The recent technology revolution has dramatically changed the way organizations design, deploy, a nd manage data-delivery and analytics systems. Hadoop, NoSQL, and the cloud brought a new era of scale-out, elastic, and real-time computing. New data preparation, catalog, and analysis tools aided by advanced machine learnin g and search technologies advanced the practices of self-service analytics and radically changed the information supply chain. The speed of busin ess has accelerated in the age of digital transformation, increasing the de mand for fast data, fast insights, and real-time analytics.
\nThe tra ditional world of data warehousing and business intelligence has been flipp ed upside down. Serial data pipelines of relational data managed, mode led, and batch loaded exclusively by IT are a thing of the past. The m odern analytics ecosystem supports multiple, continuous real-time data flow s designed by data engineers and data analysts close to the business. Multi ple data sources and a continuous stream of unique use cases result in many data pipelines. The ability to find the right data, manage data flow and w orkflow, and deliver the right data in the right forms and at the right spe ed is essential to success with modern analytics.
\nYou Will Learn:\n\n How modern analytics reshapes the consumerism, business, and data managemen t\n The shortcomings of legacy architectures in the world of modern analytics\n Stages and processes of the modern information supply chain\n Architectural principles that enable fast data and fast insights\n How the “data quake” has shaken the foundations of data managem ent\n A framework for modern data management topology\n Architectural principles for data pipelines and data services\n How to adapt from traditional BI architecture to a modern analytics ecosyst em\n\n DTSTART:20181009T083000 SUMMARY:Modernizing Data Architecture for the Age of Real-Time Analytics DTEND:20181009T114459 LOCATION: See Description END:VEVENT END:VCALENDAR