Integrate Amazon SageMaker with Tiger Cloud

Connect to your Tiger Cloud service

For Tiger Cloud, open an SQL editor in Tiger Cloud Console. For self-hosted TimescaleDB, use psql.

For better performance and easier real-time analytics, create a hypertable

Hypertables are Postgres tables that automatically partition your data by time. You interact with hypertables in the same way as regular Postgres tables, but with extra features that makes managing your time-series data much easier.

CREATE TABLE model_predictions (
  time TIMESTAMPTZ NOT NULL,
  model_name TEXT NOT NULL,
  prediction DOUBLE PRECISION NOT NULL
) WITH (
  tsdb.hypertable
);

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When you create a hypertable using CREATE TABLE ... WITH ..., the default partitioning column is automatically the first column with a timestamp data type. Also, TimescaleDB creates a columnstore policy that automatically converts your data to the columnstore, after an interval equal to the value of the chunk_interval, defined through compress_after in the policy. This columnar format enables fast scanning and aggregation, optimizing performance for analytical workloads while also saving significant storage space. In the columnstore conversion, hypertable chunks are compressed by up to 98%, and organized for efficient, large-scale queries.

You can customize this policy later using alter_job. However, to change after or created_before, the compression settings, or the hypertable the policy is acting on, you must remove the columnstore policy and add a new one.

You can also manually convert chunks in a hypertable to the columnstore.

Create a SageMaker Notebook instance

1. In Amazon SageMaker > Notebooks and Git repos, click Create Notebook instance.
2. Follow the wizard to create a default Notebook instance.

Write a Notebook script that inserts data into your Tiger Cloud service

1. When your Notebook instance is inService, click Open JupyterLab and click conda_python3.

2. Update the following script with your connection details, then paste it in the Notebook.

   import psycopg2
   from datetime import datetime
   
   def insert_prediction(model_name, prediction, host, port, user, password, dbname):
         conn = psycopg2.connect(
            host=host,
            port=port,
            user=user,
            password=password,
            dbname=dbname
         )
         cursor = conn.cursor()
   
         query = """
            INSERT INTO model_predictions (time, model_name, prediction)
            VALUES (%s, %s, %s);
         """
   
         values = (datetime.utcnow(), model_name, prediction)
         cursor.execute(query, values)
         conn.commit()
   
         cursor.close()
         conn.close()
   
   # Example usage
   insert_prediction(
         model_name="example_model",
         prediction=0.95,
         host="<host>",
         port="<port>",
         user="<user>",
         password="<password>",
         dbname="<dbname>"
   )

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Test your SageMaker script

1. Run the script in your SageMaker notebook.

2. Verify that the data is in your service

   Open an SQL editor and check the sensor_data table:

   SELECT * FROM model_predictions;

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   You see something like:

   time	model_name	prediction
   2025-02-06 16:56:34.370316+00	timescale-cloud-model	0.95