Connect to External SQL Databases

Every language in Cloudera Machine Learning has multiple client libraries available for SQL databases.

If your database is behind a firewall or on a secure server, you can connect to it by creating an SSH tunnel to the server, then connecting to the database on localhost.

If the database is password-protected, consider storing the password in an environmental variable to avoid displaying it in your code or in consoles. The examples below show how to retrieve the password from an environment variable and use it to connect.


You can access data using pyodbc or SQLAlchemy
# pyodbc lets you make direct SQL queries.
!cd pyodbc-3.0.7;python install --prefix /home/cdsw
import os

# See for information on how to construct ODBC connection strings.
db = pyodbc.connect("DRIVER={PostgreSQL Unicode};SERVER=localhost;PORT=5432;DATABASE=test_db;USER=cdswuser;OPTION=3;PASSWORD=%s" % os.environ["POSTGRESQL_PASSWORD"])
cursor = cnxn.cursor()
cursor.execute("select user_id, user_name from users")

# sqlalchemy is an object relational database client that lets you make database queries in a more Pythonic way.
!pip install sqlalchemy
import os
import sqlalchemy
from sqlalchemy.orm import sessionmaker
from sqlalchemy import create_engine
db = create_engine("postgresql://cdswuser:%s@localhost:5432/test_db" % os.environ["POSTGRESQL_PASSWORD"])
session = sessionmaker(bind=db)
user = session.query(User).filter_by(name='ed').first()


You can access remote databases with dplyr.

db <- src_postgres(dbname="test_db", host="localhost", port=5432, user="cdswuser", password=Sys.getenv("POSTGRESQL_PASSWORD")) 
flights_table <- tbl(db, "flights") 
select(flights_table, year:day, dep_delay, arr_delay)