clickzetta-connector is a dialect adapter for ClickZetta Lakehouse provided for SQLAlchemy, allowing code or upper-layer applications written with the SQLAlchemy interface to easily interact with ClickZetta Lakehouse.
Installation
Remove old version dependencies
If you have previously installed an older version of the SDK, uninstall it first to avoid conflicts:
sql = text("SELECT * FROM ecommerce_events_multicategorystore_live;")
Execute the query using the engine:
with engine.connect() as conn:
result = conn.execute(sql)
for row in result:
print(row)
⚠️ Note: If the password contains special characters (such as @, :, /, #), you must use urllib.parse.quote_plus to encode the password, otherwise the URL will be parsed incorrectly.
Method 2: Using URL.create (Recommended)
Using URL.create avoids manual URL encoding issues. SQLAlchemy handles special characters in the password automatically.
from sqlalchemy import create_engine, text
from sqlalchemy.engine import URL
url = URL.create(
drivername='clickzetta',
username='your_username',
password='your_password',
host='instance.region_id.api.clickzetta.com',
database='your_workspace',
query={
'virtualcluster': 'default',
'schema': 'public'
}
)
engine = create_engine(url)
with engine.connect() as conn:
result = conn.execute(text("SELECT 1"))
for row in result:
print(row)
⚠️ Note: URL.create automatically handles special characters in the password (such as @, :, /), no need to manually call quote_plus.
Example: Using PyGWalker for Visual Analysis of Lakehouse Data
PyGWalker is a tool that can convert pandas and polars data frames into a Tableau-style user interface for data visualization exploration. It simplifies the Jupyter Notebook data analysis and data visualization workflow, requiring only one line of code to implement.
from sqlalchemy import create_engine
from sqlalchemy import text
import pandas as pd
import pygwalker as pyg
Create an instance of the SQLAlchemy engine for ClickZetta Lakehouse:
