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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?
Run the EXPLAIN statement on the table in BigQuery to generate automated summary statistics and visual histograms of numerical fields.
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.
Export the dataset to Cloud SQL for MySQL and view the Query Insights dashboard to compute percentiles and detect anomalies.
Query the dataset using SELECT * and inspect the database performance metrics in Spanner Query Insights to observe data spread.
Run the EXPLAIN statement on the table in BigQuery to generate automated summary statistics and visual histograms of numerical fields.
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.
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).
StudyCerts
StudyCertsAVG, MIN, MAX, COUNT, STDDEV) provide essential measures of central tendency and spread across numeric columns.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.APPROX_QUANTILES() computes approximate percentiles efficiently over billions of rows with minimal compute overhead.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.
Export the dataset to Cloud SQL for MySQL and view the Query Insights dashboard to compute percentiles and detect anomalies.
Query the dataset using SELECT * and inspect the database performance metrics in Spanner Query Insights to observe data spread.