I focus on scalability, governance, and innovation to ensure a data warehouse stands the test of time amidst rapid growth. Implementing a modular architecture allows seamless expansion as data volumes grow. I prioritise cloud-based solutions, ensuring flexibility and cost-efficiency while maintaining robust security. Data governance frameworks ensure compliance and accuracy, supporting strategic decision-making. I integrate AI/ML tools to optimise processing and generate actionable insights. Collaborating with stakeholders, I future-proof the system to align with organisational goals, delivering a resilient, scalable, and efficient data ecosystem.

Your architecture should be done in such a way that it supports auto scaling as needed and allow enhancing/ refining the data further to allow easy reporting. Upstream to downstream data movement is always challenging unless there is way to perform and show the data integrity.

While developing a warehouse we need to make sure that we choose the best technology in the market in terms of scalability, flexibility and robustness in order to ensure that it handles our organisation workloads for at least next 5 years. Basically we need to invest in the best tool\tools available in our budget from the very first day. This not just provides stability but saves massive time, effort and cost involved in making major changes to our data warehouse design and overall architecture due to the ever increasing workloads\data size. A data warehouse architect needs to be far sighted enough to create\enhance the warehouse design in such a way that it not just solves current problem but is capable enough for future as well.

A logistics company saw their data volumes double in two years. By implementing a scalable cloud platform, automating backups, and routinely optimizing slow queries, they avoided costly downtime and maintained fast performance even during peak operations.

- Properly designing your warehouse to easily accommodate scale. - The architecture used, definitely has to be able to scale otherwise it can be the biggest bottleneck even with a properly designed warehouse. - Where necessary optimization. Index properly and where necessary. - Can't finish this without talking about backups and automation of the same

While developing a warehouse we need to make sure that we choose the best technology in the market in terms of scalability, flexibility and robustness in order to ensure that it handles our organisation workloads for at least next 5 years. Basically we need to invest in the best tool\tools available in our budget from the very first day. This not just provides stability but saves massive time, effort and cost involved in making major changes to our data warehouse design and overall architecture due to the ever increasing workloads\data size. A data warehouse architect needs to be far sighted enough to create\enhance the warehouse design in such a way that it not just solves current problem but is capable enough for future as well.

- Ensure schema is optimal for OLAP (tables, columns and datatypes) - Avoid multiple copies of same data - Review SQLs for optimal execution plans - Optimal indexing of tables keeping in view the queries - Leverage views and materialised views for most commonly consumed datasets - Ensure housekeeping activities (analyse, rebuild) happens regularly (could be automated based on how self-managed or otherwise it is) - Sufficient hardware resources in terms of memory and cpu. Leverage SSD and flash cache for best performance from storage standpoint

To ensure seamless maintenance of a data warehouse, I prioritize automating routine tasks such as backups, data loading, and performance monitoring. This reduces human errors and allows the system to function efficiently even as complexity grows. Automation tools facilitate real-time adjustments and optimize resource allocation, minimizing downtime. By leveraging advanced scheduling and monitoring solutions, I can proactively identify and resolve potential bottlenecks. This approach supports a dynamic and resilient data ecosystem capable of adapting to evolving business needs.

Algo que he conocido durante mi experiencia, es buscar una buena tecnología que permita el acceso a los datos de manera oportuna, es decir, ante fallos poder recuperar la información de manera completa, esto incluye hacer un análisis de que tipo de información se va a manejar, puede necesitarse un sistema muy robusto externo, especializado o incluso por la cantidad de datos que se tengan, pueden llegar a ser manejados por un sistema propio ya que la tecnología es más accesible.