exploring-data

Exploring Data

Quick Start

To explore available data:

1. List all connections to see available data sources
2. Get connection details to see schemas, tables, and columns
3. List semantic models to discover pre-defined metrics and dimensions

When to Use This Skill

• User asks "what data do I have?"
• User wants to understand table structure
• Before writing queries to understand available columns
• When onboarding a new data source
• User asks about available connections or databases

Core Workflow

Step 1: List Available Connections

List all connections via the Altertable MCP server. Each connection has a name, engine type, and slug.

Built-in connections include altertable (platform data) and sample-data.

Step 2: Get Connection Schema

Retrieve the full schema for a connection of interest:

• Catalogs, schemas, and tables
• Column names, data types, and nullability
• Note the catalog and schema names for query qualification (e.g., catalog.schema.table)

Step 3: Explore Semantic Models

List semantic models to discover pre-defined business logic:

• Dimensions (categorical attributes for grouping)
• Measures (aggregations like count, sum, average)
• Relations (join paths between sources)

Connection Types

Data Warehouses

Engine	Description
Snowflake	Cloud data warehouse with catalogs and schemas
BigQuery	Google's serverless data warehouse
Redshift	AWS data warehouse

Databases

Engine	Description
PostgreSQL	Open-source relational database
MySQL / MariaDB	Popular relational databases
Clickhouse	Column-oriented OLAP database

Built-in Connections

Name	Purpose
altertable	Platform data (events, identities, pageviews)
sample-data	Demo data for testing and learning

Understanding Schemas

Table Qualification

Tables are referenced using three-part names:

catalog.schema.table

Example:

SELECT * FROM my_warehouse.public.users LIMIT 10

Column Data Types

Common types across engines:

• VARCHAR, TEXT, STRING - Text data
• INTEGER, BIGINT, INT64 - Whole numbers
• FLOAT, DOUBLE, NUMERIC - Decimal numbers
• BOOLEAN - True/false values
• TIMESTAMP, DATETIME - Date and time
• DATE - Date only
• JSON, VARIANT - Semi-structured data

Built-in Semantic Sources

The altertable connection includes pre-defined semantic sources:

Source	Description
events	Product analytics events with properties
identities	User identity information
pageviews	Web page view events
sessions	Web session aggregations
identity-overrides	Identity resolution rules

Common Patterns

Discovering Table Purpose

Look for clues in:

• Table names (e.g., users, orders, events)
• Column names (e.g., created_at, user_id, amount)
• Data types (timestamps indicate time-series data)

Identifying Primary Keys

Look for columns named:

• id, uuid, pk
• {table_name}_id (e.g., user_id in users table)

Finding Relationships

Look for foreign key patterns:

• {other_table}_id columns
• Matching column names across tables
• Semantic model relations

Common Pitfalls

• Assuming table names without checking the schema first
• Forgetting to qualify tables with catalog.schema
• Missing that some tables may be views or materialized views
• Not checking for semantic models that may already define the metrics needed

Reference Files

• Connection types detail
• Schema patterns