Interactive SQL exercises
Solved SQL exercises: practise from beginner to advanced
Practise SQL online through progressive business cases. Each exercise provides fictional data, hints, result validation and an explained solution.
62 exercises shown
SELECTNo account
Select the correct columns
Show only the name and price of each product.
Level : BeginnerOpen exerciseWHERE · ORDER BYNo account
Filter and sort orders
Find orders over €50, from highest to lowest.
Level : BeginnerOpen exerciseINNER JOINSQL Essentials
Connect customers and orders
Associate each order with the name of the customer who placed it.
Level : IntermediateCompare plansDISTINCT · LIMITDiscovery
List unique categories
Display the first three unique categories in alphabetical order.
Level : BeginnerCreate a free accountNULL · COALESCEDiscovery
Replace a NULL value with COALESCE
Display clear labeling when a customer’s phone is absent.
Level : BeginnerCreate a free accountCOUNT · SUM · AVGSQL Essentials
Calculate paid order indicators
Calculate the volume, total and average over a specific area.
Level : BeginnerCompare plansGROUP BY · HAVINGSQL Essentials
Group revenue by category
Calculate totals by category and keep only the most important ones.
Level : BeginnerCompare plansLEFT JOIN · COUNTSQL Essentials
Keep customers without orders
Count the orders of each customer without excluding new registrants.
Level : BeginnerCompare plansCTE · CASESQL Essentials
Segment customers with a CTE
Calculate the total per customer, then transform this result into a business segment.
Level : IntermediateCompare plansBETWEENDiscovery
Filter a price range
Select an inclusive interval and check its limits.
Level : BeginnerCreate a free accountINDiscovery
Filter multiple statuses
Replace multiple OR with a readable business list.
Level : BeginnerCreate a free accountLIKEDiscovery
Search for a prefix with LIKE
Use a simple text pattern without confusing prefix and content.
Level : BeginnerCreate a free accountIS NULLDiscovery
Identify missing deliveries
Distinguish a missing date from an empty string or status.
Level : BeginnerCreate a free accountExpressions · ROUNDDiscovery
Calculate a price including tax
Transform a source price with a precise formula and alias.
Level : BeginnerCreate a free accountText · ConcatenationDiscovery
Assemble a product label
Create a readable label from two columns.
Level : BeginnerCreate a free accountTRIM · UPPER · DISTINCTDiscovery
Normalize a list of cities
Create a clean dimension before counting customers.
Level : BeginnerCreate a free accountDates · EXTRACTSQL Essentials
Extract the year of registration
Derive a calendar component without transforming the scope.
Level : BeginnerCompare plansDates · GROUP BYSQL Essentials
Calculate monthly revenue
Group together several channels over the same monthly period.
Level : IntermediateCompare plansDates · WHERESQL Essentials
Analyze a quarter with limits
Filter a complete period with an exclusive upper bound.
Level : IntermediateCompare plansLEFT JOIN · HAVINGSQL Essentials
Find customers without orders
Keep the starting population and isolate the absence of activity.
Level : IntermediateCompare plansEXISTSSQL Essentials
Test for the existence of a large order
Filter a population without duplicating it by its history.
Level : IntermediateCompare plansJOIN · AVGSQL Essentials
Calculate the average basket per customer
Only aggregate orders that are actually paid.
Level : IntermediateCompare plansSubquery · AVGSQL Essentials
Compare to average price
Use an aggregated value as a dynamic threshold.
Level : IntermediateCompare plansUNIONSQL Essentials
Merge customers and prospects
Create a single list from two compatible sources.
Level : IntermediateCompare plansTraining scenariosThese contextual repetitions reinforce an existing method. They preserve every historical link without pretending to be new canonical problems.Browse 118 scenarios
118 exercises shown
SUM OVERSQL Analyst
Building a monthly total — national expeditions · training scenario
Construct a monthly total from a national shipments dataset designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this national expeditions.
Level : IntermediateCompare plansAVG OVER · ROWSSQL Analyst
Smoothing a two-month trend — domestic shipments · training scenario
Smooth a two-month trend from a national shipments dataset designed to control level of detail and variances. You must produce the result, check it on the source data and explain what it allows you to decide in this national expeditions.
Level : AdvancedCompare plansFILTERSQL Analyst
Produce conditional KPIs by region — national shipments · training scenario
Produce conditional KPIs by region from a national shipment dataset designed to control level of detail and variances. You must produce the result, check it on the source data and explain what it allows you to decide in this national expeditions.
Level : IntermediateCompare plansWindow · PercentageSQL Analyst
Measuring the contribution of each line — national expeditions · training scenario
Measure the contribution of each line using a national shipments dataset designed to control the level of detail and variances. You must produce the result, check it on the source data and explain what it allows you to decide in this national expeditions.
Level : AdvancedCompare plansCTE · ROW_NUMBERSQL Analyst
Keep the top two lines per region — national expeditions · training scenario
Maintain the top two rows per region from a national shipment dataset designed to control level of detail and variances. You must produce the result, check it on the source data and explain what it allows you to decide in this national expeditions.
Level : AdvancedCompare plansNTILESQL Analyst
Divide results into quartiles — national expeditions training scenario
Divide results into quartiles from a national shipments dataset designed to control level of detail and variances. You must produce the result, check it on the source data and explain what it allows you to decide in this national expeditions.
Level : AdvancedCompare plansAVG OVERSQL Analyst
Compare each value to its regional average — national expeditions · training scenario
Compare each value to its regional average using a national shipment dataset designed to control level of detail and variances. You must produce the result, check it on the source data and explain what it allows you to decide in this national expeditions.
Level : IntermediateCompare plansQuality · HAVINGSQL Analyst
Detect duplicate business references — national shipments training scenario
Detect duplicate business references from a national shipping dataset designed to control level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this national expeditions.
Level : IntermediateCompare plansROW_NUMBER · PARTITION BYSQL Analyst
Classify the results in each region — customer collections · training scenario
Classify results in each region based on a customer collections dataset designed to control level of detail and variances. You must produce the result, check it on the source data and explain what it allows you to decide in this customer collections.
Level : IntermediateCompare plansLAGSQL Analyst
Compare each month to the previous one — customer collections · training scenario
Compare each month to the previous one using a customer collection data set designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this customer collections.
Level : IntermediateCompare plansSUM OVERSQL Analyst
Construct a monthly total — customer collections · training scenario
Construct a monthly total from a customer collection data set designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this customer collections.
Level : IntermediateCompare plansAVG OVER · ROWSSQL Analyst
Smoothing a trend over two months — customer collections · training scenario
Smooth a two-month trend from a customer collections data set designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this customer collections.
Level : AdvancedCompare plansFILTERSQL Analyst
Produce conditional KPIs by region — customer collections · training scenario
Produce conditional KPIs by region from a customer collections dataset designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this customer collections.
Level : IntermediateCompare plansWindow · PercentageSQL Analyst
Measure the contribution of each line — customer collections · training scenario
Measure the contribution of each line using a customer collections data set designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this customer collections.
Level : AdvancedCompare plansCTE · ROW_NUMBERSQL Analyst
Keep the two best lines per region — customer collections · training scenario
Keep the two best lines per region from a customer collections dataset designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this customer collections.
Level : AdvancedCompare plansNTILESQL Analyst
Distribute the results into quartiles — customer collections · training scenario
Distribute the results into quartiles from a customer collections data set designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this customer collections.
Level : AdvancedCompare plansAVG OVERSQL Analyst
Compare each value to its regional average — customer collections · training scenario
Compare each value to its regional average using a customer collections dataset designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this customer collections.
Level : IntermediateCompare plansQuality · HAVINGSQL Analyst
Detect duplicate business references — customer collections · training scenario
Detect duplicate business references from a customer collections data set designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this customer collections.
Level : IntermediateCompare plansROW_NUMBER · PARTITION BYSQL Analyst
Rank the results in each region — acquisition campaigns · training scenario
Rank results in each region based on an acquisition campaign dataset designed to control level of detail and variances. You must produce the result, check it on the source data and explain what it allows you to decide in this acquisition campaign.
Level : IntermediateCompare plansLAGSQL Analyst
Compare each month to the previous one — acquisition campaigns · training scenario
Compare each month to the previous one using an acquisition campaign dataset designed to control the level of detail and differences. You must produce the result, check it on the source data and explain what it allows you to decide in this acquisition campaign.
Level : IntermediateCompare plansSUM OVERSQL Analyst
Build a monthly total — acquisition campaigns · training scenario
Build a monthly total from an acquisition campaign dataset designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this acquisition campaign.
Level : IntermediateCompare plansAVG OVER · ROWSSQL Analyst
Smooth a trend over two months — acquisition campaigns · training scenario
Smooth out a two-month trend from an acquisition campaign dataset designed to control the level of detail and variances. You must produce the result, check it on the source data and explain what it allows you to decide in this acquisition campaign.
Level : AdvancedCompare plansFILTERSQL Analyst
Produce conditional KPIs by region — acquisition campaigns · training scenario
Produce conditional KPIs by region from an acquisition campaign dataset designed to control the level of detail and variances. You must produce the result, check it on the source data and explain what it allows you to decide in this acquisition campaign.
Level : IntermediateCompare plansWindow · PercentageSQL Analyst
Measure the contribution of each line — acquisition campaigns · training scenario
Measure the contribution of each line from an acquisition campaign dataset designed to control the level of detail and discrepancies. You must produce the result, check it on the source data and explain what it allows you to decide in this acquisition campaign.
Level : AdvancedCompare plansTwo focused paths reuse these experiences.
Progress remains shared: no content is duplicated.
3 exercises are available without an account, followed by 12 more in the free Discovery space. The catalogue contains a total of 62 canonical experiences. Run a first query.