Explore Data

If a file is provided (CSV, Excel, Parquet, JSON):

1. Read the file and load into a working dataset
2. Infer column types from the data

If neither:

1. Ask the user to provide a table name (with their warehouse connected) or upload a file
2. If they describe a table schema, provide guidance on what profiling queries to run

2. Understand Structure

Before analyzing any data, understand its structure:

Table-level questions:

• How many rows and columns?
• What is the grain (one row per what)?
• What is the primary key? Is it unique?
• When was the data last updated?
• How far back does the data go?

Column classification — categorize each column as one of:

• Identifier: Unique keys, foreign keys, entity IDs
• Dimension: Categorical attributes for grouping/filtering (status, type, region, category)
• Metric: Quantitative values for measurement (revenue, count, duration, score)
• Temporal: Dates and timestamps (created_at, updated_at, event_date)
• Text: Free-form text fields (description, notes, name)
• Boolean: True/false flags
• Structural: JSON, arrays, nested structures

3. Generate Data Profile

Run the following profiling checks:

Table-level metrics:

• Total row count
• Column count and types breakdown
• Approximate table size (if available from metadata)
• Date range coverage (min/max of date columns)

All columns:

• Null count and null rate
• Distinct count and cardinality ratio (distinct / total)
• Most common values (top 5-10 with frequencies)
• Least common values (bottom 5 to spot anomalies)

Numeric columns (metrics):

min, max, mean, median (p50)
standard deviation