A data analyst is conducting exploratory data analysis (EDA) on a new retail dataset in BigQuery. The analyst needs to understand the distribution of customer purchase amounts, calculate descriptive summary statistics (such as mean, min, max, and percentiles), and inspect potential outliers directly without leaving the Google Cloud console. Which approach should the analyst use to accomplish this task?

Execute a SQL query using functions such as AVG(), MIN(), MAX(), and APPROX_QUANTILES(), then explore and visualize the resulting distribution using integrated charting in BigQuery.: ### What this solution is This approach leverages **BigQuery SQL aggregate functions** and statistical functions—such as `AVG()`, `MIN()`, `MAX()`, and `APPROX_QUANTILES()`—combined with **BigQuery console data visualization features** (such as built-in Explore charts or Looker Studio integration) to conduct exploratory data analysis (**EDA**). ### How it meets the requirements - **Descriptive summary statistics (✓):** Standard aggregate functions (`AVG`, `MIN`, `MAX`, `COUNT`, `STDDEV`) provide essential measures of central tendency and spread across numeric columns. - **Quantiles and percentile distributions (✓):** The `APPROX_QUANTILES(order_amount, 100)` function divides numerical data into percentiles, allowing analysts to identify the median (50th percentile), interquartile ranges (25th and 75th percentiles), and extreme values. - **Outlier detection (✓):** Computing percentiles alongside standard deviations reveals values that fall far outside normal distribution boundaries. - **In-console visualization (✓):** BigQuery Studio and the BigQuery console allow users to visualize query results directly through integrated charts or export directly to Looker Studio for immediate visual exploration without extracting data externally. ### Key advantages and capabilities - **In-place computation:** Analyzes massive datasets directly inside BigQuery's scalable, distributed architecture without moving raw data. - **Fast percentile approximations:** `APPROX_QUANTILES()` computes approximate percentiles efficiently over billions of rows with minimal compute overhead. - **Direct visual exploration:** Analysts can generate bar charts, scatter plots, or histograms from query results in seconds. - **ANSI SQL compliance:** Uses standard SQL expressions that data practitioners can readily write and modify. ### Why this is optimal Using built-in SQL statistical functions within BigQuery is the standard, native practice for exploratory data analysis in Google Cloud. It eliminates data egress costs, avoids unnecessary data transformations, and delivers fast, interactive insights on large-scale analytical datasets.