Skill UI

This SDK provides comprehensive Python access to manage Microsoft Fabric capacities and resources within Azure. Developers can programmatically perform full lifecycle operations, including creating, reading, updating, deleting, suspending, and resuming Fabric capacities. It is essential for automating resource provisioning, monitoring capacity status, and optimizing cloud costs within the Azure ecosystem.

This SDK provides comprehensive Python access to manage Microsoft Fabric capacities and resources within Azure. Developers can programmatically perform full lifecycle operations, including creating, reading, updating, deleting, suspending, and resuming Fabric capacities. It is essential for automating resource provisioning, monitoring capacity status, and optimizing cloud costs within the Azure ecosystem.

Automates Azure ML Deployer workflows by offering best-practice guidance, code/config generation, and validation for serving, MLOps pipelines, monitoring, and production optimization.

Automates Azure ML Deployer workflows by offering best-practice guidance, code/config generation, and validation for serving, MLOps pipelines, monitoring, and production optimization.

A comprehensive Java SDK client for securely and efficiently sending custom application logs to Azure Monitor. It utilizes Data Collection Rules (DCR) and Data Collection Endpoints (DCE) to manage the ingestion process. The client supports batch uploads, concurrent processing, and robust error handling, making it ideal for integrating custom logging into Azure Log Analytics workspaces.

This SDK provides a dedicated Python interface for securely sending custom logs to Azure Monitor Log Analytics workspace. It utilizes the Logs Ingestion API, supporting features like batch compression, advanced error handling, asynchronous uploads, and compatibility with various authentication models, including sovereign clouds.

A Java exporter that facilitates sending OpenTelemetry telemetry data—including traces, metrics, and logs—from Java applications directly to Azure Monitor or Application Insights. It is crucial for achieving centralized, comprehensive observability into enterprise applications.

This library serves as a low-level exporter for Python applications, enabling the reliable transfer of comprehensive OpenTelemetry telemetry data—including traces, metrics, and logs—to Azure Application Insights. It provides robust configuration options, including connection string setup, Azure AD authentication, custom sampling, and offline storage management, ensuring complete and reliable observability.

This library provides a straightforward, one-line setup for integrating Python applications with Azure Application Insights. It leverages OpenTelemetry for automatic instrumentation across popular frameworks (Flask, Django, FastAPI) and various libraries, enabling comprehensive monitoring of traces, metrics, and logs in a single setup.

This SDK provides comprehensive support for automatically instrumenting Node.js applications to collect observability data. It integrates distributed tracing, metrics, and logs using the OpenTelemetry standard, allowing seamless data export and monitoring within Azure Monitor. It supports various resource types and connection methods.

This SDK provides a comprehensive Java client for interacting with Azure Monitor. It allows developers to execute powerful Kusto Query Language (KQL) queries against Log Analytics workspaces to retrieve detailed operational logs, and to query time-series metrics from various Azure resources. It supports both synchronous and asynchronous operations, making it ideal for building monitoring, auditing, and analytics features in Java applications.

A comprehensive Python SDK for interacting with Azure Monitor services. It enables developers to execute complex Kusto Query Language (KQL) queries against Log Analytics workspaces and retrieve various resource metrics (CPU usage, network traffic, etc.). Ideal for building robust monitoring dashboards, automated alerting systems, and custom data analysis pipelines in cloud environments.