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SQL FOR EVERYONE

Name: SQL FOR EVERYONE
Rating: 4.7 (48 reviews)

SQL FOR DATA ANALYSTS , DATA ENGINEERS AND DATA SCIENTISTS

Created byHarshit Bhadiyadra

Last updated 7/2025

English

English [Auto],

What you'll learn

MORE THAN 100s OF INTERMEDIATE TO ADVANCED SQL QUERIES ASKED IN INTERVIEWS
PRACTICAL AND THEORICAL INTERVIEW QUESTIONS ASKED IN INTERVIEWS
FAANG AND MAANG SQL QUERIES WITH SOLUTIONS
CRACK YOUR NEXT SQL INTERVIEWS

Course content

4 sections • 56 lectures • 17h 44m total length

INTRODUCTION ABOUT THE COURSE3:04
COURSE CONTENT AS BELOW :
Around 100 + FAANG and MAANG SQL Queries with Solutions(Practical approach to solve the queries)
SR NO QUERY
1 Signup Activation Rate [TikTok SQL Interview Question]
2 Supercloud Customer [Microsoft SQL Interview Question]
3 Card Launch Success [JPMorgan Chase SQL Interview Question]
4 Pharmacy Analytics (Part 4) [CVS Health SQL Interview Question]
5 First Transaction [Etsy SQL Interview Question]
6 Photoshop Revenue Analysis [Adobe SQL Interview Question]
7 Pharmacy Analytics (Part 2) [CVS Health SQL Interview Question]
8 Pharmacy Analytics (Part 1) [CVS Health SQL Interview Question]
9 Pharmacy Analytics (Part 3) [CVS Health SQL Interview Question]
10 Pharmacy Analytics (Part 4) [CVS Health SQL Interview Question]
11 LinkedIn Power Creators (Part 2) [LinkedIn SQL Interview Question]
12 Email Table Transformation [Facebook SQL Interview Question]
13 Average Deal Size (Part 2) [Salesforce SQL Interview Question]
14 Y-on-Y Growth Rate [Wayfair SQL Interview Question]
15 User Shopping Sprees [Amazon SQL Interview Question]
16 2nd Ride Delay [Uber SQL Interview Question]
17 Histogram of Users and Purchases [Walmart SQL Interview Question]
18 User Session Activity [Twitter SQL Interview Question]
19 Unique Money Transfer Relationships [PayPal SQL Interview Question]
20 ApplePay Volume
21 Uniquely Staffed Consultants [Accenture SQL Interview Question]
22 Senior Managers [Google SQL Interview Question]
23 Sending vs. Opening Snaps [Snapchat SQL Interview Question]
24 Data Science Skills [LinkedIn SQL Interview Question]
25 Histogram of Tweets [Twitter SQL Interview Question]
26 Average Post Hiatus (Part 1) [Facebook SQL Interview Question]
27 Laptop vs. Mobile Viewership [New York Times SQL Interview Question]
28 Teams Power Users [Microsoft SQL Interview Question]
29 Duplicate Job Listings [Linkedin SQL Interview Question]
30 Cities With Completed Trades [Robinhood SQL Interview Question]
31 Final Account Balance [Paypal SQL Interview Question]
32 QuickBooks vs TurboTax [Intuit SQL Interview Question]
33 App Click-through Rate (CTR) [Facebook SQL Interview Question]
34 User's Third Transaction [Uber SQL Interview Question]
35 Active User Retention [Facebook SQL Interview Question]
36 Webinar Popularity [Snowflake SQL Interview Question]
37 Monthly Merchant Balance [Visa SQL Interview Question]
38 Tweets' Rolling Averages [Twitter SQL Interview Question]
39 Who Made Quota_Oracle SQL Interview Question
40 Highest Number of Products [eBay SQL Interview Question]
41 Spare Server Capacity [Microsoft SQL Interview Question]
42 Top Rated Businesses [Yelp SQL Interview Question]
43 Ad Campaign ROAS [Google SQL Interview Question]
44 Trade In Payouts [Apple SQL Interview Question]
45 Cumulative Purchases by Product Type [Amazon SQL Interview Question]
46 Invalid Search Results [Google SQL Interview Question]
47 Repeat Purchases on Multiple Days [Stitch Fix SQL Interview Question]
48 International Call Percentage [Verizon SQL Interview Question]
49 Follow-Up Airpod Percentage [Apple SQL Interview Question]
50 Subject Matter Experts [Accenture SQL Interview Question]
51 Compressed Mode [Alibaba SQL Interview Question]
52 Consecutive Filing Years [Intuit SQL Interview Question]
53 Spotify Streaming History [Spotify SQL Interview Question]
54 Average Review Ratings [Amazon SQL Interview Question]
55 Highest-Grossing Items [Amazon SQL Interview Question]
56 Queries Quality and Percentage
57 Reported Posts
58 Customers Who Bought Products A and B but Not C
59 Game Play Analysis IV
60 Game Play Analysis III
61 Highest Grade For Each Student
62 Recyclable and Low Fat Products
63 Invalid Tweets
64 The Number of Rich Customers
65 The Latest Login in 2020
66 Game Play Analysis I
67 Warehouse Manager
68 Customer Placing the Largest Number of Orders
69 Find Total Time Spent by Each Employee
70 Immediate Food Delivery I
71 Bank Account Summary II
72 Actors and Directors Who Cooperated At Least Three Times
73 Customer Order Frequency
74 Daily Leads and Partners
75 Page Recommendations
76 Sellers With No Sales
77 Sales Person
78 Employees With Missing Information
79 Grand Slam Titles
80 Find the Team Size
81 Students With Invalid Departments
82 Department Highest Salary
83 Maximum Transaction Each Day
84 The Most Recent Orders for Each Product
85 Game Play Analysis II
86 Project Employees III
87 Find the Quiet Students in All Exams
88 Leetflex Banned Accounts
89 Friendly Movies Streamed Last Month
90 Rank Scores
91 Consequtive Numbers
92 Department Top Three Salaries
93 Classes More Than 5 Students
94 Swap_Salary
95 User Activity for the Past 30 Days I
96 Market Analysis I
97 Capital Gain_Loss
98 Top Travellers
99 Customer Who Visited but Did Not Make Any Transactions
100 Workers With The Highest Salaries
101 Most Profitable Companies
102 Activity Rank
103 Finding User Purchases
1_FAANG_SQL_PRODUCT_BASED34:43
2_FAANG_SQL_PRODUCT_BASED9:10
Identify power creators by comparing each individual's followers with the sum of followers of the companies they work for, using inner joins and aggregation to output IDs in ascending order.
3_FAANG_SQL_PRODUCT_BASED4:46
4_FAANG_SQL_PRODUCT_BASED4:51
5_FAANG_SQL_PRODUCT_BASED9:26
Identify the top three most profitable drugs in pharmacy sales by calculating profit as total sales minus cost of goods sold, using ranking and derived tables.
6_FAANG_SQL_PRODUCT_BASED7:43
7_FAANG_SQL_PRODUCT_BASED5:19
Analyze Photoshop purchasers and compute each customer's total spend on non-photoshop products using the transactions table, grouping by customer ID and sorting by customer ID ascending.
8_FAANG_SQL_PRODUCT_BASED6:31
9_FAANG_SQL_PRODUCT_BASED5:25
Find the first login date for each player by computing the minimum event_date in the activity table, or by using a dense_rank window partitioned by player_id.
10_FAANG_SQL_PRODUCT_BASED3:43
11_FAANG_SQL_PRODUCT_BASED4:12
12_FAANG_SQL_PRODUCT_BASED15:55
Master product-based sql questions by filtering candidates with Python, Tableau, and PostgreSQL using group by and count(distinct skill); apply row_number to identify first transactions per user.
13_FAANG_SQL_PRODUCT_BASED17:50
Learn to compute activation rates from emails and text tables using inner joins and casts, round to two decimals, and apply window functions and CTEs for complex SQL problems.
15_FAANG_SQL_PRODUCT_BASED4:15
Sum the deal sizes per employee, join the deals and quotas tables, and output whether each employee hits their annual quota, ordered by employee ID.
FAANG_SQL_PRODUCT_BASED7:21
Learn to write SQL queries to count distinct reported posts by reason for yesterday, grouping by the report reason (spam, racism, etc.), and build queries step by step for efficiency.
FAANG_SQL_PRODUCT_BASED9:40
FAANG_SQL_PRODUCT_BASED6:38
explain how to compute user balances by summing transaction amounts across accounts, then filter for balances above 10,000 using a join between users and transactions.
FAANG_SQL_PRODUCT_BASED3:58
FAANG_SQL_PRODUCT_BASED7:37
Solve a faang-style sql problem by joining warehouse and products tables on product_id, calculating total cubic feet by multiplying width, length, height and units, then grouping by warehouse name.
FAANG_SQL_PRODUCT_BASED4:43
Solve a leetcode sql problem on the orders table to find the customer number with the most orders by counting per customer and using group by and max.
2nd Ride Delay [Uber SQL Interview Question]10:30

PART-1_SQL_QUERIES_ASKED_IN_FAANG_AND_MNCs Companies1:10:33
Demonstrates faang-style sql interview queries: self joins for salary comparison, duplicates removal with group by and dense_rank, department-wise ranking, and multi-ctes for transactions and analytics.
PART-2_SQL_QUERIES_ASKED_IN_FAANG_AND_MNCs Companies1:05:07
PART-3 SQL QUERIES ASKED IN FAANG AND MNCs Companies1:02:54
PART-4 SQL QUERIES ASKED IN FAANG AND MNCs Companies59:30
PART-5 SQL QUERIES ASKED IN FAANG AND MNCs Companies46:23
PART-6 SQL QUERIES ASKED IN FAANG AND MNCs Companies18:35
PART-7 SQL QUERIES ASKED IN FAANG AND MNCs Companies16:31
Master running totals with sum over partition by and top grades with rank over partition by. Apply window functions and filtering to activity and enrollment data.
PART-8 SQL QUERIES ASKED IN FAANG AND MNCs Companies15:30
PART-9 SQL QUERIES ASKED IN FAANG AND MNCs Companies15:42
Master advanced sql querying used in FAANG and MNC interviews, including calculating the percentage of immediate orders from a delivery table and computing user balances from transactions. Learn to identify actor-director collaborations and analyze cross-table relationships with joins and group by.
PART-10 SQL QUERIES ASKED IN FAANG AND MNCs Companies6:26
PART-11 SQL QUERIES ASKED IN FAANG AND MNCs Companies5:14
Learn to report the names of sellers who did not make any sales in 2020 by joining customers, orders, and seller tables with a left join and sorting by seller.
PART-12 SQL QUERIES ASKED IN FAANG AND MNCs Companies7:32
PART-13 SQL QUERIES ASKED IN FAANG AND MNCs Companies8:50
PART-14 SQL QUERIES ASKED IN FAANG AND MNCs Companies5:37
PART-15 SQL QUERIES ASKED IN FAANG AND MNCs Companies7:05
PART-16 SQL QUERIES ASKED IN FAANG AND MNCs Companies8:29
Learn to compute department-wise maximum salary by joining employee and department tables, and identify day-wise maximum transaction IDs using group by and rank methods, outputting results in ascending transaction IDs.
PART-17 SQL QUERIES ASKED IN FAANG AND MNCs Companies6:20
PART-18 SQL QUERIES ASKED IN FAANG AND MNCs Companies4:32
PART-19_QUERIES ASKED IN FAANG AND MNCS1:08:43
PART-20_QUERIES ASKED IN FAANG AND MNCS1:11:04
Master sql query writing - the next level explores faang interview questions on revenue analysis, losses and profits, subqueries, joins, derived tables, ranking functions, and top-n reporting.
PART-21_QUERIES ASKED IN FAANG AND MNCS1:05:22
Explore advanced SQL with FAANG and MNCS problems, including multi-table joins to identify power creators, transforming emails to separate columns, revenue segmentation, year-over-year growth, and shopping spree detection.

1_DIFFERENCE BETWEEN DELETE AND TRUNCATE3:44
Explore the differences between delete and truncate: delete uses a where clause and manual commits to remove rows; truncate purges all data automatically as a fast DDL operation.
2_DIFFERENCE BETWEEN UNION AND UNION ALL6:55
Learn the difference between union and union all: union returns distinct rows, union all returns all rows, with rules for same column count, data type, and order, as set operators.
3_STORED PROCEDURE AND FUNCTIONS DIFFERENCE12:23
Compare stored procedures and functions to understand differences in return values and how they execute statements. Learn calling, parameters, exception handling, and transactions, including table variables and temp tables.
4_IDENTIFY AND DELETE DUPLICATE DATA15:34
Identify and delete duplicate rows by grouping on name, salary, and department with min(id), or by ranking with partition by these columns and keeping the first row.
5_DEPARTMENT_WISE_NTH_HIGHEST_SALARY19:53
6_DEPT WISE TOP 3 HIGHEST SALARY9:56
Compute the top three highest salaries across all departments using partition by and dense_rank, then switch to ranks 1–3 to return across-department results.
7_RECURSIVE_CTE WITH EXAMPLE9:55
Learn how recursive CTEs in SQL reference themselves to process hierarchical data, using an anchor member and union all, illustrated by an employee table with a CEO and escalating levels.
8_EXPLAIN ROW_NUMBER - RANK AND DENSE RANK FUNCTIONS8:42
9_EXPLAIN_CORELATED_SUBQUERY WITH EXAMPLE9:20
Learn about correlated subqueries in SQL, where the inner query depends on the outer one and is evaluated for each row to enable dynamic, row-by-row filtering.
10_REAL TIME FINANCE DATA COMPARISON SCENARIO15:37

Requirements

Basic SQL Knowledge
No Programming SQL Needed, I will teach all the queries from scratch.

Description

MORE THAN 70 + SQL QUERIES ASKED IN FAANG AND MAANG - PRODUCT BASED COMPANIES - WITH SOLUTIONS

AND SQL CONCEPTS EXPLAINED

THIS COURSE HAS COVERED THE MANY OF THE SQL QUERIES COVERED IN DIFFERENT PLATFORMS LIKE LEETCODE, DATALEMUR AND SO NO...

1 Signup Activation Rate [TikTok SQL Interview Question]

2 Supercloud Customer [Microsoft SQL Interview Question]

3 Card Launch Success [JPMorgan Chase SQL Interview Question]

4 Pharmacy Analytics (Part 4) [CVS Health SQL Interview Question]

5 First Transaction [Etsy SQL Interview Question]

6 Photoshop Revenue Analysis [Adobe SQL Interview Question]

7 Pharmacy Analytics (Part 2) [CVS Health SQL Interview Question]

8 Pharmacy Analytics (Part 1) [CVS Health SQL Interview Question]

9 Pharmacy Analytics (Part 3) [CVS Health SQL Interview Question]

10 Pharmacy Analytics (Part 4) [CVS Health SQL Interview Question]

11 LinkedIn Power Creators (Part 2) [LinkedIn SQL Interview Question]

12 Email Table Transformation [Facebook SQL Interview Question]

13 Average Deal Size (Part 2) [Salesforce SQL Interview Question]

14 Y-on-Y Growth Rate [Wayfair SQL Interview Question]

15 User Shopping Sprees [Amazon SQL Interview Question]

16 2nd Ride Delay [Uber SQL Interview Question]

17 Histogram of Users and Purchases [Walmart SQL Interview Question]

18 User Session Activity [Twitter SQL Interview Question]

19 Unique Money Transfer Relationships [PayPal SQL Interview Question]

20 ApplePay Volume

21 Uniquely Staffed Consultants [Accenture SQL Interview Question]

22 Senior Managers [Google SQL Interview Question]

23 Sending vs. Opening Snaps [Snapchat SQL Interview Question]

24 Data Science Skills [LinkedIn SQL Interview Question]

25 Histogram of Tweets [Twitter SQL Interview Question]

26 Average Post Hiatus (Part 1) [Facebook SQL Interview Question]

27 Laptop vs. Mobile Viewership [New York Times SQL Interview Question]

28 Teams Power Users [Microsoft SQL Interview Question]

29 Duplicate Job Listings [Linkedin SQL Interview Question]

30 Cities With Completed Trades [Robinhood SQL Interview Question]

31 Final Account Balance [Paypal SQL Interview Question]

32 QuickBooks vs TurboTax [Intuit SQL Interview Question]

33 App Click-through Rate (CTR) [Facebook SQL Interview Question]

34 User's Third Transaction [Uber SQL Interview Question]

35 Active User Retention [Facebook SQL Interview Question]

36 Webinar Popularity [Snowflake SQL Interview Question]

37 Monthly Merchant Balance [Visa SQL Interview Question]

38 Tweets' Rolling Averages [Twitter SQL Interview Question]

39 Who Made Quota_Oracle SQL Interview Question

40 Highest Number of Products [eBay SQL Interview Question]

41 Spare Server Capacity [Microsoft SQL Interview Question]

42 Top Rated Businesses [Yelp SQL Interview Question]

43 Ad Campaign ROAS [Google SQL Interview Question]

44 Trade In Payouts [Apple SQL Interview Question]

45 Cumulative Purchases by Product Type [Amazon SQL Interview Question]

46 Invalid Search Results [Google SQL Interview Question]

47 Repeat Purchases on Multiple Days [Stitch Fix SQL Interview Question]

48 International Call Percentage [Verizon SQL Interview Question]

49 Follow-Up Airpod Percentage [Apple SQL Interview Question]

50 Subject Matter Experts [Accenture SQL Interview Question]

51 Compressed Mode [Alibaba SQL Interview Question]

52 Consecutive Filing Years [Intuit SQL Interview Question]

53 Spotify Streaming History [Spotify SQL Interview Question]

54 Average Review Ratings [Amazon SQL Interview Question]

55 Highest-Grossing Items [Amazon SQL Interview Question]

56 Queries Quality and Percentage

57 Reported Posts

58 Customers Who Bought Products A and B but Not C

59 Game Play Analysis IV

60 Game Play Analysis III

61 Highest Grade For Each Student

62 Recyclable and Low Fat Products

63 Invalid Tweets

64 The Number of Rich Customers

65 The Latest Login in 2020

66 Game Play Analysis I

67 Warehouse Manager

68 Customer Placing the Largest Number of Orders

69 Find Total Time Spent by Each Employee

70 Immediate Food Delivery I

71 Bank Account Summary II

72 Actors and Directors Who Cooperated At Least Three Times

73 Customer Order Frequency

74 Daily Leads and Partners

75 Page Recommendations

76 Sellers With No Sales

77 Sales Person

78 Employees With Missing Information

79 Grand Slam Titles

80 Find the Team Size

81 Students With Invalid Departments

82 Department Highest Salary

83 Maximum Transaction Each Day

84 The Most Recent Orders for Each Product

85 Game Play Analysis II

86 Project Employees III

87 Find the Quiet Students in All Exams

88 Leetflex Banned Accounts

89 Friendly Movies Streamed Last Month

90 Rank Scores

91 Consequtive Numbers

92 Department Top Three Salaries

93 Classes More Than 5 Students

94 Swap_Salary

95 User Activity for the Past 30 Days I

96 Market Analysis I

97 Capital Gain_Loss

98 Top Travellers

99 Customer Who Visited but Did Not Make Any Transactions

100 Workers With The Highest Salaries

101 Most Profitable Companies

102 Activity Rank

103 Finding User Purchases

With Advanced Concepts

Who this course is for:

Professionals who are preparing for SQL Developer Interviews
For Data Analysts
For Data Engineers

SQL FOR EVERYONE

What you'll learn

Explore related topics

Course content

SQL QUERIES ASKED IN FAANG AND MNCs PRODUCT BASED22 lectures • 3hr 7min

SQL QUERIES ASKED IN FAANG AND MAANG - NEXT LEVEL21 lectures • 10hr 36min

CRACK YOUR NEXT SQL INTERVIEW10 lectures • 1hr 52min

ADVANCED SQL CONCEPTS3 lectures • 2hr 10min

Requirements

Description

Who this course is for: