Connect Jupyter to Streambased A.S.K.
Jupyter notebooks are used for exploratory data analysis, data cleaning, data visualization, statistical modeling, machine learning, and deep learning. Let's bring real-time data into the mix.
Prerequisites:
An active Streambased Cloud account, sign up or log in here
A running Jupyter deployment that has the following additional packages:
jupysql
sqlalchemy-trino
Step 1: Fetch your login credentials
Access to Streambased Cloud is managed via API-keys, you can create one for you new connection here
With Jupyter you will use your public key
as the database username and your secret key
as the database password. Make a note of these for Step 2.
Step 2: Create a database engine
In a new notebook execute the following:
from sqlalchemy.engine import create_engine
engine = create_engine("trino://[username]:[password]@ask-beta.streambased.cloud:8443/kafka",
connect_args ={"http_scheme":"https", "schema":"streambased"})
Step 3: Connect to the database
From your notebook run the following to load the SQL extension and the open the previously created engine:
%load_ext sql
%sql engine
Step 4: Run a query
Using the SQL extension we can execute anything we like. Happy querying!
%sql SELECT * FROM demo_transactions
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