Learn how to write SQL code that is clear, efficient, and reliable for BI purposes. Discover some of the SQL coding best practices for naming, formatting, joining, subquerying, and optimizing your code.

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).

What are the most important SQL coding best practices for BI?

SQL is a powerful and versatile language for querying, manipulating, and analyzing data. As a business intelligence (BI) professional, you need to write SQL code that is clear, efficient, and reliable. In this article, we will discuss some of the most important SQL coding best practices for BI, such as:

1 Use consistent naming and formatting

One of the easiest ways to improve your SQL code readability and maintainability is to use consistent naming and formatting conventions. This means using descriptive and meaningful names for tables, columns, aliases, and variables, as well as using the same case, indentation, spacing, and punctuation throughout your code. For example, you can use snake_case or camelCase for naming, and capitalize keywords and functions for clarity. You can also use comments to explain the logic and purpose of your code, especially for complex queries and calculations.

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Pablo Barrachina Pastor

COO and Director of Data&AI at sumamoOs ➤ Data Strategy ◆ Data Governance ◆ Business Intelligence ◆ Big Data ◆ AI and Machine Learning tailored to optimize business processes
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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).

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Robel Tekeste Fekadu

Planning and Scheduling Engineer
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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.

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Bobby Markov

Snr. Gaming Data Analyst | Ph.D. in behavioural economics | Tableau | SQL | Python | ML
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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.

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2 Avoid hard-coding values and parameters

Another common SQL coding mistake is to hard-code values and parameters that may change over time or vary depending on the context. For example, if you want to filter your data by a specific date range, you should not write something like this:

SELECT *
FROM sales
WHERE date BETWEEN '2020-01-01' AND '2020-12-31';

Instead, you should use variables or parameters that can be dynamically assigned or passed as arguments to your query. This way, you can reuse your code for different scenarios and avoid errors and inconsistencies. For example, you can write something like this:

DECLARE @start_date DATE = '2020-01-01';
DECLARE @end_date DATE = '2020-12-31';
SELECT *
FROM sales
WHERE date BETWEEN @start_date AND @end_date;

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Bobby Markov

Snr. Gaming Data Analyst | Ph.D. in behavioural economics | Tableau | SQL | Python | ML
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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.

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3 Use joins and subqueries wisely

Joins and subqueries are essential tools for combining and filtering data from multiple tables or sources, but they can also affect the performance and complexity of your SQL code. To ensure you're using them wisely, it's important to follow some best practices. For example, use explicit join syntax (INNER JOIN, LEFT JOIN, etc.) instead of implicit join syntax (comma-separated tables in the FROM clause) to avoid confusion and ambiguity. Additionally, use subqueries only when necessary and avoid nesting too many subqueries within each other; instead, use temporary tables, common table expressions (CTEs), or views to store intermediate results and simplify your code. Lastly, use EXISTS or NOT EXISTS instead of IN or NOT IN when checking for the presence or absence of rows in a subquery. This can improve the performance and accuracy of your code since EXISTS and NOT EXISTS return a boolean value as soon as they find a match or no match, unlike IN and NOT IN which scan the entire subquery result set.

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Bobby Markov

Snr. Gaming Data Analyst | Ph.D. in behavioural economics | Tableau | SQL | Python | ML
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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.

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4 Optimize your code for performance and scalability

As a BI professional, you want your SQL code to run fast and handle large volumes of data. To achieve this, you need to optimize your code for performance and scalability. This can be done by using indexes to speed up the search and retrieval of data from tables, aggregation functions and GROUP BY clauses to reduce the number of rows returned by your query, and LIMIT or TOP clauses to limit the number of rows returned. Additionally, best practices for SQL coding involve writing code that is easy to read, maintain, modify, and reuse. By following these best practices, you can improve your SQL skills and deliver better BI solutions.

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Bobby Markov

Snr. Gaming Data Analyst | Ph.D. in behavioural economics | Tableau | SQL | Python | ML
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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.

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MSc. Eng. Rodrigo dos Santos

Data Analyst | Power BI Specialist | Formador de Talentos em Tecnologia | SQL | AWS | Python | Statistician | Machine Learning
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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.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Bobby Markov

Snr. Gaming Data Analyst | Ph.D. in behavioural economics | Tableau | SQL | Python | ML
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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.

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What are the most important SQL coding best practices for BI?

1

2

3

4

5

1 Use consistent naming and formatting

2 Avoid hard-coding values and parameters

3 Use joins and subqueries wisely

4 Optimize your code for performance and scalability

5 Here’s what else to consider

Business Intelligence

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What are the most important SQL coding best practices for BI?

1

2

3

4

5

1 Use consistent naming and formatting

2 Avoid hard-coding values and parameters

3 Use joins and subqueries wisely

4 Optimize your code for performance and scalability

5 Here’s what else to consider

Business Intelligence

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