data-exploration

Exploratory data analysis systematically profiles datasets to understand structure, identify quality issues, and discover patterns before in-depth analysis. You need it when encountering a new dataset to assess table dimensions, grain, and data recency.

To profile a dataset for quality, compute null rates, distinct counts, and statistical summaries at the column level. This evaluates completeness, consistency, accuracy, and timeliness to identify structural issues before analysis.

Yes, you can facilitate schema documentation and exploration via SQL queries. This allows you to systematically understand table structures, identify primary keys, and generate documentation for tables and their columns.

The best way to discover patterns is by applying pattern discovery techniques to identify trends, seasonality, segmentation, and correlations. This involves computing distributions and statistical summaries across all column types.

Yes, column-level profiling computes null rates, distinct counts, and distributions for all data types. This allows you to identify which columns are metrics versus dimensions and assess their quality accurately.

Avoid automated dataset analysis if your dataset lacks clear structural boundaries or primary keys, as systematic profiling requires defined table dimensions and a recognizable grain to accurately compute quality and patterns.