How to Install and Uninstall python39-azure-monitor-ingestion Package on openSuSE Tumbleweed
Last updated: November 15,2024
1. Install "python39-azure-monitor-ingestion" package
Learn how to install python39-azure-monitor-ingestion on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python39-azure-monitor-ingestion
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2. Uninstall "python39-azure-monitor-ingestion" package
Please follow the guidance below to uninstall python39-azure-monitor-ingestion on openSuSE Tumbleweed:
$
sudo zypper remove
python39-azure-monitor-ingestion
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3. Information about the python39-azure-monitor-ingestion package on openSuSE Tumbleweed
Information for package python39-azure-monitor-ingestion:
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Repository : openSUSE-Tumbleweed-Oss
Name : python39-azure-monitor-ingestion
Version : 1.0.3-1.4
Arch : noarch
Vendor : openSUSE
Installed Size : 246.8 KiB
Installed : No
Status : not installed
Source package : python-azure-monitor-ingestion-1.0.3-1.4.src
Upstream URL : https://github.com/Azure/azure-sdk-for-python
Summary : Microsoft Azure Monitor Ingestion Client Library for Python
Description :
The Azure Monitor Ingestion client library is used to send custom logs to Azure Monitor.
This library allows you to send data from virtually any source to supported built-in tables
or to custom tables that you create in Log Analytics workspace. You can even extend the schema
of built-in tables with custom columns.
---------------------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python39-azure-monitor-ingestion
Version : 1.0.3-1.4
Arch : noarch
Vendor : openSUSE
Installed Size : 246.8 KiB
Installed : No
Status : not installed
Source package : python-azure-monitor-ingestion-1.0.3-1.4.src
Upstream URL : https://github.com/Azure/azure-sdk-for-python
Summary : Microsoft Azure Monitor Ingestion Client Library for Python
Description :
The Azure Monitor Ingestion client library is used to send custom logs to Azure Monitor.
This library allows you to send data from virtually any source to supported built-in tables
or to custom tables that you create in Log Analytics workspace. You can even extend the schema
of built-in tables with custom columns.