Was sind die wichtigsten Best Practices für die SQL-Codierung für BI?

Bajo mi punto de vista, es de vital importancia que el resultado de las sentencias sea entendible por cualquier usuario de negocio, ya que va a ser el usuario final de esta información, bien en un modelo de datos (autoservicio) o bien cuando se le presente la información en un informe o cuadro de mando. Por ello, se debe establecer un estándar de nomenclatura, utilizando para la descripción de cada columna un nombre entendible, con espacios, acentos y mayúsculas cuando sea necesario (evitar poner mayúsculas detrás de cada espacio). Por otra parte, la sentencia SQL debe estar bien formateada, con una estructura bien alineada, con los nombres de las palabras reservadas en mayúsculas (lo que hará que sea más fácil identificarlas).

Imagine SQL as a recipe book. Using snake_case or camelCase is like picking all spoons or all forks - consistency is key. Capitalize keywords like shouting "BAM!" in a cooking show, making them stand out. Descriptive names turn "Ingredient 1" into "Juicy Tomatoes," telling you exactly what's in the pot. Indentation and spacing are your neatly arranged kitchen, while wise comments are secret chef's notes, guiding without cluttering. Consistent date formats ensure every dish is served on time. Skip the jargon; make sure everyone can follow the recipe. This way, your SQL recipes aren't just edible; they're a feast for the eyes, easy to share and savor in the BI kitchen.

The essence of effective BI lies in easily understandable code, a well-structured data model, and clear documentation of KPI calculations. 1. Clear code enhances readability and maintenance, ensuring that team members can efficiently collaborate and iterate on analytics. 2. A robust data model is foundational, providing a logical framework that reflects the business's intricacies and supports accurate, insightful analyses. 3. Comprehensive documentation demystifies KPI calculations, offering transparency into the metrics driving business decisions. Together, these elements form the backbone of a resilient BI strategy, enabling organizations to harness data with confidence and precision.

Avoiding hard-coded values and parameters, especially with dates, is essential for dynamic and maintainable queries. Utilize variables or system time functions to adapt to current or relative times, ensuring your data remains relevant across time zones. For example, instead of fixed dates, use `GETDATE()` or `CURRENT_DATE` to automatically align with the present. Limit queries to recent periods, like the last 90 days, to enhance performance and focus on timely data. This approach not only boosts efficiency but also ensures your analysis stays relevant and scalable, accommodating future changes seamlessly.

Judicious use of joins, subqueries, and CTEs can significantly impact performance and clarity. When repeated joins to CTEs are required, integrating these as permanent tables in your data model can be advantageous. This approach allows for caching and direct access, reducing complexity and avoiding repetitive joins. A well-structured data model can minimize the need for excessive joins, enhancing query efficiency and understandability. By anticipating common query patterns and structuring your data model accordingly, you can streamline data retrieval processes, making your SQL code more efficient and intuitive to navigate.

Ensuring data integrity is crucial for reliable analytics. Properly handling duplicates, nulls, and understanding foreign keys are essential to maintain data quality. Avoid unnecessary joins by designing a thoughtful data model. For instance, instead of adding joins for each new field, consider whether your data structure allows for a more streamlined approach. Effective data modeling and integrity checks are vital for scalable, accurate BI solutions, helping to prevent errors and enhance the performance of your SQL queries.

Utilizar Índices apropriados podem melhorar significativamente o desempenho das consultas, especialmente em grandes conjuntos de dados. Identifique os campos mais frequentemente utilizados em suas consultas e crie índices para esses campos. Utilizar cláusulas WHERE eficientes. Subconsultas podem ser custosas em termos de desempenho. Se possível, reescreva suas consultas para evitar subconsultas ou utilize CTEs (Common Table Expressions) quando necessário.

To wrap up: - Use comments to clarify complex logic, making your SQL code more understandable for others and your future self. - Employ consistent formatting and clear aliases to enhance the readability and navigability of your SQL scripts. - Consistently name tables, columns, and procedures, aiding in code clarity and maintenance. - Embrace the challenges and successes in coding. Enjoy learning and experimenting, making your SQL journey not just productive but also enjoyable. - Continuously aim to refine and enhance your coding skills. Prioritize writing cleaner, more efficient, and more readable code with each project you undertake, viewing each new challenge as an opportunity to learn and grow in your BI expertise.