METADATA ENRICHMENT IN PUBLISHING: Boosting Productivity and Increasing User Engagement

While it only became a buzzword in the digital age, the concept of metadata has been around for Millennia. In The Great Library of Alexandria scrolls had small tags dangling off the ends that contained information about the author, title and subject of the. Today we still talk about “tagging” content with metadata. Digitalization, followed by the advent of the World Wide Web, saw the use metadata skyrocket. Eventually though, metadata tagging was often overlooked as an archaic and overly manual process that didn’t go far enough to address the needs of properly identifying content.
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OTHER ON-DEMAND WEBINARS

Faster AI Deployment on Hadoop and Spark with RapidMiner and Microsoft Azure HDInsight

rapidminer.com

Rapid Miner and Azure HDInsight work together to deliver a complete data science and machine learning platform for massive amounts of data using popular open source frameworks such as Hadoop, Hive, MapReduce, and Spark.Hear from RapidMiner Product Manager, Jesus Puente and Cloud Software Engineer Beth Zeranski, Cloud at Microsoft for this 60-minute webinar where they will discuss.
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How to Modernize Data Lake Technologies with Cloud-Based Solutions

tdwi.org

Data lakes based on Hadoop technologies have proved themselves valuable in mission-critical use cases such as data warehousing, advanced analytics, multichannel marketing, complete customer views, digital supply chains, and the modernization of data management.Most Hadoop users are committed to the data lake method of managing data, but they are limited by Hadoop shortcomings in key areas such as cluster maintenance, administration cost, resource management, metadata management, and support for SQL and other relational technologies. Many view cloud-based solutions as the optimal replacement for their data lake, but they are not ready to make such a significant change. The truth is: they don't have to, as the two technologies can coexist.
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Automated Data and Analytics Workload Modernization

Moving on-premises legacy data and analytics workloads to the cloud is unavoidable if you want to overcome infrastructural constraints, facilitate proactive analytics, and lower costs. You need a service that enables seamless scalability for petabyte-scale data processing, interactive analytics, and machine learning. However, end-to-end, automated workload transformation and optimization on serverless services is not straightforward.
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Data Confidentiality & Utility: A Way Forward Together

Enterprises in data driven fields such as finance and healthcare must balance data utility with confidentiality. Often, enterprises and their partners want to analyze or monetize non-public or personally identifiable information, often at odds with growing privacy regulations and intellectual property concerns. Innovations in bu
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