Skill UI

This guide provides comprehensive strategies for scaling Customer.io integrations to handle high volumes of daily events. It covers implementing k6 load tests for performance benchmarking and outlines architectural patterns—from simple Direct APIs to complex Redis/Kafka queue-backed systems—ensuring reliability and managing API rate limits for massive data throughput.

A comprehensive reference architecture guide for implementing enterprise-grade integrations with Customer.io. It details best practices for building robust, scalable, and reliable systems, focusing on event-driven patterns, message queuing (e.g., Redis/Kafka), service layer separation, and idempotency. Ideal for designing complex data pipelines and ensuring high availability in mission-critical applications.

This guide provides expert patterns for building robust, observable infrastructure for database migrations. It covers real-time monitoring using Change Data Capture (CDC), metrics collection (Prometheus/Grafana), and anomaly detection. Use this skill when setting up CDC pipelines (e.g., Debezium with Kafka) or implementing comprehensive observability for database schema changes and data synchronization processes.

A comprehensive guide for building robust, event-driven APIs using Webhooks, Server-Sent Events (SSE), and message brokers (e.g., Kafka, RabbitMQ). This skill covers advanced patterns like the transactional outbox pattern, implementing event schemas, managing webhooks with retry logic, and establishing real-time streaming endpoints for asynchronous microservices communication. Ideal for architecting complex, scalable, and resilient systems.

Automated backend guidance for Kafka producer and consumer workflows, offering best practices, code samples, and configuration help for Node.js, Python, Go, databases, caching, messaging, and microservices scenarios.

This comprehensive guide details the expertise needed for a Senior Data Engineer. It covers building, optimizing, and governing scalable data pipelines and robust ETL/ELT systems using modern tools like Spark, Kafka, Airflow, and dbt. Includes decision frameworks for architecting data solutions (Batch vs Streaming, Lakehouse vs Warehouse) and implementing advanced data modeling patterns.

Provides a comprehensive, production-ready reference architecture for building advanced analytics platforms powered by ClickHouse. It details everything from project layout (TypeScript/Node.js structure) and data ingestion pipelines (Kafka, Webhooks) to the optimal 3-layer schema design (Raw, Hourly, Daily) and multi-tenancy patterns, ensuring scalable and efficient OLAP query performance.