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Understanding the Differences Between Data Lake Engines, Data Virtualization, and Data Fabric
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Your business stakeholders need more data, faster data, more relevant, and more timely data. They are pushing for a data and analytics self-service model to speed up getting key insights. The CEO has made it clear that this is critical to the continued growth and health of the business...this is your top priority.
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So your company has decided to modernize its systems and migrate to the Cloud. Or, maybe, you're in the middle of this modernization and migration process right now. Taking advantage of the dynamic agility and flexibility of the Cloud offerings is certainly something to look forward to.
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ETL solutions have historically dominated the market of data integration tools, it’s a fact. In recent years, however, the exponential growth, complexity and diversification of data types, in addition to the need of an ever-faster access to fresh data, have proven the limitations of traditional solutions that depend on physical movement of data.
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Manufacturers today face challenges like rising customer expectations, geo-political tensions, material shortages, inflation, and high energy costs. Embracing data and AI is essential for overcoming these pressures and driving smart manufacturing.
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Are you aware of the next industrial revolution on the horizon? Are you prepared to capitalize on forthcoming opportunities arising from rapidly connecting Big Data, IoT and Cloud technologies for real-time insights and predictions? Adopt a Logical Data Lake for Digital Industrial Revolution.
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Data lakes have grown to be a popular architecture that enables modern analytics and data science. However, complete replication of all corporate data into giant data lakes is unfeasible. Data volumes are too high, and replication to multiple systems creates brittle point-to-point connections. Out-of-synch data and uncontrolled replication leads to “data swamp” scenarios.
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This test drive is built on the Google Cloud Platform ecosystem to solve typical challenges faced by data engineers and data analysts It pro
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See our logical data management page for an overview of the Denodo Platform’s approach, and learn how it delivers data to business and technical users at the speed of business, as well as how it delivers AI-ready data to AI applications, in real time.

Watch this 3-minute logical data management explainer video to see how, in an AI-driven world, logical data management delivers a consistent, trusted, and secure way to unify, access, and share data across your organization.

Download O’Reilly’s The Rise of Logical Data Management (complimentary) to learn how logical data management works, covering semantics, scalability and performance, data governance and security, and more.

Read The ROI of Using the Denodo Platform alongside the Modern Data Lakehouse, an independent study conducted by Veqtor8, which found that leveraging the Denodo Platform alongside a data lakehouse boosts ROI by 345% over operating a data lakehouse on its own.

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