Curated real-world problems from top tech companies. Master complex queries with realistic datasets and interactive validation.
Problems modeled after actual interview questions from FAANG and startups.
Run your SQL in the browser and get immediate validation against target results.
From basic filtering to complex recursive CTEs and performance optimization.
A big SQL interview list can feel noisy. These paths group the questions by how most candidates actually prepare: fundamentals first, analytics second, then harder logic and performance-oriented prompts.
Use this path if you still need confidence with filtering, grouping, and basic ranking questions.
Focus here if your interviews involve product analytics, conversion funnels, or KPI reporting.
These are better once you are comfortable with joins, window functions, and edge-case handling.
Interview problems are easier when you already own the underlying pattern. Use these guides to close concept gaps before you brute-force more question volume.
Essential for ranking, running totals, and cohort-style interview questions.
Useful for multi-step analytics problems where the cleanest solution builds logic in stages.
Important for conditional aggregation, metrics questions, and reporting-style prompts.
Helpful when interviewers ask about performance tradeoffs after you finish the query.
Interview questions are longer and noisier by design. When a concept still feels shaky, switch to a smaller challenge page, get a clean rep, then come back to the company-style prompt.
Find the second distinct highest salary without accidentally returning the same maximum twice.
Assign tied scores the same rank while keeping the next rank consecutive with no gaps.
Compute daily cancellation rate after excluding any trip where either the client or driver is banned.
Return only the customers whose distinct purchased product set covers every product listed in the catalog.
Interview prep gets easier when you stop treating every question as isolated. These paths connect the blog clusters and tool workflows that support the most common SQL interview patterns.
Performance
Best when the query already exists and you need to understand why it is slow, what the planner is doing, and which anti-patterns to remove first.
Data Conversion
Use this path when payloads, spreadsheets, or seed files need to become relational inserts quickly without writing one-off scripts.
Schema Design
Useful when the hard part is table structure, relationships, migration impact, or how to explain a schema clearly before implementation.
Advanced SQL
A good path when the challenge is less about syntax and more about understanding multi-step logic, CTEs, window functions, and interview-style problem solving.
Technical interviews at companies like Google, Meta, Amazon, and Netflix almost always include a SQL component. They are not just looking for someone who can write a simple SELECT statement; they want to see if you can handle complex data relationships, optimize for performance, and use advanced features like Window Functions and Common Table Expressions (CTEs).
SQL skills are tested because they directly translate to real job responsibilities. Data analysts, backend engineers, and even product managers need to query databases daily. Companies use SQL interviews to filter candidates who can think analytically and translate business requirements into precise queries.
SQL tests your ability to break down business requirements into logical, step-by-step operations.
Real-world data is messy. Managing NULLs, duplicates, and unexpected values is critical.
Understanding the cost of JOINs, subqueries, and indexing is essential for large-scale data.
Understand that FROM runs before WHERE, and GROUP BY runs before HAVING. This mental model is the foundation of debugging complex queries.
DENSE_RANK, ROW_NUMBER, and LEAD/LAG are the most common advanced topics in senior-level interviews.
Our playground uses a real SQLite engine, allowing you to experiment and learn from mistakes in a safe environment.
Real interviews have time pressure. Practice solving problems in 15-20 minutes to build speed and confidence.
SQL interview questions typically fall into several categories. Understanding what to expect helps you focus your preparation on high-impact areas.
Foundation skills tested in entry-level roles. Expect 1-2 of these as warmups.
Core competency for most data roles. The majority of interview questions fall here.
Expected for senior roles at FAANG. Differentiates strong candidates.
Window functions perform calculations across a set of rows related to the current row without collapsing them. Unlike GROUP BY, they preserve individual rows while adding computed columns.
Key functions: ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD(), SUM() OVER()
CTEs (defined with the WITH keyword) allow you to create named temporary result sets that can be referenced within the main query. They make complex queries more readable and maintainable.
Recursive CTEs are particularly useful for hierarchical data like org charts, category trees, or finding connected components in graphs.
Understanding when to use INNER JOIN vs LEFT JOIN vs subqueries is crucial. Left joins preserve all rows from the left table, which is important for finding "missing" data or calculating metrics with optional relationships.
Self-joins are common for comparing rows within the same table—finding employees with higher salaries than their managers, for example.
Subqueries in the WHERE clause filter based on aggregated values. Correlated subqueries reference the outer query columns and execute once per outer row, which is powerful but potentially slow.
Know when EXISTS is more efficient than IN, especially with large datasets.
When facing an SQL interview question, follow this structured approach to break down the problem systematically.
Read the problem carefully. Identify the expected output columns and any edge cases mentioned.
Examine the table schemas. Note primary/foreign keys, data types, and potential NULLs.
Sketch the query structure. Decide on JOINs, aggregations, and filtering logic before coding.
Write the query incrementally. Test intermediate results to catch errors early.
Check edge cases: empty tables, NULLs, duplicates. Optimize if time permits.
Most companies are dialect-agnostic. Mastering standard SQL (ANSI) is the best approach. SQLite, PostgreSQL, and MySQL are very similar for interview purposes. Focus on core concepts rather than vendor-specific syntax.
Consistency beats intensity. Solving 1-2 problems daily for 2-3 weeks is usually enough to build the necessary muscle memory. Start with easy problems and gradually increase difficulty.
Yes, especially for senior roles. You should be able to explain why you chose a JOIN over a subquery, how indexes improve search performance, and the trade-offs of different approaches.
Rarely, but companies like Google and Snowflake occasionally use them for hierarchical data problems like org charts or bill-of-materials. Worth learning if targeting senior positions.
Understanding concepts is more important than memorization. Interviewers care about your problem-solving approach. However, fluency with common patterns (window functions, CTEs) helps you work faster under time pressure.
Talk through your thought process. Interviewers value communication. Ask clarifying questions, explain your approach, and describe what you'd do if you had more time. Partial solutions with good reasoning are valuable.