Context

In SQL interviews, candidates are often asked how they would analyze a new dataset before writing complex queries. Interviewers want to see whether you can move from raw tables to reliable insights in a structured way.

Core question

Explain how you would approach analyzing a dataset using SQL. Your answer should cover:

1. How you inspect the table structure and understand the available fields
2. How you check data quality issues such as NULLs, duplicates, and unexpected values
3. How you summarize the data with basic aggregations and grouping
4. How you decide what follow-up queries to run based on early findings

Scope guidance

Keep the answer practical and SQL-oriented. The interviewer is not looking for advanced modeling or machine learning. Focus on a clear, step-by-step workflow for exploring, validating, and summarizing data in PostgreSQL, and mention the kinds of queries you would write at each stage.

Schema inspection

The first step is understanding what data exists, what each column represents, and which fields may be useful for filtering or grouping. In practice, this means checking column names, data types, and likely keys before doing any analysis.

SELECT column_name, data_type
FROM information_schema.columns
WHERE table_name = 'orders'
ORDER BY ordinal_position;

Data quality validation

Before trusting any metric, you should check for missing values, duplicates, and invalid ranges. Early validation prevents incorrect conclusions caused by bad records or misunderstood fields.

SELECT COUNT(*) AS total_rows,
       COUNT(*) FILTER (WHERE customer_id IS NULL) AS null_customer_id,
       COUNT(*) FILTER (WHERE amount IS NULL) AS null_amount
FROM orders;

Descriptive aggregation

Basic summaries help you understand scale, distribution, and major segments in the data. Common starting points are row counts, averages, totals, minimums, maximums, and grouped summaries by category or date.

SELECT status, COUNT(*) AS order_count, AVG(amount) AS avg_amount
FROM orders
GROUP BY status
ORDER BY order_count DESC;

Iterative drill-down

Analysis is usually iterative: broad summaries reveal patterns, and then follow-up queries investigate anomalies or interesting segments. A good analyst narrows from overview metrics into specific subsets that explain what is happening.

SELECT order_date, COUNT(*) AS orders
FROM orders
GROUP BY order_date
ORDER BY order_date;

Business-focused interpretation

SQL analysis is not just about writing queries; it is about connecting results to the business question. Strong answers explain how the outputs would guide decisions, identify risks, or suggest the next metric to examine.