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As maintained by Gartner , more than 80% of enterprises will have AI deployed by 2026. These encompass a holistic approach, covering datagovernance, model development, ethical deployment, and ongoing monitoring, reinforcing the organization’s commitment to responsible and ethical AI/ML practices.
Supporting the data management life cycle According to IDC’s Global StorageSphere, enterprise data stored in data centers will grow at a compound annual growth rate of 30% between 2021-2026. [2] The solution will also be available in AWS Marketplace.
Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective datagovernance enhances quality and security throughout the data lifecycle. What is Data Engineering?
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