Скачать или смотреть Day 16 – HANDSON, LOADING DATA, GROUP BY & HAVING in PostgreSQL | SQL for Analytics in Bhojpuri

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एह वीडियो में PostgreSQL में GROUP BY आ HAVING clause के असली मतलब आ काम समझावल गइल बा। Example खातिर Movies टेबल से Genre आ Release Year पर Grouping करके Count of Movies, Average Length of Movies आ Genre जिनकर Count 100 से जादे बा, उ सब देखावल गइल बा।

GROUP BY → Data के Category (जइसे Genre, Language) पर Aggregate Function (COUNT, SUM, AVG) लगावे खातिर।

WHERE → Raw Rows पर Filter करे खातिर (Grouping से पहिले)।

HAVING → Grouped Result पर Filter करे खातिर (जइसे जिन Genre में 100 से जादे Movies बा)।

ORDER BY + GROUP BY → Grouped Data के Meaningful Sorting खातिर।

Alias → Result Column के Clear Name दिहल जाय (जइसे COUNT(*) AS number_of_movies)।

ई Concept Analytics आ Reporting (जइसे Top Genres, Average Movie Length, High Revenue Categories) में बहुते काम आवेला।

In this tutorial, we explore the practical use of GROUP BY and HAVING clauses in PostgreSQL with hands-on examples from the Movies dataset. We demonstrate grouping data by Genre and Release Year, applying aggregate functions like COUNT, SUM, and AVG, and filtering results with HAVING.

GROUP BY → Aggregates data by categories (e.g., Genre, Language) for analytics.

WHERE → Filters rows before grouping.

HAVING → Filters groups after aggregation (e.g., genres with more than 100 movies).

ORDER BY + GROUP BY → Sorts grouped results for better insights.

Aliases → Assign clear names to aggregated columns (e.g., COUNT(*) AS number_of_movies).

This concept is widely used in Analytics & Reporting to answer questions like Top genres by movie count, Average length of movies per language, or filtering only significant groups.

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