Apache Hadoop: The New Enterprise Data Platform for Big Data

Cloud data management CDM is simply data management that involves clouds. For example, when focused on data persistence, CDM provides cloud-native data storage and optimized processing for the burgeoning volumes of enterprise data, big data, and data from new sources that users are choosing to manage and use on clouds. When focused on integration, CDM provides data integration infrastructure (with related functions for quality and semantics) to unify multicloud and hybrid on-premises/cloud environments.

The need to monetize data has become a strategic imperative for businesses undergoing digital transformation, whether it’s using data to improve customer engagement, identify compelling opportunities or deliver actionable insight. However, as businesses look to expand revenue-generating, customer-facing applications beyond operational processing to include analytics, they find themselves outgrowing the their transactional database. They need full operational and full analytic capabilities.

Rapidly evolving business needs drive unprecedented demand for the benefits of master data management (MDM) across a wider array of data, processes and operating entities. They also drive more questions from data and analytics leaders about what they must do to meet those needs and take full advantage of master data management. Join this complimentary data and analytics webinar as Gartner expert Thornton Craig fields your and your peers’ most-pressing questions about MDM, including about the essentials of MDM, implementation red flags and the importance of data governance.

Search capability is ingrained into our daily life. How many arguments these days are settled with the conclusion, “Just Google it”??! We all expect some type of search functionality in every application and website. Meanwhile, advances in computer vision, natural language processing, large language models, and generative AI have made it possible to extract semantic properties from unstructured data in the form of vector embeddings. It can be daunting to query this kind of data, which combines K-Nearest Neighbors algorithms and lexical search, unless you have the right tool for the job. When you fail, performance suffers. Web users typically expect search results under one second.