Understanding HAVING vs WHERE in SQL: Filter Grouped Data

DataCamp · Intermediate ·📊 Data Analytics & Business Intelligence ·1y ago

Key Takeaways

The video explores the HAVING clause in SQL, its role in filtering grouped data, and its difference from the WHERE clause, with a focus on working with SQL aggregate functions and the SQL order of execution.

Full Transcript

that was excellent work we've combined sorting and grouping next we will combine filtering with grouping in SQL we can't filter aggregate functions with wear Clauses for example this query attempting to filter the title count is invalid that means that if we want to filter based on the result of an aggregate function we need another way groups have their own special filtering word having for example this query shows only those years in which more than 10 films were released the reason why groups have their own keyword for filtering comes down to the order of execution we've written a query using many of the key words we have covered here this is their written order starting with select from films where the certification is G PG or PG13 Group by certification having the title count be greater than 500 order by title count and limit to three in contrast the order of execution is from where Group by having select order by and limit by reviewing this order we can see where is executed before Group by and before any aggregation occurs this order is also why we cannot use the Alias with having but we can with orderby where filters individual records while having filters grouped records we'll walk through two business questions here to show how to translate them into the correct filter the first question is what films were released in the year 2000 this question does not indicate any sort of grouping it asks to see only the titles from a specific year and can therefore be written as Select Title from films where release year equals 2000 the second question is in what years was the average film duration over 2 hours straight away we can see this question has a few more layers let's break down the question and query into smaller easier to understand steps this question requires us to return information about years so we select the release year from the film's table next it asks for the average film duration which tells us we need to place average duration somewhere since we do not need to provide any additional information around the duration on its own it is unlikely we need to perform the aggregation within the select Clause so we'll try the having Clause instead the last part of the question indicates we need to filter on the duration since we can't filter Aggregates with wear this supports our theory about using having finally we need to add a group by into our query since we have selected a column that has not been aggregated recall the aggregate function will convert the duration values into one average value going back to the start of our question we're interested in knowing the average duration per year so we group it by release year and there we have it [Music]

Original Description

In this video, we explore the HAVING clause in SQL and its role in filtering grouped data, a key concept for intermediate SQL learners. Discover why HAVING is crucial for working with SQL aggregate functions and how it differs from the WHERE clause. We'll guide you through examples illustrating the SQL order of execution and demonstrate practical use cases. - Learn to filter aggregated results using HAVING - Understand the difference between HAVING and WHERE - Explore SQL's order of execution - Practice SQL queries with aggregate functions and GROUP BY #sql #datascience #dataengineering #databases #postgresql 00:00 Intro 00:09 Understanding HAVING in SQL 00:44 Order of SQL Execution 01:53 HAVING vs WHERE: Business Questions 02:52 Breaking Down HAVING vs WHERE 04:05 Let's Practice!
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from DataCamp · DataCamp · 0 of 60

← Previous Next →
1 SQL Server Tutorial: Date manipulation
SQL Server Tutorial: Date manipulation
DataCamp
2 R Tutorial: Intermediate Interactive Data Visualization with plotly in R
R Tutorial: Intermediate Interactive Data Visualization with plotly in R
DataCamp
3 R Tutorial: Adding aesthetics to represent a variable
R Tutorial: Adding aesthetics to represent a variable
DataCamp
4 R Tutorial: Moving Beyond Simple Interactivity
R Tutorial: Moving Beyond Simple Interactivity
DataCamp
5 Python Tutorial: Why use ML for marketing? Strategies and use cases
Python Tutorial: Why use ML for marketing? Strategies and use cases
DataCamp
6 Python Tutorial: Preparation for modeling
Python Tutorial: Preparation for modeling
DataCamp
7 Python Tutorial: Machine Learning modeling steps
Python Tutorial: Machine Learning modeling steps
DataCamp
8 R Tutorial: The prior model
R Tutorial: The prior model
DataCamp
9 R Tutorial: Data & the likelihood
R Tutorial: Data & the likelihood
DataCamp
10 R Tutorial: The posterior model
R Tutorial: The posterior model
DataCamp
11 R Tutorial: An Introduction to plotly
R Tutorial: An Introduction to plotly
DataCamp
12 R Tutorial: Plotting a single variable
R Tutorial: Plotting a single variable
DataCamp
13 R Tutorial: Bivariate graphics
R Tutorial: Bivariate graphics
DataCamp
14 Python Tutorial: Customer Segmentation in Python
Python Tutorial: Customer Segmentation in Python
DataCamp
15 Python Tutorial: Time cohorts
Python Tutorial: Time cohorts
DataCamp
16 Python Tutorial: Calculate cohort metrics
Python Tutorial: Calculate cohort metrics
DataCamp
17 Python Tutorial: Cohort analysis visualization
Python Tutorial: Cohort analysis visualization
DataCamp
18 R Tutorial: Building Dashboards with flexdashboard
R Tutorial: Building Dashboards with flexdashboard
DataCamp
19 R Tutorial: Anatomy of a flexdashboard
R Tutorial: Anatomy of a flexdashboard
DataCamp
20 R Tutorial: Layout basics
R Tutorial: Layout basics
DataCamp
21 R Tutorial: Advanced layouts
R Tutorial: Advanced layouts
DataCamp
22 Python Tutorial: Time Series Analysis in Python
Python Tutorial: Time Series Analysis in Python
DataCamp
23 Python Tutorial: Correlation of Two Time Series
Python Tutorial: Correlation of Two Time Series
DataCamp
24 Python Tutorial: Simple Linear Regressions
Python Tutorial: Simple Linear Regressions
DataCamp
25 Python Tutorial: Autocorrelation
Python Tutorial: Autocorrelation
DataCamp
26 R Tutorial: The gapminder dataset
R Tutorial: The gapminder dataset
DataCamp
27 R Tutorial: The filter verb
R Tutorial: The filter verb
DataCamp
28 R Tutorial: The arrange verb
R Tutorial: The arrange verb
DataCamp
29 R Tutorial: The mutate verb
R Tutorial: The mutate verb
DataCamp
30 R Tutorial: What is cluster analysis?
R Tutorial: What is cluster analysis?
DataCamp
31 R Tutorial: Distance between two observations
R Tutorial: Distance between two observations
DataCamp
32 R Tutorial: The importance of scale
R Tutorial: The importance of scale
DataCamp
33 R Tutorial: Measuring distance for categorical data
R Tutorial: Measuring distance for categorical data
DataCamp
34 Python Tutorial: Plotting multiple graphs
Python Tutorial: Plotting multiple graphs
DataCamp
35 Python Tutorial: Customizing axes
Python Tutorial: Customizing axes
DataCamp
36 Python Tutorial: Legends, annotations, & styles
Python Tutorial: Legends, annotations, & styles
DataCamp
37 Python Tutorial: Introduction to iterators
Python Tutorial: Introduction to iterators
DataCamp
38 Python Tutorial: Playing with iterators
Python Tutorial: Playing with iterators
DataCamp
39 Python Tutorial: Using iterators to load large files into memory
Python Tutorial: Using iterators to load large files into memory
DataCamp
40 SQL Tutorial: Introduction to Relational Databases in SQL
SQL Tutorial: Introduction to Relational Databases in SQL
DataCamp
41 SQL Tutorial: Tables: At the core of every database
SQL Tutorial: Tables: At the core of every database
DataCamp
42 SQL Tutorial: Update your database as the structure changes
SQL Tutorial: Update your database as the structure changes
DataCamp
43 Python Tutorial: Classification-Tree Learning
Python Tutorial: Classification-Tree Learning
DataCamp
44 Python Tutorial: Decision-Tree for Classification
Python Tutorial: Decision-Tree for Classification
DataCamp
45 Python Tutorial: Decision-Tree for Regression
Python Tutorial: Decision-Tree for Regression
DataCamp
46 Python Tutorial: Census Subject Tables
Python Tutorial: Census Subject Tables
DataCamp
47 Python Tutorial: Census Geography
Python Tutorial: Census Geography
DataCamp
48 Python Tutorial: Using the Census API
Python Tutorial: Using the Census API
DataCamp
49 R Tutorial: A/B Testing in R
R Tutorial: A/B Testing in R
DataCamp
50 R Tutorial: Baseline Conversion Rates
R Tutorial: Baseline Conversion Rates
DataCamp
51 R Tutorial: Designing an Experiment - Power Analysis
R Tutorial: Designing an Experiment - Power Analysis
DataCamp
52 R Tutorial: Introduction to qualitative data
R Tutorial: Introduction to qualitative data
DataCamp
53 R Tutorial: Understanding your qualitative variables
R Tutorial: Understanding your qualitative variables
DataCamp
54 R Tutorial: Making Better Plots
R Tutorial: Making Better Plots
DataCamp
55 SQL Tutorial: OLTP and OLAP
SQL Tutorial: OLTP and OLAP
DataCamp
56 SQL Tutorial: Storing data
SQL Tutorial: Storing data
DataCamp
57 SQL Tutorial: Database design
SQL Tutorial: Database design
DataCamp
58 Python Tutorial: Introduction to spaCy
Python Tutorial: Introduction to spaCy
DataCamp
59 Python Tutorial: Statistical Models
Python Tutorial: Statistical Models
DataCamp
60 Python Tutorial: Rule-based Matching
Python Tutorial: Rule-based Matching
DataCamp

This video teaches intermediate SQL learners how to use the HAVING clause to filter grouped data and understand its role in working with SQL aggregate functions, with practical examples and use cases.

Key Takeaways
  1. Learn the HAVING clause in SQL
  2. Understand the difference between HAVING and WHERE
  3. Explore the SQL order of execution
  4. Practice SQL queries with aggregate functions and GROUP BY
💡 The HAVING clause is used to filter aggregated results, whereas the WHERE clause is used to filter individual rows.

Related Reads

📰
Tracking Macroeconomic Indicators with the Finance Toolkit
Learn to track macroeconomic indicators using the Finance Toolkit and understand its importance in global economic trends
Dev.to · Jeroen Bouma
📰
Pydantic for Data Engineering: Schema Validation in ETL & Pipeline Contracts
Use Pydantic for schema validation in ETL pipelines to ensure data consistency and quality
Dev.to · Gowtham Potureddi
📰
Half of Data Engineering Jobs on LinkedIn Aren't Real
Understand the discrepancy between reported data engineering job growth and actual job availability on LinkedIn
Dev.to · DataDriven
📰
Evolutionary Data Through Schemaboi: Achieving Forward, Backwards, and Sideways Compatibility
Learn how Schemaboi achieves forward, backwards, and sideways compatibility for evolutionary data through self-contained schemas in file headers
InfoQ AI/ML

Chapters (6)

Intro
0:09 Understanding HAVING in SQL
0:44 Order of SQL Execution
1:53 HAVING vs WHERE: Business Questions
2:52 Breaking Down HAVING vs WHERE
4:05 Let's Practice!
Up next
6-Phase SQL Roadmap 2026 | Data Analytics & Engineering | #shorts
SCALER
Watch →