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Snowflake

Learn how to setup an import job using Snowflake
This feature is currently in beta release, please contact [email protected] for access

Step 1 - Start the Data Upload Wizard

Navigate to the 'Upload Data' page on the left navigation bar in the Arize platform. From there, select the 'Snowflake' card or navigate to the Data Warehouse tab to start a new table import job to begin a new table import job.
Storage Selection: Snowflake

Step 2 - Input the Dataset and Role information

Locate the Dataset, and Role name of the table you would like to sync from Snowflake. Example query can be found in Snowflake docs here.
  • The dataset and table name correspond to the path where your table is located
  • The role is assigned to Arize with permissions to read from the table
Fill in table information

Step 3 - Grant Access To Your Dataset, Table, or View

Tag your dataset/table/view with the arize-ingestion-key and the provided label value using the steps below. For more details, see docs on granting permissions to Arize's role for Snowflake.
In Arize UI: Copy arize-ingestion-key value
Copy your ingestion key and add it as a key value pair to your Snowflake dataset

In Snowflake: Follow along with Managing Reader Accounts. You may need to reach out to Snowflake to use this functionality.

If you have issues granting permissions please reach out to [email protected]

Step 4 - Configure Your Model And Define Your Table’s Schema

Match your model schema to your model type and define your model schema through the form input or a json schema.
Set up model configurations
Map your table using a form
Map your table using a JSON schema
Property
Description
Required
prediction_ID
The unique identifier of a specific prediction
Required
timestamp
The timestamp of the prediction in seconds or an RFC3339 timestamp
Optional, defaults to current timestamp at file ingestion time
change_timestamp*
Timestamp of when a row was added (see example for details)
Required *(only applicable for table upload)
prediction_label
Column name for the prediction value
Required
prediction_score
Column name for the predicted score
Required based on model type​
actual_label
Column name for the actual or ground truth value
Optional for production records
relevance_label
Column name for ranking actual or ground truth value
Required for ranking models
actual_score
Column name for the ground truth score
Required based on model type​
relevance_score
Column name for ranking ground truth score
Required for ranking models
features
A string prefix to describe a column feature/. Features must be sent in the same file as predictions
Arize automatically infers columns as features. Choose between feature prefixing OR inferred features.
tags
A string prefix to describe a column tag/. Tags must be sent in the same file as predictions and features
Optional
shap_values
A string prefix to describe a column shap/. SHAP must be sent in the same file as predictions or with a matching prediction_id
Optional
version
A column to specify model version. version/ assigns a version to the corresponding data within a column, or configure your version within the UI
Optional, defaults to 'no_version'
batch_id
Distinguish different batches of data under the same model_id and model_version. Must be specified as a constant during job setup or in the schema
Optional for validation records only
exclude
A list of columns to exclude if the features property is not included in the ingestion schema
Optional
embedding_features
A list of embedding columns, required vector column, optional raw data column, and optional link to data column. Learn more here​
Optional
Once finished, Arize will begin querying your table and ingesting your records as model inferences.

Step 5 - Add Model Data To The Table Or View

Arize will run queries to ingest records from your table based on your configured refresh interval.

Step 6 - Check your Table Import Job

Arize will attempt a dry run to validate your job for any access, schema or record-level errors. If the dry run is successful, you may then create the import job.
After creating a job following a successful dry run, you will be taken to the 'Job Status' tab where you can see the status of your import jobs.
You can view the job details and import progress by clicking on the job ID, which uncovers more information about the job.

Step 7 - Troubleshooting An Import Job

An import job may run into a few problems. Use the dry run and job details UI to troubleshoot and quickly resolve data ingestion issues.

Validation Errors

If there is an error validating a file or table against the model schema, Arize will surface an actionable error message. From there, click on the 'Fix Schema' button to adjust your model schema.

Dry Run File/Table Passes But The Job Fails

If your dry run is successful, but your job fails, click on the job ID to view the job details. This uncovers job details such as information about the file path or query id, the last import job, potential errors, and error locations.
Once you've identified the job failure point, append the edited row to the end of your table with an updated change_timestamp value.