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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.

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.

This guide provides comprehensive patterns and best practices for working with ClickHouse, a high-performance OLAP database. It covers advanced topics such as MergeTree engine design, efficient query optimization (filtering, aggregations, window functions), bulk/streaming data ingestion, materialized view creation, and performance monitoring techniques, ensuring optimal performance for large-scale analytical workloads.

SDK for building apps that interact with GitHub Copilot via JSON-RPC, managing sessions, streaming responses, custom tools, and hooks across Node.js, Python, Go, and .NET clients.