CREATE TABLE Tech_Layoffs (
    Layoff_ID INT PRIMARY KEY,
    Company_ID INT,
    Company_Name VARCHAR(30),
    Location VARCHAR(30),
    HeadQuarter VARCHAR(10),
    LaidOff INT,
    Layoff_Date DATE,
    Layoff_Percentage DECIMAL(4,2),
    Industry VARCHAR(20),
    Funds_Raised INT,
    Country VARCHAR(20),
    Source VARCHAR(40)
);

INSERT INTO Tech_Layoffs (Layoff_id, Company_id, Company_Name, Location, HeadQuarter, LaidOff, Layoff_Date, Layoff_Percentage, Industry, Funds_Raised, Country, Source) VALUES
(1, 1, 'Intel', 'Sacramento', 'US', 22000, '2025-04-23', 0.20, 'Hardware', 12, 'United States', 'https://[Log in to view URL]'),
(2, 1, 'Intel', 'SF Bay Area', 'US', 15000, '2024-08-01', 0.15, 'Hardware', 12, 'United States', 'https://[Log in to view URL]'),
(3, 2, 'Tesla', 'Austin', 'US', 14000, '2024-04-15', 0.10, 'Transportation', 20200, 'United States', 'https://[Log in to view URL]'),
(4, 3, 'Google', 'SF Bay Area', 'US', 12000, '2023-01-20', 0.06, 'Consumer', 26, 'United States', 'https://[Log in to view URL]'),
(5, 4, 'Meta', 'SF Bay Area', 'US', 11000, '2022-11-09', 0.13, 'Consumer', 26000, 'United States', 'https://[Log in to view URL]'),
(6, 5, 'Microsoft', 'Seattle', 'US', 10000, '2023-01-18', 0.05, 'Other', 1, 'United States', 'https://[Log in to view URL]'),
(7, 6, 'Amazon', 'Seattle', 'US', 10000, '2022-11-16', 0.03, 'Retail', 108, 'United States', 'https://[Log in to view URL]'),
(8, 5, 'Microsoft', 'Seattle', 'US', 9000, '2025-07-02', 0.04, 'Other', 1, 'United States', 'https://[Log in to view URL]'),
(9, 7, 'Ericsson', 'Stockholm', 'Non US', 8500, '2023-02-24', 0.08, 'Other', 663, 'Sweden', 'https://[Log in to view URL]'),
(10, 8, 'SAP', 'Walldorf', 'Non US', 8000, '2024-01-23', 0.07, 'Other', 1300, 'Germany', 'https://[Log in to view URL]'),
(11, 6, 'Amazon', 'Seattle', 'US', 8000, '2023-01-04', 0.02, 'Retail', 108, 'United States', 'https://[Log in to view URL]'),
(12, 9, 'Salesforce', 'SF Bay Area', 'US', 8000, '2023-01-04', 0.10, 'Sales', 65, 'United States', 'https://[Log in to view URL]'),
(13, 5, 'Microsoft', 'Seattle', 'US', 6000, '2025-05-13', 0.03, 'Other', 1, 'United States', 'https://[Log in to view URL]'),
(14, 10, 'Cisco', 'SF Bay Area', 'US', 5600, '2024-08-09', 0.07, 'Infrastructure', 2, 'United States', 'https://[Log in to view URL]'),
(15, 11, 'Micron', 'Boise', 'US', 4800, '2023-01-01', 0.10, 'Hardware', 50, 'United States', 'https://[Log in to view URL]'),
(16, 10, 'Cisco', 'SF Bay Area', 'US', 4250, '2024-02-14', 0.05, 'Infrastructure', 2, 'United States', 'https://[Log in to view URL]'),
(17, 10, 'Cisco', 'SF Bay Area', 'US', 4100, '2022-11-16', 0.05, 'Infrastructure', 2, 'United States', 'https://[Log in to view URL]'),
(18, 12, 'Twitter', 'SF Bay Area', 'US', 3700, '2022-11-04', 0.50, 'Consumer', 12900, 'United States', 'https://[Log in to view URL]'),
(19, 4, 'Meta', 'SF Bay Area', 'US', 3600, '2025-02-10', 0.05, 'Consumer', 26000, 'United States', 'https://[Log in to view URL]'),
(20, 13, 'Xerox', 'Norwalk', 'US', 3000, '2024-01-03', 0.15, 'Hardware', 27200, 'United States', 'https://[Log in to view URL]'),
(21, 8, 'SAP', 'Walldorf', 'Non US', 3000, '2023-01-26', 0.03, 'Other', 1300, 'Germany', 'https://[Log in to view URL]'),
(22, 14, 'Better.com', 'New York City', 'US', 3000, '2022-03-08', 0.33, 'Real Estate', 905, 'United States', 'https://[Log in to view URL]'),
(23, 15, 'Peloton', 'New York City', 'US', 2800, '2022-02-08', 0.20, 'Fitness', 1900, 'United States', 'https://[Log in to view URL]'),
(24, 16, 'Hewlett Packard Enterprise', 'SF Bay Area', 'US', 2500, '2025-03-06', 0.05, 'Hardware', 1400, 'United States', 'https://[Log in to view URL]'),
(25, 17, 'PayPal', 'SF Bay Area', 'US', 2500, '2024-01-30', 0.09, 'Finance', 216, 'United States', 'https://[Log in to view URL]'),
(26, 18, 'Getir', 'London', 'Non US', 2500, '2023-08-22', 0.11, 'Food', 1800, 'United Kingdom', 'https://[Log in to view URL]'),
(27, 19, 'Byju''s', 'Bengaluru', 'Non US', 2500, '2022-10-12', 0.05, 'Education', 5500, 'India', 'https://[Log in to view URL]'),
(28, 20, 'Carvana', 'Phoenix', 'US', 2500, '2022-05-10', 0.12, 'Transportation', 1600, 'United States', 'https://[Log in to view URL]'),
(29, 21, 'Katerra', 'SF Bay Area', 'US', 2434, '2021-06-01', 1.00, 'Construction', 1600, 'United States', 'https://[Log in to view URL]'),
(30, 11, 'Micron', 'Boise', 'US', 2400, '2023-02-17', 0.05, 'Hardware', 50, 'United States', 'https://[Log in to view URL]'),
(31, 22, 'Indeed', 'Austin', 'US', 2200, '2023-03-22PLL', 0.15, 'HR', 5, 'United States', 'https://[Log in to view URL]'),
(32, 23, 'Farfetch', 'London', 'Non US', 2000, '2024-02-16', 0.25, 'Retail', 1700, 'United Kingdom', 'https://[Log in to view URL]'),
(33, 17, 'PayPal', 'SF Bay Area', 'US', 2000, '2023-01-31', 0.07, 'Finance', 216, 'United States', 'https://[Log in to view URL]'),
(34, 24, 'Crypto.com', 'Singapore', 'Non US', 2000, '2022-10-06', 0.30, 'Crypto', 156, 'Singapore', 'https://[Log in to view URL]'),
(35, 25, 'Zillow', 'Seattle', 'US', 2000, '2021-11-02', 0.25, 'Real Estate', 97, 'United States', 'https://[Log in to view URL]'),
(36, 26, 'Intuit', 'SF Bay Area', 'US', 1800, '2024-07-10', 0.10, 'Finance', 18, 'United States', 'https://[Log in to view URL]'),
(37, 27, 'Unity', 'SF Bay Area', 'US', 1800, '2024-01-08', 0.25, 'Other', 1300, 'United States', 'https://[Log in to view URL]'),
(38, 28, 'Workday', 'SF Bay Area', 'US', 1750, '2025-02-05', 0.08, 'HR', 230, 'United States', 'https://[Log in to view URL]'),
(39, 29, 'Wayfair', 'Boston', 'US', 1750, '2023-01-20', 0.10, 'Retail', 1700, 'United States', 'https://[Log in to view URL]'),
(40, 29, 'Wayfair', 'Boston', 'US', 1650, '2024-01-19', 0.13, 'Retail', 1700, 'United States', 'https://[Log in to view URL]'),
(41, 30, 'Northvolt', 'Stockholm', 'Non US', 1600, '2024-09-23', 0.20, 'Energy', 13800, 'Sweden', 'https://[Log in to view URL]'),
(42, 31, 'Yahoo', 'SF Bay Area', 'US', 1600, '2023-02-09', 0.20, 'Consumer', 6, 'United States', 'https://[Log in to view URL]'),
(43, 32, 'Expedia', 'Seattle', 'US', 1500, '2024-02-26', 0.08, 'Travel', 3300, 'United States', 'https://[Log in to view URL]'),
(44, 33, 'Spotify', 'Stockholm', 'Non US', 1500, '2023-12-04', 0.17, 'Media', 2100, 'Sweden', 'https://[Log in to view URL]'),
(45, 34, 'Twilio', 'SF Bay Area', 'US', 1500, '2023-02-13', 0.17, 'Other', 614, 'United States', 'https://[Log in to view URL]'),
(46, 20, 'Carvana', 'Phoenix', 'US', 1500, '2022-11-18', 0.08, 'Transportation', 1600, 'United States', 'https://[Log in to view URL]'),
(47, 35, 'Gopuff', 'Philadelphia', 'US', 1500, '2022-07-12', 0.10, 'Food', 3400, 'United States', 'https://[Log in to view URL]'),
(48, 36, 'PaisaBazaar', 'Gurugram', 'Non US', 1500, '2020-06-16', 0.50, 'Finance', 496, 'India', 'https://[Log in to view URL]'),
(49, 37, 'Ola', 'Bengaluru', 'Non US', 1400, '2020-05-20', 0.35, 'Transportation', 3800, 'India', 'https://[Log in to view URL]');

SELECT Industry, SUM(LaidOff) AS total_laid_off 
from Tech_Layoffs
GROUP BY Industry;