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What is data architecture? A framework to manage data

CIO

Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Ensure security and access controls.

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Revolutionizing data management: Trends driving security, scalability, and governance in 2025

CIO

The evolution of cloud-first strategies, real-time integration and AI-driven automation has set a new benchmark for data systems and heightened concerns over data privacy, regulatory compliance and ethical AI governance demand advanced solutions that are both robust and adaptive.

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Are enterprises ready to adopt AI at scale?

CIO

To overcome those challenges and successfully scale AI enterprise-wide, organizations must create a modern data architecture leveraging a mix of technologies, capabilities, and approaches including data lakehouses, data fabric, and data mesh. Another challenge here stems from the existing architecture within these organizations.

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The future of data: A 5-pillar approach to modern data management

CIO

The proposed model illustrates the data management practice through five functional pillars: Data platform; data engineering; analytics and reporting; data science and AI; and data governance. Not my original quote, but a cardinal sin of cloud-native data architecture is copying data from one location to another.

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Top Considerations for Building an Open Cloud Data Lake

Increasingly, enterprises are leveraging cloud data lakes as the platform used to store data for analytics, combined with various compute engines for processing that data. The primary architectural principles of a true cloud data lake, including a loosely coupled architecture and open file formats and table structures.

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How today’s enterprise architect juggles strategy, tech and innovation

CIO

Jenga builder: Enterprise architects piece together both reusable and replaceable components and solutions enabling responsive (adaptable, resilient) architectures that accelerate time-to-market without disrupting other components or the architecture overall (e.g. compromising quality, structure, integrity, goals).

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From legacy to lakehouse: Centralizing insurance data with Delta Lake

CIO

This is where Delta Lakehouse architecture truly shines. Approach Sid Dixit Implementing lakehouse architecture is a three-phase journey, with each stage demanding dedicated focus and independent treatment. Step 2: Transformation (using ELT and Medallion Architecture ) Bronze layer: Keep it raw. Ensure reliability.

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Partner Webinar: A Framework for Building Data Mesh Architecture

Speaker: Jeremiah Morrow, Nicolò Bidotti, and Achille Barbieri

Data teams in large enterprise organizations are facing greater demand for data to satisfy a wide range of analytic use cases. In this session, you will learn: How the silos development led to challenges with data growth, data quality, data sharing, and data governance (an example of datamesh paradigm adoption).