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Netflix Analytics Engineer Interview Question (SQL) 🚀
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### Scenario Overview
Netflix wants to analyze user engagement with their platform. Imagine you have a table called netflix_data with the following columns:
- user_id: Unique identifier for each user
- subscription_plan: Type of subscription (e.g., Basic, Standard, Premium)
- genre: Genre of the content the user watched (e.g., Drama, Comedy, Action)
- timestamp: Date and time when the user watched a show
- watch_duration: Length of time (in minutes) a user spent watching
- country: User’s country

The main objective is to figure out how to get insights into user behavior, such as which genres are most popular or how watch duration varies across subscription plans.

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### Typical Interview Question

> “Using the netflix_data table, find the top 3 genres by average watch duration in each subscription plan, and return both the genre and the average watch duration.”

This question tests your ability to:
1. Filter or group data by subscription plan.
2. Calculate average watch duration within each group.
3. Sort results to find the “top 3” within each group.
4. Handle tie situations or edge cases (e.g., if there are fewer than 3 genres).

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### Step-by-Step Approach

1. Group and Aggregate
Use the GROUP BY clause to group by subscription_plan and genre. Then, use an aggregate function like AVG(watch_duration) to get the average watch time for each combination.

2. Rank Genres
You can utilize a window function—commonly ROW_NUMBER() or RANK()—to assign a ranking to each genre within its subscription plan, based on the average watch duration. For example:

   AVG(watch_duration) OVER (PARTITION BY subscription_plan ORDER BY AVG(watch_duration) DESC)

(Note that in many SQL dialects, you’ll need a subquery because you can’t directly apply an aggregate in the ORDER BY of a window function.)

3. Select Top 3
After ranking rows in each partition (i.e., subscription plan), pick only the top 3 by watch duration. This could look like:

   SELECT subscription_plan,
          genre,
          avg_watch_duration
   FROM (
       SELECT subscription_plan,
              genre,
              AVG(watch_duration) AS avg_watch_duration,
              ROW_NUMBER() OVER (
                  PARTITION BY subscription_plan 
                  ORDER BY AVG(watch_duration) DESC
              ) AS rn
       FROM netflix_data
       GROUP BY subscription_plan, genre
   ) ranked
   WHERE rn <= 3;

4. Validate Results
- Make sure each subscription plan returns up to 3 genres.
- Check for potential ties. Depending on the question, you might use RANK() or DENSE_RANK() to handle ties differently.
- Confirm the data type and units for watch_duration (minutes, seconds, etc.).

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### Key Takeaways
- Window Functions: Essential for ranking or partitioning data.
- Aggregations & Grouping: A foundational concept for Analytics Engineers.
- Data Validation: Always confirm you’re interpreting columns (like watch_duration) correctly.

By mastering these techniques, you’ll be better prepared for SQL interview questions that delve into real-world scenarios—especially at a data-driven company like Netflix.

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12:20

19.02.2025

SQL Tricks to Level Up Your Database Skills 🚀

SQL is a powerful language, but mastering a few clever tricks can make your queries faster, cleaner, and more efficient. Here are some cool SQL hacks to boost your skills:

1️⃣ Use COALESCE Instead of CASE
Instead of writing a long CASE statement to handle NULL values, use COALESCE():

SELECT COALESCE(name, 'Unknown') FROM users;

This returns the first non-null value in the list.

2️⃣ Generate Sequential Numbers Without a Table
Need a sequence of numbers but don’t have a numbers table? Use GENERATE_SERIES (PostgreSQL) or WITH RECURSIVE (MySQL 8+):

SELECT generate_series(1, 10);

3️⃣ Find Duplicates Quickly
Easily identify duplicate values with GROUP BY and HAVING:

SELECT email, COUNT(*) 
FROM users 
GROUP BY email 
HAVING COUNT(*) > 1;

4️⃣ Randomly Select Rows
Want a random sample of data? Use:
- PostgreSQL: ORDER BY RANDOM()
- MySQL: ORDER BY RAND()
- SQL Server: ORDER BY NEWID()

5️⃣ Pivot Data Without PIVOT (For Databases Without It)
Use CASE with SUM() to pivot data manually:

SELECT 
    user_id,
    SUM(CASE WHEN status = 'active' THEN 1 ELSE 0 END) AS active_count,
    SUM(CASE WHEN status = 'inactive' THEN 1 ELSE 0 END) AS inactive_count
FROM users
GROUP BY user_id;

6️⃣ Efficiently Get the Last Inserted ID
Instead of running a separate SELECT, use:
- MySQL: SELECT LAST_INSERT_ID();
- PostgreSQL: RETURNING id;
- SQL Server: SELECT SCOPE_IDENTITY();

SQL is full of hidden gems—what are your favorite tricks? Let’s discuss in the comments! 💬🔍 #SQL #Database

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