Create an Event Hub data connection for Azure Synapse Data Explorer by using Python (Preview)

Azure Synapse Data Explorer is a fast and highly scalable data exploration service for log and telemetry data. Azure Synapse Data Explorer offers ingestion (data loading) from Event Hubs, IoT Hubs, and blobs written to blob containers.

In this article, you create an Event Hub data connection for Azure Synapse Data Explorer by using Python.

Prerequisites

• An Azure subscription. Create a free Azure account.

• Create a Data Explorer pool using Synapse Studio or the Azure portal

• Create a Data Explorer database.

  1. In Synapse Studio, on the left-side pane, select Data.

  2. Select + (Add new resource) > Data Explorer pool, and use the following information:

     Setting	Suggested value	Description
     Pool name	contosodataexplorer	The name of the Data Explorer pool to use
     Name	TestDatabase	The database name must be unique within the cluster.
     Default retention period	365	The time span (in days) for which it's guaranteed that the data is kept available to query. The time span is measured from the time that data is ingested.
     Default cache period	31	The time span (in days) for which to keep frequently queried data available in SSD storage or RAM, rather than in longer-term storage.

  3. Select Create to create the database. Creation typically takes less than a minute.

• Event Hub with data for ingestion.
• Python 3.4+.

Create a table on your test cluster

Create a table named StormEvents that matches the schema of the data in the StormEvents.csv file.

var databaseName = "<DatabaseName>";
var table = "StormEvents";
using (var kustoClient = KustoClientFactory.CreateCslAdminProvider(kustoConnectionStringBuilder))
{
    var command =
        CslCommandGenerator.GenerateTableCreateCommand(
            table,
            new[]
            {
                Tuple.Create("StartTime", "System.DateTime"),
                Tuple.Create("EndTime", "System.DateTime"),
                Tuple.Create("EpisodeId", "System.Int32"),
                Tuple.Create("EventId", "System.Int32"),
                Tuple.Create("State", "System.String"),
                Tuple.Create("EventType", "System.String"),
                Tuple.Create("InjuriesDirect", "System.Int32"),
                Tuple.Create("InjuriesIndirect", "System.Int32"),
                Tuple.Create("DeathsDirect", "System.Int32"),
                Tuple.Create("DeathsIndirect", "System.Int32"),
                Tuple.Create("DamageProperty", "System.Int32"),
                Tuple.Create("DamageCrops", "System.Int32"),
                Tuple.Create("Source", "System.String"),
                Tuple.Create("BeginLocation", "System.String"),
                Tuple.Create("EndLocation", "System.String"),
                Tuple.Create("BeginLat", "System.Double"),
                Tuple.Create("BeginLon", "System.Double"),
                Tuple.Create("EndLat", "System.Double"),
                Tuple.Create("EndLon", "System.Double"),
                Tuple.Create("EpisodeNarrative", "System.String"),
                Tuple.Create("EventNarrative", "System.String"),
                Tuple.Create("StormSummary", "System.Object"),
            });

    kustoClient.ExecuteControlCommand(databaseName, command);
}

Define ingestion mapping

Map the incoming CSV data to the column names used when creating the table. Provision a CSV column mapping object on that table.

var tableMapping = "StormEvents_CSV_Mapping";
using (var kustoClient = KustoClientFactory.CreateCslAdminProvider(kustoConnectionStringBuilder))
{
    var command =
        CslCommandGenerator.GenerateTableMappingCreateCommand(
            Data.Ingestion.IngestionMappingKind.Csv,
            table,
            tableMapping,
            new[] {
                new ColumnMapping() { ColumnName = "StartTime", Properties = new Dictionary<string, string>() { { MappingConsts.Ordinal, "0" } } },
                new ColumnMapping() { ColumnName = "EndTime", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "1" } } },
                new ColumnMapping() { ColumnName = "EpisodeId", Properties = new Dictionary<string, string>() { { MappingConsts.Ordinal, "2" } } },
                new ColumnMapping() { ColumnName = "EventId", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "3" } } },
                new ColumnMapping() { ColumnName = "State", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "4" } } },
                new ColumnMapping() { ColumnName = "EventType", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "5" } } },
                new ColumnMapping() { ColumnName = "InjuriesDirect", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "6" } } },
                new ColumnMapping() { ColumnName = "InjuriesIndirect", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "7" } } },
                new ColumnMapping() { ColumnName = "DeathsDirect", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "8" } } },
                new ColumnMapping() { ColumnName = "DeathsIndirect", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "9" } } },
                new ColumnMapping() { ColumnName = "DamageProperty", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "10" } } },
                new ColumnMapping() { ColumnName = "DamageCrops", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "11" } } },
                new ColumnMapping() { ColumnName = "Source", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "12" } } },
                new ColumnMapping() { ColumnName = "BeginLocation", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "13" } } },
                new ColumnMapping() { ColumnName = "EndLocation", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "14" } } },
                new ColumnMapping() { ColumnName = "BeginLat", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "15" } } },
                new ColumnMapping() { ColumnName = "BeginLon", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "16" } } },
                new ColumnMapping() { ColumnName = "EndLat", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "17" } } },
                new ColumnMapping() { ColumnName = "EndLon", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "18" } } },
                new ColumnMapping() { ColumnName = "EpisodeNarrative", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "19" } } },
                new ColumnMapping() { ColumnName = "EventNarrative", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "20" } } },
                new ColumnMapping() { ColumnName = "StormSummary", Properties =  new Dictionary<string, string>() { { MappingConsts.Ordinal, "21" } } }
        });

    kustoClient.ExecuteControlCommand(databaseName, command);
}

Install Python package

To install the Python package for Azure Synapse Data Explorer, open a command prompt that has Python in its path. Run the following command:

pip install azure-common
pip install azure-mgmt-kusto

Authentication

To run the following example, you need a Microsoft Entra application and service principal that can access resources. To create a free Microsoft Entra application and add role assignment at the subscription level, see Create a Microsoft Entra application. You also need the directory (tenant) ID, application ID, and client secret.

Add an Event Hub data connection

The following example shows you how to add an Event Hub data connection programmatically. See connect to the event hub for adding an Event Hub data connection using the Azure portal.

from azure.mgmt.kusto import KustoManagementClient
from azure.mgmt.kusto.models import EventHubDataConnection
from azure.common.credentials import ServicePrincipalCredentials

#Directory (tenant) ID
tenant_id = "xxxxxxxx-xxxxx-xxxx-xxxx-xxxxxxxxx"
#Application ID
client_id = "xxxxxxxx-xxxxx-xxxx-xxxx-xxxxxxxxx"
#Client Secret
client_secret = "xxxxxxxxxxxxxx"
subscription_id = "xxxxxxxx-xxxxx-xxxx-xxxx-xxxxxxxxx"
credentials = ServicePrincipalCredentials(
        client_id=client_id,
        secret=client_secret,
        tenant=tenant_id
    )
kusto_management_client = KustoManagementClient(credentials, subscription_id)

resource_group_name = "testrg"
#The cluster and database that are created as part of the Prerequisites
cluster_name = "mykustocluster"
database_name = "mykustodatabase"
data_connection_name = "myeventhubconnect"
#The event hub that is created as part of the Prerequisites
event_hub_resource_id = "/subscriptions/xxxxxxxx-xxxxx-xxxx-xxxx-xxxxxxxxx/resourceGroups/xxxxxx/providers/Microsoft.EventHub/namespaces/xxxxxx/eventhubs/xxxxxx";
consumer_group = "$Default"
location = "Central US"
#The table and column mapping that are created as part of the Prerequisites
table_name = "StormEvents"
mapping_rule_name = "StormEvents_CSV_Mapping"
data_format = "csv"
#Returns an instance of LROPoller, check https://learn.microsoft.com/python/api/msrest/msrest.polling.lropoller?view=azure-python
poller = kusto_management_client.data_connections.create_or_update(resource_group_name=resource_group_name, cluster_name=cluster_name, database_name=database_name, data_connection_name=data_connection_name,
                                        parameters=EventHubDataConnection(event_hub_resource_id=event_hub_resource_id, consumer_group=consumer_group, location=location,
                                                                            table_name=table_name, mapping_rule_name=mapping_rule_name, data_format=data_format))

Clean up resources

To delete the data connection, use the following command:

kusto_management_client.data_connections.delete(resource_group_name=resource_group_name, cluster_name=kusto_cluster_name, database_name=kusto_database_name, data_connection_name=kusto_data_connection_name)

Next steps

• Analyze with Data Explorer
• Monitor Data Explorer pools