As eBay is moving to OpenStack, we need to find capacity conversion ratio between ESX and KVM. Moreover, we hope to tunning KVM performance that make KVM to be same as or better than ESX
面對日新月異的大數據工具,有時候很難跟上這節奏。有鑑於此,Amazon Web Services提供了廣泛而完善的雲端運算服務組合,幫助您構建、維護和部署大數據應用程式。
這場線上研討會,將為各位深入淺出介紹AWS 雲端平台提供的各種大數據選項,包括現正流行的大數據框架,如Hadoop、Spark、NoSQL數據庫等,同時透過使用案例來瞭解最佳實踐方式。最後,您將了解如何應用這些工具服務,將大數據導入您的現實應用程式中。
20230523- D Forum 智慧工廠新竹- 智雲製造、數位轉型_如何以數據分析為基礎的架構佈局.pdfssuser293781
The slide discusses the framework for data-driven architecture in the context of digital transformation. It highlights the convergence of Operational Technology (OT) and Information Technology (IT) and emphasizes the need for a robust data infrastructure, advanced analytics, and a data-driven culture. Challenges such as data silos, redundancy, inconsistency, and performance issues in traditional setups are outlined.
The Amazon Data Flywheel is introduced as a five-step process: Release data, data hosting, modern cloud data warehousing, innovative data analytics applications, and increased data insights. Cloud data analytics architecture, featuring a Data Lake for centralized storage of structured and unstructured data, is explored. The advantages of a Data Lake include separating computation and storage, advanced analytics support for all data sources, reduced ETL complexity, and scalability for new technologies.
Amazon Redshift, a widely used cloud data warehouse, is mentioned for SQL-based analysis of large datasets. The comparison between traditional data centers and AWS Data Lake covers aspects such as storage and computational capabilities, analysis tools, data management, real-time processing, and data application capabilities.
Contributions to the community:
The presentation offers insights into overcoming challenges associated with traditional data architectures, providing a roadmap for organizations to embark on digital transformation. By introducing concepts like the Amazon Data Flywheel and cloud data analytics architecture, the text contributes valuable information on leveraging modern technologies for efficient data management and analytics. This knowledge-sharing benefits the community by facilitating a deeper understanding of the importance of a data-driven approach in the evolving landscape of technology and business.