Cloud Data Management

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.
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Spotlight

OTHER ON-DEMAND WEBINARS

Leverage your QlikView Investment to Modernize your BI Experience

Your business is evolving and it’s critical to understand data in a deeper way than ever before. Thousands of customers like you are finding their organizations demand more than guided analytics apps and dashboards, so now is the ideal time to modernize your BI platform with Qlik.
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Why the Solution to Data Democratization Is in the Stars

Data democratization is tough for even the most established enterprises. Now imagine how difficult it is for an organization working to eliminate the connectivity and data access barriers that hold economies and communities back.
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Why is a Central Analytics Team Vital to a Self-Service Data Culture?

When your teams are building products for competitive markets and demanding customers, the right insights can make all the difference. Join us for this month’s Coffee Chat webinar to learn how you help your whole organization make better decisions, take faster action—and deliver the kind of experiences that drive revenue, loyalty, and lifetime value.
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Data Observability / DataOps using AI

Modern-day systems are transforming into complex, open-source, cloud-native services running on various environments and being developed/deployed at lightning speed by distributed teams. When working on these systems, identifying a broken link in the chain can be near impossible. Everything fails at one point or another, whether due to code bugs, infrastructure overload, or changes in end-user behavior or market driven factors or errors in data collection. This has led to the rise of DataOps with a focus on changing the organizational speed and trust in delivering data pipelines and the related artifacts by co-creating “decision quality” data with the consumers. This development has led to the idea of observability that includes monitoring, tracking, and triaging incidents to prevent downtime of the systems and around several factors such as freshness, distribution, volume, schema, lineage.
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