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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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The key to operational AI: Modern data architecture

CIO

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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

CIO

From data masking technologies that ensure unparalleled privacy to cloud-native innovations driving scalability, these trends highlight how enterprises can balance innovation with accountability. With machine learning, these processes can be refined over time and anomalies can be predicted before they arise.

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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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The AI Future According to Google Cloud Next ’25: My Interesting Finds

Xebia

Native Multi-Agent Architecture: Build scalable applications by composing specialized agents in a hierarchy. BigFrames provides a Pythonic DataFrame and machine learning (ML) API powered by the BigQuery engine. offers a scikit-learn-like API for ML. BigFrames 2.0

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Building a Scalable ML Pipeline and API in AWS

Dzone - DevOps

With rapid progress in the fields of machine learning (ML) and artificial intelligence (AI), it is important to deploy the AI/ML model efficiently in production environments. The architecture downstream ensures scalability, cost efficiency, and real-time access to applications.

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Automate Amazon Bedrock batch inference: Building a scalable and efficient pipeline

AWS Machine Learning - AI

To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. Solution overview The solution presented in this post uses batch inference in Amazon Bedrock to process many requests efficiently using the following solution architecture.