Skill UI

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.

Automates change-data-capture pipeline planning by guiding developers through ETL, orchestration, and streaming best practices, then generating validated configs and code when “cdc pipeline creator” is requested.

Automates beam pipeline builder tasks by guiding best practices, generating production-ready code, and validating ETL, transformation, orchestration, and streaming workflows.

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.

Guides users through compression optimizer scenarios within data pipelines, offering best practices, code templates, and validation steps for ETL, transformation, and streaming workflows.

The official SDK for building applications that programmatically interact with GitHub Copilot. It provides comprehensive features including session management, custom tool definition (function calling), real-time streaming, and lifecycle hooks. Supports multiple languages, including Node.js, Python, Go, and .NET, making AI integration seamless across your tech stack.

Guides you through building Dagster data pipelines with best-practice advice, ready-to-use configurations, and validation checks for ETL, transformation, orchestration, and streaming workflows whenever the Dagster pipeline creator skill is activated.