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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.
Developing a robust technical architecture for digital twins necessitates a comprehensive understanding of several foundational components and integration of advanced technologies. This architecture allows for better decision-making, predictive maintenance and enhanced operational efficiency. Digital model.
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. Would you know that the user agent performs sentiment/text analysis?
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).
We will hear about specific use cases where organizations leveraged serverless refactoring, containerization or a combination of both solutions, that resulted in improved performance, availability, and scalability. How to make the right architectural choices given particular application patterns and risks.
In the era of generative AI , new large language models (LLMs) are continually emerging, each with unique capabilities, architectures, and optimizations. Among these, Amazon Nova foundation models (FMs) deliver frontier intelligence and industry-leading cost-performance, available exclusively on Amazon Bedrock.
About six weeks ago, I sent an email to Satya Nadella complaining about the monolithic winner-takes-all architecture that Silicon Valley seems to envision for AI, contrasting it with the architecture of participation that had driven previous technology revolutions, most notably the internet and open source software.
In todays digital-first economy, enterprise architecture must also evolve from a control function to an enablement platform. This transformation requires a fundamental shift in how we approach technology delivery moving from project-based thinking to product-oriented architecture. The stakes have never been higher.
Surely, dedicated teams backed by real budgets were mobilized to deliver a seamless journey, define the target architecture and drive change management at scale. Yet often, theres no centralized team guiding the integration of strategy, architecture and execution. Business architecture. Technical architecture and engineering.
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.
However, as companies expand their operations and adopt multi-cloud architectures, they are faced with an invisible but powerful challenge: Data gravity. While centralizing data can improve performance and security, it can also lead to inefficiencies, increased costs and limitations on cloud mobility. Security is another key concern.
Enterprises need infrastructure that can scale and provide the high performance required for intensive AI tasks, such as training and fine-tuning large language models. As customers entrust us with their data, we see even more opportunities ahead to help them operationalize AI and high-performance workloads. Performance enhancements.
Technology leaders in the financial services sector constantly struggle with the daily challenges of balancing cost, performance, and security the constant demand for high availability means that even a minor system outage could lead to significant financial and reputational losses. Architecture complexity. Legacy infrastructure.
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.
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).
For example, AI can perform real-time data quality checks flagging inconsistencies or missing values, while intelligent query optimization can boost database performance. Its ability to apply masking dynamically at the source or during data retrieval ensures both high performance and minimal disruptions to operations.
The company says it can achieve PhD-level performance in challenging benchmark tests in physics, chemistry, and biology. Agents will begin replacing services Software has evolved from big, monolithic systems running on mainframes, to desktop apps, to distributed, service-based architectures, web applications, and mobile apps.
The reasons include higher than expected costs, but also performance and latency issues; security, data privacy, and compliance concerns; and regional digital sovereignty regulations that affect where data can be located, transported, and processed. That said, 2025 is not just about repatriation. Judes Research Hospital St.
By emphasizing immediate cost-cutting, FinOps often encourages behaviors that compromise long-term goals such as performance, availability, scalability and sustainability. The result was a compromised availability architecture. Complex sustainable architecture patterns.
Apache Cassandra is an open-source distributed database that boasts an architecture that delivers high scalability, near 100% availability, and powerful read-and-write performance required for many data-heavy use cases. The topics covered include: Using Cassandra as if it were a Relational Database.
Suboptimal integration strategies are partly to blame, and on top of this, companies often don’t have security architecture that can handle both people and AI agents working on IT systems. Our research reveals that top performers allocate around 15% of their IT budget to debt remediation.
Cloud architects are responsible for managing the cloud computing architecture in an organization, especially as cloud technologies grow increasingly complex. At organizations that have already completed their cloud adoption, cloud architects help maintain, oversee, troubleshoot, and optimize cloud architecture over time.
It prevents vendor lock-in, gives a lever for strong negotiation, enables business flexibility in strategy execution owing to complicated architecture or regional limitations in terms of security and legal compliance if and when they rise and promotes portability from an application architecture perspective.
Structured frameworks such as the Stakeholder Value Model provide a method for evaluating how IT projects impact different stakeholders, while tools like the Business Model Canvas help map out how technology investments enhance value propositions, streamline operations, and improve financial performance.
As enterprises evolve their AI from pilot programs to an integral part of their tech strategy, the scope of AI expands from core data science teams to business, software development, enterprise architecture, and IT ops teams. The Forrester Wave™ evaluates Leaders, Strong Performers, Contenders, and Challengers.
The new protocols will enable IT teams to seamlessly connect diverse AI agents and to reduce the cost and complexity of AI integrations, adds Gary Lerhaupt, vice president of product architecture at Salesforce. It also helps them to avoid vendor lock-in, he adds.
Much of it centers on performing actions, like modifying cloud service configurations, deploying applications or merging log files, to name just a handful of examples. It provides an efficient, standardized way of building AI-powered agents that can perform actions in response to natural-language requests from users.
Native Multi-Agent Architecture: Build scalable applications by composing specialized agents in a hierarchy. Built-in Evaluation: Systematically assess agent performance. MCP support: Agents built with ADK act as MCP (Model Context Protocol) clients and thus offer native integration with any MCP servers.
Their DeepSeek-R1 models represent a family of large language models (LLMs) designed to handle a wide range of tasks, from code generation to general reasoning, while maintaining competitive performance and efficiency. 70B-Instruct ), offer different trade-offs between performance and resource requirements.
Whether you need to rework your security architecture, improve performance, and/or deal with new threats, this webinar has you covered. What methods and architectures you should consider to proactively protect your data. We are excited to be joined by a CTO who is an expert in pragmatic choices around security.
An agent uses a function call to invoke an external tool (like an API or database) to perform specific actions or retrieve information it doesnt possess internally. We will deep dive into the MCP architecture later in this post.
And as part of the expanded partnership, the two companies are collaborating on a joint data flywheel architecture that will integrate ServiceNow Workflow Data Fabric with Nvidia NeMo microservices. ServiceNow said it expects the new model to be available in Q2 this year.
In this model, organizations are investing in creating architectures for intelligent choices and using technology to augment people, not automate tasks, transforming the entire value chain, he says. CIOs should consider how agentic AI and other emerging AI capabilities enable the creation of intelligent organizations.
Added up, perhaps these are among the reasons that 51% of companies have not seen an increase in performance or profitability from digital investments, according to KPMG research. They were new products, interfaces, and architectures to do the same thing we always did. Amazon reimagined commerce to become digital-first.
An organization’s data is copied for many reasons, namely ingesting datasets into data warehouses, creating performance-optimized copies, and building BI extracts for analysis. How Dremio delivers clear business advantages in productivity, security, and performance. What to consider when implementing a "no-copy" data strategy.
To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. The solution incorporates the following key features: Using a Retrieval Augmented Generation (RAG) architecture, the system generates a context-aware detailed assessment.
Built on top of EXLerate.AI, EXLs AI orchestration platform, and Amazon Web Services (AWS), Code Harbor eliminates redundant code and optimizes performance, reducing manual assessment, conversion and testing effort by 60% to 80%. The EXLerate.AI
About six weeks ago, I sent an email to Satya Nadella complaining about the monolithic winner-takes-all architecture that Silicon Valley seems to envision for AI, contrasting it with the architecture of participation that had driven previous technology revolutions, most notably the internet and open source software.
Understanding Traditional Python Package Managers Python package managers like pip perform three core functions: 1. – They perform redundant I/O operations during installation. – They perform redundant I/O operations during installation. They resolve dependencies (figure out which package versions work together) 2.
Speaker: Leo Zhadanovsky, Principal Solutions Architect, Amazon Web Services
Amazon's journey to its current modern architecture and processes provides insights for all software development leaders. The result was enabling developers to rapidly release and iterate software while maintaining industry-leading standards on security, reliability, and performance.
Zscalers zero trust architecture delivers Zero Trust Everywheresecuring user, workload, and IoT/OT communicationsinfused with comprehensive AI capabilities. Real-time AI insights: Employ predictive and generative AI for actionable insights that enhance security operations and digital performance.
which performed two ERP deployments in seven years. We really liked [NetSuite’s] architecture and that it’s in the cloud, and it hit the vast majority of our business requirements,” Shannon notes. When it embarked on an ERP modernization project, the second time proved to be the charm for Allegis Corp.,
What is needed is a single view of all of my AI agents I am building that will give me an alert when performance is poor or there is a security concern. If agents are using AI and are adaptable, youre going to need some way to see if their performance is still at the confidence level you want it to be, says Gartners Coshow.
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