📊 Choosing the Best Data Analytics and Visualization Tools on AWS
2👁️🗨️ Learn how to pick the right data analysis and visualization services on AWS\n🔎 Evaluate business needs, data characteristics and access requirements\n📈 Compare Athena for SQL queries, QuickSight for interactive dashboards and OpenSearch for real-time analytics\n🎯 Tailor tool selection to specific use cases like gaming analytics or fraud detection\n📊 Interactive examples demonstrate the power of these services to uncover insights\nThis informative video explores key factors in choosing AWS data tools for different analytics and visualization tasks. The presenter outlines a framework for decision making based on business needs, data properties and access controls. Several services are compared including Athena, QuickSight and OpenSearch. Real-world gaming and fraud use cases show how to match the ideal tool to specific requirements. Data engineers will appreciate the guidelines to build effective pipelines. Viewers can expect practical takeaways to start uncovering valuable insights from their data.\n\n✨I build courses: https://insight.paiml.com/bzf\nHands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀\n📚LLMOps Specialization: \n📚Operationalizing LLMs on Azure: https://insight.paiml.com/e2u\n\n📚Rust Programming Specialization: https://insight.paiml.com/qwh\n📚Rust for DevOps: https://insight.paiml.com/x14\n📚Rust LLMOps: https://insight.paiml.com/g3b\n📚Rust Fundamentals: https://insight.paiml.com/qyt\n📚Data Engineering with Rust: https://insight.paiml.com/zm1\n📚Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot\n\n\n📚Applied Python Data Engineering Specialization: https://insight.paiml.com/5r9\n📚Data Visualization with Python: https://insight.paiml.com/y9p\n📚Virtualization, Docker, and Kubernetes for Data Engineering: https://insight.paiml.com/xtp\n📚Spark, Hadoop, and Snowflake for Data Engineering: https://insight.paiml.com/f6j\n\n\n📚MLOps | Machine Learning Operations Specialization: https://insight.paiml.com/l5u\n📚Python Essentials for MLOps: https://insight.paiml.com/uvm\n📚DevOps, DataOps, MLOps: https://insight.paiml.com/ggi\n📚MLOps Tools: MLflow and Hugging Face: https://insight.paiml.com/y2v\n📚MLOps Platforms: Amazon SageMaker and Azure ML: https://insight.paiml.com/ymb\n\n\n📚Python, Bash and SQL Essentials for Data Engineering Specialization: https://insight.paiml.com/2or\n📚Linux and Bash for Data Engineering: https://insight.paiml.com/d31\n📚Scripting with Python and SQL for Data Engineering: https://insight.paiml.com/n3b\n📚Python and Pandas for Data Engineering: https://insight.paiml.com/nz7\n📚Web Applications and Command-Line Tools for Data Engineering: https://insight.paiml.com/o86\n\n\n📚Building Cloud Computing Solutions at Scale Specialization: https://insight.paiml.com/hrt\n📚Cloud Computing Foundations: https://insight.paiml.com/zrb\n📚Cloud Data Engineering: https://insight.paiml.com/75t\n📚Cloud Machine Learning Engineering and MLOps: https://insight.paiml.com/jjh\n📚Cloud Virtualization, Containers and APIs: https://insight.paiml.com/ce5