SQL (Structured Query Language) is the backbone of Data Analytics, enabling analysts to extract, manipulate, and analyze data stored in relational databases. Whether you are a fresher, business analyst, or experienced data professional, SQL interview questions often range from basic querying concepts to advanced analytical techniques.
This blog covers Top 25 SQL Interview Questions and Answers, progressing from basic fundamentals to advanced queries, with explanations tailored specifically for data analytics roles.
SQL is a standard language used to interact with relational databases. In data analytics, SQL is crucial for retrieving datasets, filtering records, aggregating metrics, and preparing data for visualization and reporting. It allows analysts to convert raw data into meaningful insights efficiently.
SQL commands are categorized into:
A primary key uniquely identifies each record in a table. It cannot contain NULL values and must be unique. In analytics, primary keys help ensure data integrity and enable accurate joins between tables.
NULL represents missing or unknown data. It is different from zero or an empty string. In analytics, handling NULL values properly is important to avoid incorrect calculations.
A JOIN combines data from multiple tables based on a related column. It allows analysts to analyze relationships between datasets such as customers, orders, and products.
GROUP BY groups rows with similar values and applies aggregate functions like COUNT, SUM, AVG, MAX, and MIN. It is essential for summarizing analytical data.
Aggregate functions perform calculations on a set of values:
A subquery is a query nested inside another query. It helps break down complex analytical problems into manageable steps.
Correlated subqueries depend on the outer query for execution. They are executed row by row and are often used for row-level comparisons in analytics.
Window functions perform calculations across a set of rows without collapsing the result set. Common functions include ROW_NUMBER(), RANK(), and SUM() OVER().
A CTE is a temporary result set defined using WITH clause. It improves readability and simplifies complex analytical queries.
Indexes speed up data retrieval by reducing table scans. In analytics, indexes significantly improve performance when querying large datasets.
Normalization organizes data to reduce redundancy and improve data integrity. It ensures clean and consistent analytical datasets.
Denormalization introduces redundancy to improve query performance. Analytics systems often use denormalized tables for faster reporting.
An execution plan shows how SQL processes a query. Analysts use it to optimize slow queries by identifying bottlenecks.
Duplicates can be removed using DISTINCT, GROUP BY, or ROW_NUMBER() with DELETE. Removing duplicates ensures accurate analytics results.
Running totals are calculated using window functions like SUM() OVER(ORDER BY date). They are common in trend analysis.
This can be done using subqueries, LIMIT with OFFSET, or window functions like DENSE_RANK().
Pivoting converts rows into columns to improve readability of analytical reports, especially for dashboards.
SQL enables analysts to access, clean, transform, and summarize data directly from databases, making it a core skill for data-driven decision-making.
SQL remains a must-have skill for Data Analytics professionals, regardless of experience level. Mastering both basic querying and advanced analytical techniques like window functions and CTEs can significantly improve your interview success and job performance.
Preparing these Top 25 SQL Interview Questions and Answers will help you confidently handle real-world data analytics interviews and stand out as a strong candidate.