What common data warehouse design mistakes should you avoid?

Data warehouse architecture affects the cost-effectiveness of a business's data operation. Let's take two of the popular cloud data warehouse solutions as illustrative examples. AWS Redshift started as massively parallel processing PostgreSQL-based databases in clusters. Until the release of the RA node in 2019, the data were replicated in the computing node. So, the computing node had to grow with the amount of the data stored. The user had to monitor the disk usage and manage the cluster size. This resulted in high computing and operational costs. Snowflake (2012-) is one of the first DW architectures to separate computing and storage. This allowed more flexible cost management through on-the-fly scalable computing and data-cloning.

When choosing a data warehouse architecture, it's crucial to pick the right one - like star schema, snowflake schema, or data vault - based on your data, business needs, and analytics. Wrong choice can cause issues like poor performance, data redundancy, inconsistency, or complexity. It's important to understand each architecture's pros and cons to match your data warehouse goals.

La arquitectura de almacenamiento de datos se refiere a la forma en que se estructuran y relacionan los datos dentro del almacén. Existen diferentes tipos de arquitecturas, como el modelo dimensional, el modelo relacional, el modelo de estrella, el modelo de copo de nieve, el modelo de bóveda de datos, etc. Cada una tiene sus ventajas y desventajas, y se adapta mejor a ciertos escenarios y requisitos. Elegir la arquitectura incorrecta puede provocar problemas de rendimiento, complejidad, mantenimiento y escalabilidad. Para evitar este error, es importante analizar las características de los datos, las consultas y los informes que se van a realizar, y las expectativas de los usuarios.

Effective data quality and governance are vital in data warehousing to ensure accuracy, reliability, and security. Overlooking data duplication, inconsistency, or privacy, can lead to misleading information and breaches. To mitigate these risks, it's essential to implement robust processes and tools like data profiling, cleansing, validation, auditing, cataloging, encryption, and masking. These measures not only prevent data corruption and loss but also uphold data integrity, making them indispensable in contemporary data management strategies

Join and ordering operations are very expensive in large datasets environments. Avoiding them or deferring them to client side as much as possible, especially resulting datasets are relatively small in size and by memory requirements. By doing so dataware house servers are released from resource intensive tasks.

Las prácticas recomendadas de dise?o de almacenamiento de datos son un conjunto de principios y consejos que ayudan a optimizar el rendimiento, la usabilidad y la escalabilidad del almacén de datos. Algunas de estas prácticas son: usar claves artificiales en lugar de claves naturales, evitar el uso de valores nulos, normalizar o desnormalizar los datos según el caso, usar índices y particiones adecuados, documentar el modelo de datos, etc. No seguir estas prácticas puede provocar que el almacén de datos sea lento, difícil de entender, modificar o ampliar.

Common data warehouse design mistakes to avoid include neglecting proper data modeling, leading to inconsistent or redundant data. Overlooking data quality issues can result in inaccurate insights. Failure to establish clear business requirements may lead to irrelevant or unaligned data collection. Ignoring scalability can hinder growth and performance. Lack of documentation can make the system hard to understand and maintain. Also, not considering security measures can expose sensitive data. Lastly, avoiding regular performance tuning may lead to inefficiencies in data retrieval and analysis.

Usually DWH collects data from all over the company resources so it's a good idea not only to design it well from technical point of view but also design and launch reliable and efficient business-processes to provide data delivery into DWH. Which require good communication with colleagues from other departments. So it's a good idea to create a work group that would be responsible for DWH data income processes and would include people that are aware of the details of various data sources and flows who would create data providing processes and maintain them ensuring the good and actual state of the DWH data.