Exploring Warehouse Schemas

Tutorial

Exploring Warehouse Schemas

Discover tables, columns, and relationships in your data warehouse

20 min Intermediate

SchemaCatalogDiscoveryTables

What You'll Learn

• Catalog Browsing - List catalogs, schemas, and tables
• Column Discovery - Inspect table columns and data types
• Search - Find tables and columns by pattern
• Schema Navigation - Drill down through the catalog hierarchy

1

Setup

Connect to the platform

# Cell 1 — Parameters
USERNAME = "_FILL_ME_IN_"  # Set your email before running

# Cell 2 — Connect
from graph_olap import GraphOLAPClient
client = GraphOLAPClient(username=USERNAME)

# Cell 3 — Provision
from notebook_setup import provision
personas, conn = provision(USERNAME)
analyst = personas["analyst"]
admin = personas["admin"]
ops = personas["ops"]
client = analyst

print(f"Connected to: {client._config.api_url}")

2

Browsing the Catalog

Navigate catalogs, schemas, and tables

# List available catalogs
catalogs = client.schema.list_catalogs()
print("Catalogs:")
if not catalogs:
    print("  (no catalogs in cache — Starburst cache may be cold)")
else:
    for cat in catalogs:
        print(f"  {cat.catalog_name}")

    # List schemas within a catalog
    cat_name = catalogs[0].catalog_name
    schemas = client.schema.list_schemas(cat_name)
    print(f"\nSchemas in '{cat_name}':")
    for s in schemas:
        print(f"  {s.schema_name}")

    # List tables within a schema
    if schemas:
        schema_name = schemas[0].schema_name
        tables = client.schema.list_tables(cat_name, schema_name)
        print(f"\nTables in '{cat_name}.{schema_name}':")
        for t in tables:
            print(f"  {t.table_name}")

3

Inspecting Columns

View column names and data types

# List columns for the customer demographics table
if catalogs and schemas and tables:
    columns = client.schema.list_columns(cat_name, schema_name, tables[0].table_name)
    print(f"Columns in '{tables[0].table_name}':")
    for col in columns:
        print(f"  {col.column_name}: {col.data_type}")

    # List columns for the account data table
    if len(tables) > 1:
        acct_columns = client.schema.list_columns(cat_name, schema_name, tables[1].table_name)
        print(f"\nColumns in '{tables[1].table_name}':")
        for col in acct_columns:
            print(f"  {col.column_name}: {col.data_type}")
else:
    print("Skipped — no catalogs, schemas, or tables available.")

4

Searching Tables and Columns

Find tables and columns by pattern

# Search for tables matching a pattern
table_results = client.schema.search_tables("bis", limit=10)
print("Tables matching 'bis':")
for t in table_results:
    print(f"  {t}")

# Search for columns matching a pattern
column_results = client.schema.search_columns("psdo", limit=10)
print("\nColumns matching 'psdo':")
for c in column_results:
    print(f"  {c}")

Key Takeaways

• Use client.schema (singular) to access the schema browser
• Navigate the hierarchy: list_catalogs() → list_schemas() → list_tables() → list_columns()
• Use search_tables() and search_columns() to find objects by pattern
• Column metadata helps you understand your data before building mappings