Real-time queries require low latency and high responsiveness, while historical analyses demand greater batch processing capacity to handle large volumes of data. Never forget: Separating these responsibilities within specific architectures ensures not only processing efficiency but also optimization tailored to the needs of each query type. In addition to traditional practices such as updating statistics, creating indexes, and using caching, it is essential to structure the environment in a way that segments these processes, taking into account the differences in query nature. This careful implementation is crucial to ensure consistent performance aligned with operational expectations.

When I hire engineers, advanced troubleshooting skills are a must. "How to optimize" is not a question that can be answered in 1 or 2 sentences. You need to understand the context, the current situation, and the business requirements. Finding the root cause is important. That's why I value engineers with problem-solving skills. You don't need to know everything about GCP, AWS, or Azure, but you should be able to identify a problem and find a solution. It doesn't matter if you use ChatGPT or a quick Google search (which I highly recommend).

From the outside of a data warehouse looking in, I have two recommendations : get more hardware or get less data. I usually gravitate to the latter. When queries slow down, it is often due to the increase in the amount of data being scanned. To further the statement above, getting less data doesnt mean deleting data for the sake of saving space. To ensure consistent query performance, you have to control the amount of data being scanned. This can be done in multiple ways, data archival, for one. But practically, partitioning data is a great way to go. This may be a feature (like in Oracle) but may need to be done manually (other db's). In any case, limiting the amount of data scanned will ensure good to consistent performance.

PT/BR: Num cenário em que Data Warehouses e Data Lakes parecem se igualar cada vez mais, perdendo distinções e demais diferenças; o conceito de DW fica cada vez mais confuso. Mas bom: acima de tudo, são conceitos. Importante é pôr em prática, e à otimizar respectivamente. Isso pode ser feito através da IA e técnicas de scrapping dentro do próprio Data Warehouse, ou: potencializar o Hardware, investindo em computação em nuvem para melhor otimização dos serviços, e escalabilidade... É uma das soluções.

Optimizing data warehouse queries requires a strategic blend of technical precision and proactive planning. Queries often falter due to increasing data volumes or inefficient structures. To counter this, I prioritize minimizing the scanned data without sacrificing accessibility—a principle rooted in robust data partitioning. Whether using built-in database features like Oracle's partitioning or implementing custom solutions, segmentation ensures queries remain fast and reliable. Further, leveraging cloud computing for scalable performance and applying AI-driven optimizations underscores modern best practices. These strategies not only elevate efficiency but also adapt to evolving data demands, maintaining agility and performance at scale.