In the bustling heart of Downtown New York, a mystery unfolds at Keiko Corp, a company renowned for its innovative ride-sharing platform, Movr. The date is June 23, 2020, a day that will be etched in the company’s history as the day of the great theft—an inside job that left everyone baffled. The company’s number one product, Movr, has revolutionized urban mobility, but now it’s the key to unraveling the enigma. Bruno, the head of security, suspects that the culprit used Movr to facilitate their escape. With full access granted to Keiko’s employee database and Movr’s transactional records, the hunt for clues begins. The first step is to restore the Movr and Movr_Employees databases. The Movr database, containing a wealth of ride data, and the Movr_Employees database, detailing employee information, are critical pieces of the puzzle. As the SQL queries start running, the data unveils patterns. Rides around Keiko Corp’s coordinates on the day of the incident are scrutinized, revealing a list of vehicles and their owners. Yet, interrogating the drivers leads nowhere—the real focus should have been on the riders. The plot thickens as the investigation shifts to the riders who used Movr on that fateful day. Unique riders are identified, and their names are noted as potential suspects. But the breakthrough comes when cross-referencing data between the two databases using the dblink extension. It’s a meticulous process, matching employee movements with ride data, searching for the missing link. As the story unfolds, the SQL masters at Keiko Corp inch closer to the truth, piecing together digital breadcrumbs left behind in the data. The great theft of Keiko Corp is more than a mere crime; it’s a test of wits, a challenge of analytical prowess, and a tale of technology’s double-edged sword.
** NOTE ** : The notations used in the project varies according to query style make sure you notice that