergies with Agile’s feedback loops and the data as a product paradigm, this strategy reinforces other methodologies.
Conclusion
In essence, implement the difficult first encourages teams to be proactive, not reactive. For those willing to face their most formidable challenges head-on, the ensuing journey is often more streamlined and foreseeable.