End to End Monitoring of High Performance Systems - A Data Engineering Project PART 1

Ever wondered how to monitor a system that processes 1 billion records per hour seamlessly? In this video, we break down the architecture and tools to make it happen: PART 2 AVAILABLE HERE: https://youtu.be/PL6Tl2sqh8k ✅ Apache Kafka: The backbone of real-time data streaming. ✅ Apache Spark: Lightning-fast processing for massive data pipelines. ✅ ELK Stack: Gain visibility with Elasticsearch, Logstash, and Kibana. ✅ Grafana & Prometheus: Real-time monitoring and performance insights. ✅ Kafka Schema Registry & Control Center: Streamlined management and schema validation. 🎯 What You'll Learn: ✅ How to design a robust architecture for high-throughput data pipelines. ✅ Insights into Python vs. Java Kafka Producers: Which one performs better? ✅ Real-time logging, monitoring, and debugging strategies. 🔥 Why This Matters: If you're in data engineering or want to level up your skills, this video showcases everything you need to build, monitor, and scale an ultra-high-performance streaming platform. Timestamps: 0:00 Introduction 3:33 Realtime monitoring with Prometheus 27:18 Outro 👀 Don't just watch it, build it! 🚧 👍 Like, Comment, & Subscribe for more cutting-edge data engineering content! Resources: Full Source Code: https://buymeacoffee.com/yusuf.ganiyu/full-source-code-monitoring-high-performance-architecture-systems Kafka Documentation: https://kafka.apache.org/documentation/ Apache Spark Documentation: https://spark.apache.org/documentation.html JMX Exporter Agent - https://github.com/prometheus/jmx_exporter/releases #ApacheKafka, #ApacheSpark, #DataEngineering, #BigData, #RealTimeProcessing, #ELKStack, #Grafana, #Prometheus, #KafkaStreams, #BigDataAnalytics, #DataPipeline, #StreamingData, #KafkaMonitoring, #SparkStreaming, #DataArchitecture, #HighPerformanceComputing