metrics

Compute table metrics such as row counts and null counts.

209|30|Updated Sep 18, 2021

Search Tags:#schema #metrics #data-analysis #domain #data-quality #table

Ready to run

npx skills add https://github.com/starlake-ai/starlake --skill metrics-starlake-ai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: metrics
Source: https://github.com/starlake-ai/starlake/tree/main/.agent/skills/metrics
Command: npx skills add https://github.com/starlake-ai/starlake --skill metrics-starlake-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill computes metrics for a table to support data quality checks and analytics.

Core Features & Use Cases

Compute basic table metrics such as row counts, null counts, distinct value counts, and simple aggregates.
Use in data quality dashboards to monitor schema health and data completeness.
Apply in analytics pipelines to validate data against expected tolerances and to surface anomalies.

Quick Start

Use starlake metrics --domain <domain> --schema <schema> to compute metrics for your table.

Frequently Asked Questions about metrics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I compute table metrics for data quality checks?▼

Table metrics for data quality checks are computed by applying the analytics skill to a defined domain and schema, yielding row counts, null counts, and distinct value counts to monitor schema health.

What table metrics can I calculate to monitor data completeness?▼

To monitor data completeness, you can calculate basic table metrics including row counts, null counts, distinct value counts, and simple aggregates to validate data against expected tolerances and surface anomalies.

How do I get started computing schema metrics using the command line?▼

Computing schema metrics via command line requires running the tool with domain and schema inputs, such as specifying the target domain and schema parameters to generate the required table analytics output.

Do I need authentication credentials to compute metrics on secure data sources?▼

Authentication credentials are optional when computing metrics on secure data sources, allowing the analytics workflow to safely access and monitor data within a defined domain and schema if security configurations demand it.

Can I use table metrics for anomaly detection in analytics pipelines?▼

Table metrics can be applied in analytics pipelines for anomaly detection by validating data against expected tolerances, surfacing irregularities in simple aggregates, and assessing data quality within your defined schema.