profiling-tables

$2a

Run via run_sql:

SELECT

    COUNT(*) as total_rows,

    COUNT(*) / 1000000.0 as millions_of_rows

FROM <table>

Step 3: Column-Level Statistics

For each column, gather appropriate statistics based on data type:

Numeric Columns

SELECT

    MIN(column_name) as min_val,

    MAX(column_name) as max_val,

    AVG(column_name) as avg_val,

    STDDEV(column_name) as std_dev,

    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY column_name) as median,

    SUM(CASE WHEN column_name IS NULL THEN 1 ELSE 0 END) as null_count,

    COUNT(DISTINCT column_name) as distinct_count

FROM <table>

String Columns

SELECT

    MIN(LEN(column_name)) as min_length,

    MAX(LEN(column_name)) as max_length,

    AVG(LEN(column_name)) as avg_length,

    SUM(CASE WHEN column_name IS NULL OR column_name = '' THEN 1 ELSE 0 END) as empty_count,

    COUNT(DISTINCT column_name) as distinct_count

FROM <table>

Date/Timestamp Columns

SELECT

    MIN(column_name) as earliest,

    MAX(column_name) as latest,

    DATEDIFF('day', MIN(column_name), MAX(column_name)) as date_range_days,

    SUM(CASE WHEN column_name IS NULL THEN 1 ELSE 0 END) as null_count

FROM <table>

Step 4: Cardinality Analysis

For columns that look like categorical/dimension keys:

SELECT

    column_name,

    COUNT(*) as frequency,

    ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER(), 2) as percentage

FROM <table>

GROUP BY column_name

ORDER BY frequency DESC

LIMIT 20

This reveals:

• High-cardinality columns (likely IDs or unique values)

• Low-cardinality columns (likely categories or status fields)

• Skewed distributions (one value dominates)

Step 5: Sample Data

Get representative rows:

SELECT *

FROM <table>

LIMIT 10

If the table is large and you want variety, sample from different time periods or categories.

Step 6: Data Quality Assessment

Summarize quality across dimensions:

Completeness

• Which columns have NULLs? What percentage?

• Are NULLs expected or problematic?

Uniqueness

• Does the apparent primary key have duplicates?

• Are there unexpected duplicate rows?

Freshness

• When was data last updated? (MAX of timestamp columns)

• Is the update frequency as expected?

Validity

• Are there values outside expected ranges?

• Are there invalid formats (dates, emails, etc.)?

• Are there orphaned foreign keys?

Consistency

• Do related columns make sense together?

• Are there logical contradictions?

Step 7: Output Summary

Provide a structured profile:

Overview

2-3 sentences describing what this table contains, who uses it, and how fresh it is.

Schema

Column

Type

Nulls%

Distinct

Description

...

...

...

...

...

Key Statistics

• Row count: X

• Date range: Y to Z

• Last updated: timestamp

Data Quality Score

• Completeness: X/10

• Uniqueness: X/10

• Freshness: X/10

• Overall: X/10

Potential Issues

List any data quality concerns discovered.

Recommended Queries

3-5 useful queries for common questions about this data.