How do you prioritize tasks when streamlining ETL processes for efficient data warehousing?

Depends. Load sequencing based on individual tasks is a critical factor at my workplace. You dont want to jeopardize the parent-child relationship because of incorrect load sequence of the tasks based on different source systems(depends from one company/tool to another). Then comes the platform optimization. Finding the problem areas are a proactive practice. Unless someone takes ownership and leads the effort of finding the bottenecks, there wont be any performance issues. Fine tuning and optimization is endless if you find the gaps like load duration, query performance, distribution method techniques, processing time of ETL engine and find ways to tackle one after monitoring which process could yield most efficiency.

To improve process efficiency, I focus on optimizing the performance of our ETL jobs at ADM. This includes streamlining data transformation logic, optimizing SQL queries, and ensuring efficient resource utilization. For example, I have restructured complex transformations into more manageable, modular scripts, significantly reducing processing times. By regularly reviewing and refining these processes, I ensure that our ETL workflows are as efficient as possible, reducing latency and improving overall system performance.

Here’s how I’ve approached this at ADM, drawing on insights for top-tier performance: In my role, I first pinpointed the repetitive tasks within our ETL processes. This includes data validation, cleaning, and transformation tasks that are performed regularly. By focusing on these, I aimed to reduce the manual workload and potential for errors. Automation isn’t just about data processing; it also involves monitoring. We've set up real-time monitoring and alert systems that track ETL job performance and notify us (email triggers) immediately of any failures or issues. This proactive approach helps in addressing problems quickly, minimizing downtime and ensuring continuous data flow.