How can you handle NULL values in a database query?

A melhor prática para evitar interferências de campos com valores "NULL" é atribuir algum valor para identificá-los, seja esse valor "0" ou por exemplo "Sem dados encontrados" para facilitar a consulta SQL. Valores NULL pode tornar os resultados incompletos ou inconsistentes porque registros são excluídos/ignorados da consulta.

In managing data, understanding NULL values is important. NULL means "no value," and it matters because it can affect data accuracy, query results, and how well systems perform. By handling NULL values wisely like validating data, organizing it neatly, and designing smart queries, we keep our data reliable and make better decisions.

NULL values are special entries used to indicate missing or unknown data in a database. However, when using SQL queries, these values can present some challenges. For example, they can cause issues when doing comparisons, aggregations or joins. It's important to be cautious when working with NULL values to ensure that your SQL query results are dependable and accurate.

Null values are properly handled to represent missing and unknown data values by assuring consistency in your handling. This helps to flexibly manage your data.

It depends. E.g., in PowerBI NULL values can cause rows of a table to disappear. If for example we have no revenue for a given product within a certain period of time the associated row will not be shown. So, one good practice is to replace NULL with 0. Also counting NULLs in SQL is tricky.

The IS NULL condition in SQL is used to check for a NULL value. It gives back TRUE if it finds a NULL value, and FALSE if not. You can use this condition in different kinds of SQL operations like SELECT, INSERT, UPDATE, or DELETE. The IS NULL operator is really useful for finding rows in a table where a specific column doesn't have any value. This is important when you need to spot which records are missing information. On the other hand, the IS NOT NULL operator helps you find rows where a column does have a value, which means it's not missing. This is good for when you want to leave out rows that don't have certain information.

🔍 Use IS NULL or IS NOT NULL to filter records based on their NULL status. For example, to find customers without an email address: SELECT * FROM customers WHERE email IS NULL;.

According to ansi 92 sql NULL is a default for indicating the absense of value in a specific column. So every ANSI rdbms should be able to handle such specific value. The is null operator where statement can be used to handle null value but in general is preferreable from a data mining point of view to assign a default to null specific value time. So for example if you have an integer column to find null value one should first query the column to find min / MAX value and then choose a value for the null. In sql server for example to find all null value in such column one can use isnull(colum_name,-1) = -1

You can also use CASE WHEN ... IS NULL statement as a way to handle conditions based on whether a column's value is NULL or not. It's very useful for scenarios where you need to assign a specific value to rows depending on the presence or absence of data (NULL values). Imagine you have a table called Products with columns ProductID, ProductName, and Price. Some of the products do not have a price set, so their Price column is NULL. SELECT ProductID, ProductName, CASE WHEN Price IS NULL THEN 'Price Not Available' ELSE CAST(Price AS VARCHAR) END AS PriceInfo FROM Products

Using COALESCE in SQL is a simple yet powerful way to do this. It lets us swap NULLs with other values, like zeros, making calculations and comparisons consistent. By using COALESCE smartly in our queries and processes, we ensure data reliability, helping everyone make better decisions with complete information.

🔄 Use COALESCE to substitute NULLs with a default value. For instance, to ensure no NULLs in an order's total amount calculation: SELECT order_id, COALESCE(quantity, 0) * COALESCE(price, 0) AS total_amount FROM orders;.

The first question one should ask him/herself before replacing the null value with a default is to consider if the amount of null value in a column is above a certain threshold so that the substitution can alter further analysis on the column. In general, the substitution with some values could alter the overall analysis. COALESCE and isnull can be used but sometimes using random binomial values extracted from the column could be a better choice

We can handle Null values in SQL various ways for example IS NOT NULL and Not NULL is used as a operators to filer rows based on the presence or absence.

↔️ Standard comparison operators don't work with NULLs. Use NULL-safe operators like <=> in MySQL or IS DISTINCT FROM in PostgreSQL to compare values, including NULLs.

When you compare null value befor doing anything one should make sure that the column type are of the same type otherwise it is better to use some convert operator. So for example the following query select c_1, c_2 from table1, table2 where c_1 = c_2 and c_1 column and c_2 column can have null value to mantain the integrity one could rewrite the query this way select c_1, c_2 from table1, table2 where isnull(c_1,-9999) = isnull(c_2,-9999) and -9999 should be a value that is not present in c_1 neither in c_2, if both column are integer value. In case the type is different ex c_2 is string one should evaluate a conversion for c_2 from string to integer.

Knowing that functions like COUNT, SUM, and AVG skip NULLs while COUNT(*) includes them helps ensure comprehensive insights. By using these functions wisely in queries, we capture valuable metrics while acknowledging the presence of NULLs. This approach enriches our data analysis, enabling informed decision-making and enhancing overall data quality.

Inspect your null values before you attempt to delete them (check why you have null values). Depending on whether you're doing exploratory analysis, or data visualization make sure that deleting the null values won't affect your results or misguide you.

In managing databases, it's important to address how NULL values impact data integrity and query efficiency. Effective handling involves validating data, normalizing techniques, and thoughtful query design. Encouraging a culture of data quality through audits, training, and clear documentation ensures stakeholders understand the significance of NULL handling, fostering informed decision-making and business growth.

null values can be a great indicator of the quality of the value in your dataset so if used wisely they can be a fundamental instrument to perform further analysis. On the other side an excessive use of tentative to convert null value to some default can alter all of the next phases of your data mining task.

Effectively managing NULL values in database queries needs careful evaluation and an understanding of their impact on the analysis and storage of data. Before utilising any strategy to manage NULLs, consider the following. 1. Consider the definition of NULL in your data (missing, unknown, etc.). 2. Determine how replacing or disregarding NULLs may affect your analysis. 3. Choose a strategy that is both straightforward and reliable for your needs.