In this webinar, we will cover the pros and cons of building a centralized data lake vs federated data mesh. Traditionally data warehouses are built on the premise of centralized data. This requires team, process and tool alignment which adds significant complexity and layers of process. Oftentimes the internal conflicts lead to subpar data management and quickly fragments to siloed data processing and insights. We will provide our unbiased view on building federated data mesh and the benefits of building an operational metrics layer.
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The global spread of the novel coronavirus (COVID-19) and the economic impact that followed has prompted many businesses to furlough the workforce or migrate from the traditional office to remote-working environments. The volatile landscape has created incremental risks, especially for organizations heavily relying on IT Sec/Ops teams to monitor security and privacy and enforce regulatory compliance.
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Arcadiadata
Brand new research published from Dresner Advisory Services digs deeply into the trends in 2018 around big data analytics. Where are organizations heading in 2019? How are analytic and data warehouse architectures evolving to enable faster and deeper self-service analytics and BI for organizations looking to create a competitive edge? How is public, private and hybrid clouds factoring into deployment decisions? What are the hottest open source projects from Apache Spark to Kudu, Kafka, Hadoop, and beyond?
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everestgrp
As technology adoption increases exponentially, organizations are challenged by the proliferation of data that the technology generates. Increasingly, Shared Services Global In-house Centers (GICs) are leading their organizations’ efforts to tame data and derive key insights from it. Based on our recent Pinnacle Model research on data & analytics maturity in SSCs/GICs, this webinar will show executives how they can build capabilities in their SSCs GICs to turn this challenge into a strategic asset, generating value and enhancing service delivery
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