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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.
He brings more than 30 years of experience across some of the largest enterprise customers, helping them understand and utilize AI ranging from initial concepts to specific application architectures, design, development and delivery. This article was made possible by our partnership with the IASA Chief Architect Forum.
Some folks swear a microservices architecture is always a good choice and that a monolith architecture is always a bad choice. Come to think of it – what architecture is a good choice for you and your product? Not all softwarearchitectures are equal. But is this true? And is this true for you and your product?
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. To get there, Amazon focused on decomposing for agility, making critical cultural and operational changes, and creating tools for software delivery.
Like many modern software architects, Andrew Harmel-Law struggles with the need to scale architectural thinking to larger organizations while allowing teams to be as autonomous as possible. In this first installment , Andrew describes this advice process, later installments will dig into four supporting elements that help make it work.
Generative artificial intelligence ( genAI ) and in particular large language models ( LLMs ) are changing the way companies develop and deliver software. The future will be characterized by more in-depth AI capabilities that are seamlessly woven into software products without being apparent to end users. An overview.
I have seen firsthand that this change makes software more accessible to everyone. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing code. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems.
It seems like only yesterday when software developers were on top of the world, and anyone with basic coding experience could get multiple job offers. In February, CEO Marc Benioff told CNBCs Squawk Box that 2025 will be the first year in the companys 25-year history that it will not add more software engineers.
The software development ecosystem exists in a state of dynamic equilibrium, where any new tool, framework, or technique leads to disruption and the establishment of a new equilibrium. It’s no surprise many CIOs and CTOs are struggling to adapt, in part because their architecture isn’t equipped to evolve.
These common software problems are exacerbated by the need to prioritize different features in different markets. Mobile applications often deliver features rapidly at first, but slow as a codebase builds up.
Their journey offers valuable lessons for IT leaders seeking scalable and efficient architecture solutions. This story may sound familiar to many IT leaders: the business grows, but legacy IT architecture cant keep up limiting innovation and speed. Domain-Driven Design gurus could see good old bounded contexts here.
Understanding and tracking the right software delivery metrics is essential to inform strategic decisions that drive continuous improvement. In todays digital-first economy, enterprise architecture must also evolve from a control function to an enablement platform. The stakes have never been higher.
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.
Choosing software to bake into your architecture is a long-term decision and it is important to understand all the implications of your choice. Learn three key areas that should be considered when evaluating a particular open source project.
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).
The rise of platform engineering Over the years, the process of software development has changed a lot. Initially, our industry relied on monolithic architectures, where the entire application was a single, simple, cohesive unit. On top of that, a single bug in the software could take down an entire system.
Vendors are adding gen AI across the board to enterprise software products, and AI developers havent been idle this year either. In 2025, those use cases will see massive adoption, especially if the AI technology is integrated into the software platforms that companies are already using, making it very simple to adopt.
The built-in elasticity in serverless computing architecture makes it particularly appealing for unpredictable workloads and amplifies developers productivity by letting developers focus on writing code and optimizing application design industry benchmarks , providing additional justification for this hypothesis. Architecture complexity.
Speaker: Ron Lichty, Consultant: Interim VP Engineering, Ron Lichty Consulting, Inc.
As a senior software leader, you likely spend more time working on the architecture of your systems than the architecture of your organization. In fact, the impact of softwarearchitecture parallels the impact of organizational structure. The wrong organizational structure can create friction of all kinds.
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.
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
75% of firms that build aspirational agentic AI architectures on their own will fail. The challenge is that these architectures are convoluted, requiring diverse and multiple models, sophisticated retrieval-augmented generation stacks, advanced data architectures, and niche expertise,” they said. “The
The result was a compromised availability architecture. Making emissions estimations visible to architects and engineers, such as the metrics based on the Green Software Foundation Software Carbon Intensity , along with green systems design training gives them the tools to make sustainability optimizations early in the design process.
In this white paper, discover the key use cases that make Cassandra® such a compelling open source software – and learn the important pitfalls to avoid. There’s a good reason why Apache Cassandra® is quickly becoming the NoSQL database of choice for organizations of all stripes.
Good coding practices for performance and efficiency have been part of software engineering since the earliest days. But over the past few decades, the overwhelming need for speed and productivity pushed architectural efficiency concerns to the background. Green Software Foundation was founded to help with these answers.
SAP R/1 was designed as standard software that could be offered to other companies. This principle was so successful that R/3 almost completely replaced the R/2 software package. The entire architecture of S/4HANA is tightly integrated and coordinated from a software perspective. In 2010, SAP introduced the HANA 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. Or, in some cases, companies have platforms that were built with human interactions in mind and aren’t ideal today for many gen AI implementations.
The future of leadership is architecturally driven As the demands of technology continue to reshape the business landscape, organizations must rethink their approach to leadership. The future of leadership is agile, adaptable and architecturally driven.
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.
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.
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.
Hire “architects” and require even small changes to have an “architecture review” Require even small changes to have a “security review” Product Dismiss useful metrics on academic grounds (e.g. “biased” or “lagging indicator”).
Microsoft , AWS, Google, IBM, Salesforce, ServiceNow, Workday, and SAP are already there, while data management software provider Informatica promised this week to add AI agents to its Intelligent Data Management Cloud to automate data processing. Over the last year, vendors have been rushing to add agentic AI products to their offerings.
Multi-tenant architecture allows software vendors to realize tremendous efficiencies by maintaining a single application stack instead of separate database instances while meeting data privacy needs. When you use a data warehouse to power your multi-tenant analytics, the proper approach is vital.
3] Looking ahead, GenAI promises a quantum leap in how we develop software, democratising development and bridging the skill gaps that hold back growth. The Software Development Life Cycle (SDLC) will be redefined and various job roles will merge into a unified, frictionless workbench of expert creation.
Hes seeing the need for professionals who can not only navigate the technology itself, but also manage increasing complexities around its surrounding architectures, data sets, infrastructure, applications, and overall security. Were continually finding ways to leverage it, Beerman says. Thomas, based in St.
As a long-time partner with NVIDIA, NetApp has delivered certified NVIDIA DGX SuperPOD and NetApp ® AIPod ™ architectures and has seen rapid adoption of AI workflows on first-party cloud offerings at the hyperscalers. Planned innovations: Disaggregated storage architecture. Performance enhancements.
FinOps, which was first created to maximise the use of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) models, is currently broadening its scope to include Software as a Service (SaaS).
A faster time to market and a better customer experience GenAI copilots are well-established in the world of software engineering and will continue to proliferate and evolve. In fact, many organizations save up to 30% of the time from strategy to deployment by taking a modern approach to application modernization.
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.
Last summer, a faulty CrowdStrike software update took down millions of computers, caused billions in damages, and underscored that companies are still not able to manage third-party risks, or respond quickly and efficiently to disruptions. It was an interesting case study of global cyber impact, says Charles Clancy, CTO at Mitre.
By modern, I refer to an engineering-driven methodology that fully capitalizes on automation and software engineering best practices. Not my original quote, but a cardinal sin of cloud-native data architecture is copying data from one location to another. Just because the work is data-centric or SQL-heavy does not warrant a free pass.
Pretty much all the practitioners I favor in SoftwareArchitecture are deeply suspicious of any kind of general law in the field. Good softwarearchitecture is very context-specific, analyzing trade-offs that resolve differently across a wide range of environments. I made my first architectural decision” he told me.
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