This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
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. Cloud storage.
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.
To succeed, Operational AI requires a modern data architecture. These advanced architectures offer the flexibility and visibility needed to simplify data access across the organization, break down silos, and make data more understandable and actionable.
In todays fast-paced digital landscape, the cloud has emerged as a cornerstone of modern business infrastructure, offering unparalleled scalability, agility, and cost-efficiency. As organizations increasingly migrate to the cloud, however, CIOs face the daunting challenge of navigating a complex and rapidly evolving cloud ecosystem.
Read more about how to simplify the deployment and scalability of your embedded analytics, along with important considerations for your: Environment Architecture: An embedded analytics architecture is very similar to a typical web architecture. Deployment: Benefits and drawbacks of hosting on premises or in the cloud.
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).
As organizations globally discover new opportunities created by AI, many are investing significantly in GenAI, including as part of their cloud modernization efforts. Many legacy applications were not designed for flexibility and scalability. The result is a more cybersecure enterprise.
Google Cloud Next 2025 was a showcase of groundbreaking AI advancements. Native Multi-Agent Architecture: Build scalable applications by composing specialized agents in a hierarchy. The post The AI Future According to Google Cloud Next ’25: My Interesting Finds appeared first on Xebia. BigFrames 2.0
Add to this the escalating costs of maintaining legacy systems, which often act as bottlenecks for scalability. The latter option had emerged as a compelling solution, offering the promise of enhanced agility, reduced operational costs, and seamless scalability. Scalability. Architecture complexity. Legacy infrastructure.
When it comes to the modern tech stack, one of the fastest changing areas is around containers, serverless, and choosing the ideal path to cloud native computing. We are excited to be joined by a leading expert who has helped many organizations get started on their cloud native journey.
At Cloud Next 2025, Google announced several updates that could help CIOs adopt and scale agents while reducing integration complexity and costs. Smaller LLMs and other updates At Cloud Next 2025, Google also introduced specialized LLMs for video, audio, and images in the form of Veo 2, Chirp 3, and Imagen 3.
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.
The core of their problem is applying AI technology to the data they already have, whether in the cloud, on their premises, or more likely both. And all of that data is stored on premises, but your training is taking place on the cloud where your GPUs live. Imagine that you’re a data engineer. How did we achieve this level of trust?
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.
This surge is driven by the rapid expansion of cloud computing and artificial intelligence, both of which are reshaping industries and enabling unprecedented scalability and innovation. On average, financial services clients weve worked with on cloud migration have had cloud bills 2-3 times the original expectations.
In todays dynamic digital landscape, multi-cloud strategies have become vital for organizations aiming to leverage the best of both cloud and on-premises environments. As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience. Why Hybrid and Multi-Cloud?
Initially, our industry relied on monolithic architectures, where the entire application was a single, simple, cohesive unit. Ever increasing complexity To overcome these limitations, we transitioned to Service-Oriented Architecture (SOA). We started building Cloud-native software. ’ by Sander and Chris!)
Unfortunately, despite hard-earned lessons around what works and what doesn’t, pressure-tested reference architectures for gen AI — what IT executives want most — remain few and far between, she said. It’s time for them to actually relook at their existing enterprise architecture for data and AI,” Guan said. “A
In modern cloud-native application development, scalability, efficiency, and flexibility are paramount. As organizations increasingly migrate their workloads to the cloud, architects are embracing innovative technologies and design patterns to meet the growing demands of their systems.
{{interview_audio_title}} 00:00 00:00 Volume Slider 10s 10s 10s 10s Seek Slider The genesis of cloud computing can be traced back to the 1960s concept of utility computing, but it came into its own with the launch of Amazon Web Services (AWS) in 2006. This alarming upward trend highlights the urgent need for robust cloud security measures.
The first is migrating data and workloads off of legacy platforms entirely and rehosting them in new environments, like the public cloud. This is a way to reap the benefits of cloud migration without having to overhaul all existing workloads. Kausik Chaudhuri is the CIO of Lemongrass.
With data existing in a variety of architectures and forms, it can be impossible to discern which resources are the best for fueling GenAI. With the right hybrid data architecture, you can bring AI models to your data instead of the other way around, ensuring safer, more governed deployments.
In today’s digital landscape, businesses increasingly use cloudarchitecture to drive innovation, scalability, and efficiency. The global market for cloud services is estimated to reach $723.4 The rise of these technologies motivates various companies to adopt the cloud approach. billion in 2024. Automation.
To overcome this, many CIOs originally adopted enterprise data platforms (EDPs)—centralized cloud solutions that delivered insights quickly, securely, and reliably across various business units and geographies. When evaluating options, prioritize platforms that facilitate data democratization through low-code or no-code architectures.
As organizations globally discover new opportunities created by AI, many are investing significantly in GenAI, including as part of their cloud modernization efforts. Many legacy applications were not designed for flexibility and scalability. The result is a more cybersecure enterprise.
These metrics might include operational cost savings, improved system reliability, or enhanced scalability. CIOs must take an active role in educating their C-suite counterparts about the strategic applications of technologies like, for example, artificial intelligence, augmented reality, blockchain, and cloud computing.
Data sovereignty and the development of local cloud infrastructure will remain top priorities in the region, driven by national strategies aimed at ensuring data security and compliance. The skills gap, particularly in AI, cloud computing, and cybersecurity, remains a critical issue.
Sashank Purighalla Contributor Share on Twitter Sashank Purighalla is the founder and CEO of BOS Framework , a cloud enablement platform. The promise of lower hardware costs has spurred startups to migrate services to the cloud, but many teams were unsure how to do this efficiently or cost-effectively.
CIOs who bring real credibility to the conversation understand that AI is an output of a well architected, well managed, scalable set of data platforms, an operating model, and a governance model. Cloud security and third-party risk is also a must, with bad actors becoming more sophisticated, Hackley says.
To maintain their competitive edge, organizations are constantly seeking ways to accelerate cloud adoption, streamline processes, and drive innovation. However, Cloud Center of Excellence (CCoE) teams often can be perceived as bottlenecks to organizational transformation due to limited resources and overwhelming demand for their support.
VMware Cloud Foundation on Google Cloud VMware Engine (GCVE) is now generally available, and there has never been a better time to move your VMware workloads to Google Cloud, so you can bring down your costs and benefit from a modern cloud experience. Lets take a look at these announcements in greater depth.
AI practitioners and industry leaders discussed these trends, shared best practices, and provided real-world use cases during EXLs recent virtual event, AI in Action: Driving the Shift to Scalable AI. And its modular architecture distributes tasks across multiple agents in parallel, increasing the speed and scalability of migrations.
Azures growing adoption among companies leveraging cloud platforms highlights the increasing need for effective cloud resource management. Enterprises must focus on resource provisioning, automation, and monitoring to optimize cloud environments. As Azure environments grow, managing and optimizing costs becomes paramount.
Building cloud infrastructure based on proven best practices promotes security, reliability and cost efficiency. To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloudarchitectures.
Innovation with respect to the customer experience remains crucial as global CX technology spending grows year-over-year , including increased spending on generative AI, the cloud, and digital services. Yet this acceleration can aggravate business management and create fundamental business risk, especially for established enterprises.
The adoption of cloud-native architectures and containerization is transforming the way we develop, deploy, and manage applications. Containers offer speed, agility, and scalability, fueling a significant shift in IT strategies.
Scalable Onboarding: Easing New Members into a Scala Codebase Piotr Zawia-Niedwiecki In this talk, Piotr Zawia-Niedwiecki, a senior AI engineer, shares insights from his experience onboarding over ten university graduates, focusing on the challenges and strategies to make the transition smoother. Real-world projects can feel intimidating.
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. Agents can be more loosely coupled than services, making these architectures more flexible, resilient and smart.
As enterprises increasingly embrace serverless computing to build event-driven, scalable applications, the need for robust architectural patterns and operational best practices has become paramount. Enterprises and SMEs, all share a common objective for their cloud infra – reduced operational workloads and achieve greater scalability.
After marked increase in cloud adoption through the pandemic, enterprises are facing new challenges, namely around the security, maintenance, and management of cloud infrastructure. According to the Foundry report, 78% of organizations say that, in response to cloud investments made by the organization, they have added new roles.
It’s no longer a question of whether organizations are moving to the cloud but rather how well it’s going. Cloud isn’t that shiny new object in the distance, full of possibility. Companies may have had highly detailed migration or execution plans, but many failed to develop a point of view on the role of cloud in the enterprise.
And third, systems consolidation and modernization focuses on building a cloud-based, scalable infrastructure for integration speed, security, flexibility, and growth. The second, business process transformation, is to streamline workflows through automation, which is especially important as we merge two distinct organizations.
As more enterprises migrate to cloud-based architectures, they are also taking on more applications (because they can) and, as a result of that, more complex workloads and storage needs. “This has driven our sales demand from the world’s largest cloud operators by an estimated 25% to 30%.”
Informatica Power Center professionals transitioning to Informatica Intelligent Cloud Services (IICS) Cloud Data Integration (CDI) will find both exciting opportunities and new challenges. While core data integration principles remain, IICS’s cloud-native architecture requires a shift in mindset.
We organize all of the trending information in your field so you don't have to. Join 49,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content