Snowflake miner package

The Snowflake miner package mines query history from Snowflake. This data is used for generating lineage and usage metrics.

Source extraction

2.1.8 1.9.0

To mine query history directly from Snowflake using its built-in database:

Java Python Kotlin Raw REST API

AtlanClient client = Atlan.getDefaultClient();

Workflow miner = SnowflakeMiner.creator( // (1)
        "default/snowflake/1234567890" // (2)
    )
    .direct( // (3)
        "TEST_DB",
        "TEST_SCHEMA",
        1713225600
    )
    .excludeUsers( // (4)
        List.of(
          "test-user-1",
          "test-user-2"
        )
    )
    .nativeLineage(true) // (5)
    .build()  // (6)
    .toWorkflow();  // (7)

WorkflowResponse response = miner.run();  // (8)

1. Base configuration for a new Snowflake miner.
2. You must provide the exact qualifiedName of the Snowflake connection in Atlan for which you want to mine query history.

3. To create a workflow for mining history directly from Snowflake using its built-in database you need to provide:

   • name of the database to extract from.
   • name of the schema to extract from.
   • date and time from which to start mining, as an epoch.

4. Optionally, you can specify list of users who should be excluded when calculating usage metrics for assets (for example, system accounts).

5. Optionally, you can specify whether to enable native lineage from Snowflake, using Snowflake's ACCESS_HISTORY.OBJECTS_MODIFIED Column. Note: this is only available only for Snowflake Enterprise customers.
6. Build the minimal package object.
7. Now, you can convert the package into a Workflow object.

8. Run the workflow by invoking the run() method on the workflow client, passing the created object.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.packages import SnowflakeMiner

miner = (
    SnowflakeMiner( # (1)
        connection_qualified_name="default/snowflake/1234567890"
        # (2)
    )
    .direct( # (3)
        start_epoch=1713225600,
        database="TEST_DB",
        schema="TEST_SCHEMA",
    )
    .exclude_users( # (4)
        users=[
          "test-user-1",
          "test-user-2",
        ]
    )
    .popularity_window(days=30) # (5)
    .native_lineage(enabled=True) # (6)
    .custom_config( # (7)
        config={
          "test": True,
          "feature": 1234
      }
    )
    .to_workflow() # (8)
)

response = client.workflow.run(miner) # (9)

1. Base configuration for a new Snowflake miner.
2. You must provide the exact qualified_name of the Snowflake connection in Atlan for which you want to mine query history.

3. To create a workflow for mining history directly from Snowflake using its built-in database you need to provide:

   • date and time from which to start mining, as an epoch.
   • name of the database to extract from.
   • name of the schema to extract from.

4. Optionally, you can specify list of users who should be excluded when calculating usage metrics for assets (for example, system accounts).

5. Optionally, you can provide number of days to consider for calculating popularity.
6. Optionally, you can specify whether to enable native lineage from Snowflake, using Snowflake's ACCESS_HISTORY.OBJECTS_MODIFIED Column. Note: this is only available only for Snowflake Enterprise customers.
7. Optionally, you can provide custom configuration controlling experimental feature flags for the miner.
8. Now, you can convert the package into a Workflow object.

9. Run the workflow by invoking the run() method on the workflow client, passing the created object.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

var client = Atlan.getDefaultClient();

var miner = SnowflakeMiner.creator( // (1)
        "default/snowflake/1234567890" // (2)
    )
    .direct( // (3)
        "TEST_DB",
        "TEST_SCHEMA",
        1713225600
    )
    .excludeUsers( // (4)
        listOf(
          "test-user-1",
          "test-user-2"
        )
    )
    .nativeLineage(true) // (5)
    .build()  // (6)
    .toWorkflow();  // (7)

var response = miner.run();  // (8)

1. Base configuration for a new Snowflake miner.
2. You must provide the exact qualifiedName of the Snowflake connection in Atlan for which you want to mine query history.

3. To create a workflow for mining history directly from Snowflake using its built-in database you need to provide:

   • name of the database to extract from.
   • name of the schema to extract from.
   • date and time from which to start mining, as an epoch.

4. Optionally, you can specify list of users who should be excluded when calculating usage metrics for assets (for example, system accounts).

5. Optionally, you can specify whether to enable native lineage from Snowflake, using Snowflake's ACCESS_HISTORY.OBJECTS_MODIFIED Column. Note: this is only available only for Snowflake Enterprise customers.
6. Build the minimal package object.
7. Now, you can convert the package into a Workflow object.

8. Run the workflow by invoking the run() method on the workflow client, passing the created object.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

Create the workflow via UI only

We recommend creating the workflow only via the UI. To rerun an existing workflow, see the steps below.

Offline extraction

2.1.8 1.9.0

To mine query history from the S3 bucket:

Java Python Kotlin Raw REST API

AtlanClient client = Atlan.getDefaultClient();

Workflow miner = SnowflakeMiner.creator( // (1)
        "default/snowflake/1234567890" // (2)
    )
    .s3( // (3)
        "test-s3-bucket",
        "test-s3-prefix",
        "TEST_QUERY",
        "TEST_DB",
        "TEST_SCHEMA",
        "TEST_SESSION_ID"
    )
    .nativeLineage(true) // (4)
    .build()  // (5)
    .toWorkflow();  // (6)

WorkflowResponse response = miner.run();  // (7)

1. Base configuration for a new Snowflake miner.
2. You must provide the exact qualifiedName of the Snowflake connection in Atlan for which you want to mine query history.

3. To create a workflow for mining history from S3 bucket you need to provide:

   • S3 bucket where the JSON line-separated files are located.
   • prefix within the S3 bucket in which the JSON line-separated files are located.
   • JSON key containing the query definition.
   • JSON key containing the default database name to use if a query is not qualified with database name.
   • JSON key containing the default schema name to use if a query is not qualified with schema name.
   • JSON key containing the session ID of the SQL query.

4. Optionally, you can specify whether to enable native lineage from Snowflake, using Snowflake's ACCESS_HISTORY.OBJECTS_MODIFIED Column. Note: this is only available only for Snowflake Enterprise customers.

5. Build the minimal package object.
6. Now, you can convert the package into a Workflow object.

7. Run the workflow by invoking the run() method on the workflow client, passing the created object.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.packages import SnowflakeMiner

miner = (
    SnowflakeMiner( # (1)
        connection_qualified_name="default/snowflake/1234567890"
        # (2)
    )
    .s3( # (3)
        s3_bucket="test-s3-bucket",
        s3_prefix="test-s3-prefix",
        s3_bucket_region="test-s3-bucket-region",
        sql_query_key="TEST_QUERY",
        default_database_key="TEST_DB",
        default_schema_key="TEST_SCHEMA",
        session_id_key="TEST_SESSION_ID",
    )
    .popularity_window(days=30) # (4)
    .native_lineage(enabled=True) # (5)
    .custom_config( # (6)
        config={
          "test": True,
          "feature": 1234
      }
    )
    .to_workflow() # (7)
)

response = client.workflow.run(miner) # (8)

1. Base configuration for a new Snowflake miner.
2. You must provide the exact qualified_name of the Snowflake connection in Atlan for which you want to mine query history.

3. To create a workflow for mining history from S3 bucket you need to provide:

   • S3 bucket where the JSON line-separated files are located.
   • prefix within the S3 bucket in which the JSON line-separated files are located.
   • (Optional) region of the S3 bucket if applicable.
   • JSON key containing the query definition.
   • JSON key containing the default database name to use if a query is not qualified with database name.
   • JSON key containing the default schema name to use if a query is not qualified with schema name.
   • JSON key containing the session ID of the SQL query.

4. Optionally, you can provide number of days to consider for calculating popularity.

5. Optionally, you can specify whether to enable native lineage from Snowflake, using Snowflake's ACCESS_HISTORY.OBJECTS_MODIFIED Column. Note: this is only available only for Snowflake Enterprise customers.
6. Optionally, you can provide custom configuration controlling experimental feature flags for the miner.
7. Now, you can convert the package into a Workflow object.

8. Run the workflow by invoking the run() method on the workflow client, passing the created object.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

var client = Atlan.getDefaultClient();

var miner = SnowflakeMiner.creator( // (1)
        "default/snowflake/1234567890" // (2)
    )
    .s3( // (3)
        "test-s3-bucket",
        "test-s3-prefix",
        "TEST_QUERY",
        "TEST_DB",
        "TEST_SCHEMA",
        "TEST_SESSION_ID"
    )
    .nativeLineage(true) // (4)
    .build()  // (5)
    .toWorkflow();  // (6)

var response = miner.run();  // (7)

1. Base configuration for a new Snowflake miner.
2. You must provide the exact qualifiedName of the Snowflake connection in Atlan for which you want to mine query history.

3. To create a workflow for mining history from S3 bucket you need to provide:

   • S3 bucket where the JSON line-separated files are located.
   • prefix within the S3 bucket in which the JSON line-separated files are located.
   • JSON key containing the query definition.
   • JSON key containing the default database name to use if a query is not qualified with database name.
   • JSON key containing the default schema name to use if a query is not qualified with schema name.
   • JSON key containing the session ID of the SQL query.

4. Optionally, you can specify whether to enable native lineage from Snowflake, using Snowflake's ACCESS_HISTORY.OBJECTS_MODIFIED Column. Note: this is only available only for Snowflake Enterprise customers.

5. Build the minimal package object.
6. Now, you can convert the package into a Workflow object.

7. Run the workflow by invoking the run() method on the workflow client, passing the created object.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

Create the workflow via UI only

We recommend creating the workflow only via the UI. To rerun an existing workflow, see the steps below.

Re-run existing workflow

1.9.5 1.10.6

To re-run an existing workflow for Snowflake query mining:

Java Python Kotlin Raw REST API

List<WorkflowSearchResult> existing = WorkflowSearchRequest // (1)
            .findByType(SnowflakeMiner.PREFIX, 5); // (2)
// Determine which of the results is the
// Snowflake workflow you want to re-run...
WorkflowRunResponse response = existing.get(n).rerun(); // (3)

1. You can search for existing workflows through the WorkflowSearchRequest class.
2. You can find workflows by their type using the findByType() helper method and providing the prefix for one of the packages. In this example, we do so for the SnowflakeMiner. (You can also specify the maximum number of resulting workflows you want to retrieve as results.)

3. Once you've found the workflow you want to re-run, you can simply call the rerun() helper method on the workflow search result. The WorkflowRunResponse is just a subtype of WorkflowResponse so has the same helper method to monitor progress of the workflow run.

   • Optionally, you can use the rerun(true) method with idempotency to avoid re-running a workflow that is already in running or in a pending state. This will return details of the already running workflow if found, and by default, it is set to false

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.enums import WorkflowPackage

client = AtlanClient()

existing = client.workflow.find_by_type(  # (1)
  prefix=WorkflowPackage.SNOWFLAKE_MINER, max_results=5
)

# Determine which Snowflake workflow (n)
# from the list of results you want to re-run.
response = client.workflow.rerun(existing[n]) # (2)

1. You can find workflows by their type using the workflow client find_by_type()method and providing the prefix for one of the packages. In this example, we do so for the SnowflakeMiner. (You can also specify the maximum number of resulting workflows you want to retrieve as results.)

2. Once you've found the workflow you want to re-run, you can simply call the workflow client rerun() method.

   • Optionally, you can use rerun(idempotent=True) to avoid re-running a workflow that is already in running or in a pending state. This will return details of the already running workflow if found, and by default, it is set to False.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

var existing = WorkflowSearchRequest // (1)
            .findByType(SnowflakeMiner.PREFIX, 5); // (2)
// Determine which of the results is the
// Snowflake workflow you want to re-run...
var response = existing.get(n).rerun(); // (3)

1. You can search for existing workflows through the WorkflowSearchRequest class.
2. You can find workflows by their type using the findByType() helper method and providing the prefix for one of the packages. In this example, we do so for the SnowflakeMiner. (You can also specify the maximum number of resulting workflows you want to retrieve as results.)

3. Once you've found the workflow you want to re-run, you can simply call the rerun() helper method on the workflow search result. The WorkflowRunResponse is just a subtype of WorkflowResponse so has the same helper method to monitor progress of the workflow run.

   • Optionally, you can use the rerun(true) method with idempotency to avoid re-running a workflow that is already in running or in a pending state. This will return details of the already running workflow if found, and by default, it is set to false

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

Requires multiple steps through the raw REST API

1. Find the existing workflow.
2. Send through the resulting re-run request.

{
  "from": 0,
  "size": 5,
  "query": {
    "bool": {
      "filter": [
        {
          "nested": {
            "path": "metadata",
            "query": {
              "prefix": {
                "metadata.name.keyword": {
                  "value": "atlan-snowflake-miner" // (1)
                }
              }
            }
          }
        }
      ]
    }
  },
  "sort": [
    {
      "metadata.creationTimestamp": {
        "nested": {
          "path": "metadata"
        },
        "order": "desc"
      }
    }
  ],
  "track_total_hits": true
}

1. Searching by the atlan-snowflake-miner prefix will ensure you only find existing Snowflake miner workflows.

   Name of the workflow

   The name of the workflow will be nested within the _source.metadata.name property of the response object. (Remember since this is a search, there could be multiple results, so you may want to use the other details in each result to determine which workflow you really want.)

{
  "namespace": "default",
  "resourceKind": "WorkflowTemplate",
  "resourceName": "atlan-snowflake-miner-1684500411" // (1)
}

1. Send the name of the workflow as the resourceName to rerun it.

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