Getting Started
MindsDB can be integrated with the most popular databases, as well as with the DBT and MLflow workflows.
To try out MindsDB right away without bringing in your own data or models, follow our Quickstart guide.
1. Create a MindsDB Cloud Account or Install MindsDB Locally
MindsDB Cloud
Docker
pip
Create your free MindsDB Cloud account.
2. Connect to MindsDB from a SQL Client
If you do not have a preferred SQL client yet, we recommend using the MindsDB SQL Editor or DBeaver Community Edition. Follow this guide to set up your MindsDB SQL Editor. And here, you’ll find how to connect to MindsDB from DBeaver.
MindsDB Cloud
MindsDB Cloud to Dbeaver
Local to Dbeaver
By default, on MindsDB Cloud the SQL Editor is already connected. Skip to step 3
3. Connect your Data to MindsDB Using CREATE DATABASE
CREATE DATABASE example_data
WITH ENGINE = "postgres",
PARAMETERS = {
"user": "demo_user",
"password": "demo_password",
"host": "3.220.66.106",
"port": "5432",
"database": "demo"
};
4. Preview the Available Data Using SELECT
SELECT *
FROM example_data.demo_data.home_rentals
LIMIT 10;
5. Create a Model Using CREATE MODEL
If you already have a model in MLFlow, you can connect to your model.
MindsDB creates my model
My model is in MLflow
CREATE MODEL mindsdb.home_rentals_predictor
FROM example_data
(SELECT * FROM demo_data.home_rentals)
PREDICT rental_price;
6. Make Predictions Using SELECT
SELECT rental_price
FROM mindsdb.home_rentals_predictor
WHERE number_of_bathrooms = 2
AND sqft = 1000;
On execution, we get:
+--------------+
| rental_price |
+--------------+
| 1130 |
+--------------+
7. Integrate your Predictions into the DBT Workflow
To do so, you need to make the following changes:
mindsdb:
type: mysql
host: mysql.mindsdb.com
user: mindsdb.user@example.com
password: mindsdbpassword
port: 3306
dbname: mindsdb
schema: example_data
threads: 1
keepalives_idle: 0 # default 0, indicating the system default
connect_timeout: 10 # default 10 seconds