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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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Agentic AI design: An architectural case study

CIO

You can use these agents through a process called chaining, where you break down complex tasks into manageable tasks that agents can perform as part of an automated workflow. These agents are already tuned to solve or perform specific tasks. Microsoft is describing AI agents as the new applications for an AI-powered world.

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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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Trade routes of the digital age: How data gravity shapes cloud strategy

CIO

Just as ancient trade routes determined how and where commerce flowed, applications and computing resources today gravitate towards massive datasets. However, as companies expand their operations and adopt multi-cloud architectures, they are faced with an invisible but powerful challenge: Data gravity.

Strategy 164
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Modern Data Architecture for Embedded Analytics

Every data-driven project calls for a review of your data architecture—and that includes embedded analytics. Before you add new dashboards and reports to your application, you need to evaluate your data architecture with analytics in mind. 9 questions to ask yourself when planning your ideal architecture.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements. In this post, we provide an overview of common multi-LLM applications.

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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

By implementing this architectural pattern, organizations that use Google Workspace can empower their workforce to access groundbreaking AI solutions powered by Amazon Web Services (AWS) and make informed decisions without leaving their collaboration tool. The following figure illustrates the high-level design of the solution.

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Why Distributed Tracing is Essential for Performance and Reliability

Speaker: Daniel "spoons" Spoonhower, CTO and Co-Founder at Lightstep

Many engineering organizations have now adopted microservices or other loosely coupled architectures, often alongside DevOps practices. However, this increased velocity often comes at the cost of overall application performance or reliability. Hold teams accountable using service level objectives (SLOs).

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Serverless and Containers: How to Choose the Right Application Strategy

Speaker: Tolga Tarhan, Senior Vice President, GM AWS Services at Onica

Of course, the key as a senior leader is to understand what your organization needs, your application requirements, and to make choices that leverage the benefits of the right approach that fits the situation. How to make the right architectural choices given particular application patterns and risks.