data-exploration

Dataset profiling involves systematically assessing table structures, computing null counts, and evaluating completeness to understand data shape and quality before analysis. This process identifies missing values, distinct distributions, and potential schema issues.

Data profiling is the systematic computation of column-level statistics, null counts, and distinct values to assess dataset shape and quality. You need to run it when onboarding new CSV files to discover patterns, identify outliers, and document schemas before analysis.

To identify data patterns and outliers in a new sales dataset, compute statistical summaries and visualize the distribution of key fields like order value. Pattern discovery techniques reveal trends, seasonality, segmentation, and correlations within the data.

Yes, you can assess data completeness, consistency, accuracy, and timeliness by running systematic quality assessment checks. These checks evaluate null counts and distinct values across columns to automatically flag structural anomalies and quality issues.

The best way to document a dataset schema is to analyze table and column structures, identify data types, and determine data grain. This structural understanding provides the foundational documentation needed to guide accurate statistical analysis.

Data exploration helps identify data grain and structural relationships by analyzing table and column structures alongside data types. This structural understanding reveals the granularity of your records and how columns relate to each other.