Skill UI

A comprehensive, production-grade reference architecture for integrating with Apollo.io. It details a layered design encompassing API services, business logic, background job processing (e.g., lead enrichment), and data modeling, enabling the building of robust sales intelligence and CRM synchronization systems.

This module provides production-ready TypeScript patterns for interacting with the Apollo.io REST API. It implements critical best practices including type-safe client setup (using Zod), custom error handling, exponential backoff retry logic, asynchronous pagination, and rate-aware bulk processing. Use this when building reliable, scalable, and robust integrations with Apollo data.

This skill provides comprehensive guidance on implementing high-performance, non-blocking applications in Python. It utilizes asyncio, async/await, and advanced concurrency patterns to handle I/O-bound workloads efficiently. Ideal for building async web APIs, real-time systems, and concurrent data processing.

Provides canonical AWS serverless patterns covering Lambda handlers, DynamoDB single-table modeling, CDK infrastructure, and S3 event processing along with anti-patterns and a checklist for production-ready deployments.

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 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 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.