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A comprehensive Python SDK for accessing Azure AI Language service capabilities. It enables advanced Natural Language Processing (NLP) features, including sentiment analysis, named entity recognition (NER), key phrase extraction, language detection, and sensitive PII detection. Ideal for building sophisticated text analysis applications and data pipelines.

A comprehensive Python SDK for interacting with the Azure AI Translator service. It provides robust features including real-time text translation, language detection, script transliteration, and detailed dictionary lookups. Ideal for developers building multilingual applications that require accurate cross-cultural text processing.

A comprehensive Python SDK for interacting with the Azure AI Translator service. It provides robust features including real-time text translation, language detection, script transliteration, and detailed dictionary lookups. Ideal for developers building multilingual applications that require accurate cross-cultural text processing.

A high-throughput SDK for .NET applications to connect with Azure Event Hubs. This library enables reliable event publishing, supporting efficient batching and buffered sending for high-volume data streams. It also provides a robust, production-grade event consumption mechanism using EventProcessorClient, which manages checkpointing and ensures scalable, real-time data pipeline integration. Ideal for building microservices and streaming architectures.

A high-throughput SDK for .NET applications to connect with Azure Event Hubs. This library enables reliable event publishing, supporting efficient batching and buffered sending for high-volume data streams. It also provides a robust, production-grade event consumption mechanism using EventProcessorClient, which manages checkpointing and ensures scalable, real-time data pipeline integration. Ideal for building microservices and streaming architectures.

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 client library for interacting with Azure Event Hubs in Rust. It facilitates robust data streaming ingestion by providing functionality for both producers (sending event batches) and consumers (receiving and processing events from partitions). Designed for building high-throughput, real-time, event-driven data pipelines and architectures.

A comprehensive client library for interacting with Azure Event Hubs in Rust. It facilitates robust data streaming ingestion by providing functionality for both producers (sending event batches) and consumers (receiving and processing events from partitions). Designed for building high-throughput, real-time, event-driven data pipelines and architectures.

This SDK provides comprehensive Python support for Azure Data Lake Storage Gen2. It enables developers to interact with hierarchical file systems, performing essential big data operations such as creating, listing, uploading, downloading, and managing metadata for files and directories. Ideal for building data analytics pipelines and cloud-based data processing workflows.