Skill UI

A comprehensive Python SDK for interacting with Azure Container Registry (ACR). This tool allows developers to manage the full container lifecycle, including listing repositories, handling tags, manipulating manifests, downloading artifacts, and managing container images within Azure's cloud environment. Ideal for implementing robust CI/CD pipelines and container automation.

This comprehensive guide details how to build robust, production-grade NoSQL microservices using Python and Azure Cosmos DB. It emphasizes following architectural best practices, including the Service Layer pattern, TDD principles, secure authentication (DefaultAzureCredential), and implementing graceful degradation, making it ideal for developing enterprise-level backend systems.

A comprehensive client library for interacting with Azure Cosmos DB's NoSQL API in Rust. It enables performing core database operations, including CRUD (Create, Read, Update, Delete), patching, and managing globally distributed data. Ideal for building robust, data-intensive, and high-performance backend services on the Azure cloud platform.

The official JavaScript/TypeScript SDK for performing data plane operations on Azure Cosmos DB NoSQL API. This SDK provides comprehensive tools for managing data, including full CRUD (Create, Read, Update, Delete) operations on documents, executing complex parameterized and cross-partition queries, and handling bulk operations. It is essential for building data-intensive applications that require high-performance data interaction with Cosmos DB.

A comprehensive Python SDK for interacting with Azure Table Storage and Cosmos DB Table API. This library enables developers to perform full NoSQL key-value operations, including creating, reading, updating, and deleting (CRUD) entities, executing complex queries using filtering, and managing batch transactions. It supports both synchronous and asynchronous operations, making it ideal for building resilient, high-throughput cloud applications.

This library offers comprehensive Java clients for interacting with Azure Event Hubs. It enables developers to build robust, high-throughput, real-time streaming applications and event-driven architectures. Features include sending single events, batching events, sending to specific partitions, and using the production-grade EventProcessorClient with checkpointing for reliable data processing.

This library offers comprehensive Java clients for interacting with Azure Event Hubs. It enables developers to build robust, high-throughput, real-time streaming applications and event-driven architectures. Features include sending single events, batching events, sending to specific partitions, and using the production-grade EventProcessorClient with checkpointing for reliable data processing.

This SDK provides comprehensive Python support for interacting with Azure Event Hubs, a high-throughput streaming platform. It allows developers to build robust data ingestion pipelines, acting as both event producers (sending data) and event consumers (receiving data). Key features include efficient batch processing, partition key management, and reliable checkpointing using Azure Blob Storage for production-grade stream applications.

This SDK provides comprehensive Python support for interacting with Azure Event Hubs, a high-throughput streaming platform. It allows developers to build robust data ingestion pipelines, acting as both event producers (sending data) and event consumers (receiving data). Key features include efficient batch processing, partition key management, and reliable checkpointing using Azure Blob Storage for production-grade stream applications.

A comprehensive guide and implementation guide for using the Azure Event Hubs SDK in TypeScript. It demonstrates best practices for high-throughput event streaming, including batch event sending, targeted partitioning, and robust event consumption. Covers advanced features like checkpointing, starting from specific positions, and handling event properties, making it ideal for building scalable, real-time data ingestion pipelines.

A comprehensive guide and implementation guide for using the Azure Event Hubs SDK in TypeScript. It demonstrates best practices for high-throughput event streaming, including batch event sending, targeted partitioning, and robust event consumption. Covers advanced features like checkpointing, starting from specific positions, and handling event properties, making it ideal for building scalable, real-time data ingestion pipelines.

Master expert patterns for building robust serverless applications using Azure Functions. This guide covers multi-language support (.NET, Python, Node.js), including the isolated worker model, Durable Functions orchestration, cold start optimization, and implementing various triggers (HTTP, Timer, Blob). Essential for developing scalable, production-ready cloud backend services.