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Thales and Skydweller Team Up on Solar-Powered, AI-Equipped UAV for Persistent Maritime Surveillance

Thales and U.S.-based Skydweller Aero have unveiled an ambitious joint venture to develop a solar-powered unmanned aerial vehicle capable of sustained maritime surveillance missions extending from weeks to months. The partnership, announced at the Paris Air Show in mid-June, integrates Thales’s advanced AirMaster S radar—enhanced with artificial-intelligence functions—onto Skydweller’s medium-altitude pseudo-satellite platform, marking a milestone in ultra-endurance, zero-carbon aerial surveillance.

Built with a wingspan comparable to that of a Boeing 747, Skydweller’s UAV combines over 17,000 solar panels and lightweight carbon-fiber construction to sustain flight durations previously unattainable by conventional UAVs. Its payload capacity reaches approximately 400 kg—substantially more than earlier solar-based platforms—and is powered by solar energy alone, enabling long-term, emission-free operation .

Central to the system is the AirMaster S radar, an X-band active electronically scanned array (AESA) previously deployed on manned maritime patrol aircraft. The radar leverages AI to autonomously classify thousands of simultaneous air and sea tracks within a roughly 200 km range. It auto-adjusts settings mid-flight and filters data to send only critical intelligence to operators, thus minimizing bandwidth and enhancing situational awareness.

The strategic edge of this UAV lies in its cost and endurance advantages. Sebastien Renouard, Skydweller’s head of international development, estimates operational costs to be five to ten times lower than those of conventional drones such as the MQ-9 Reaper. The firm has already defined a landmark milestone of achieving 90 days of continuous flight, with future objectives extending to potentially year-long missions supported by predictive maintenance technologies.

Philippe Duhamel, Executive Vice President for Defense Mission Systems at Thales, highlighted how Skydweller’s pseudo-satellite UAV complements traditional assets by providing continuous maritime oversight—an operational mode beyond the reach of current crewed systems.

The joint solution is positioned to meet a wide spectrum of use cases. While military adoption is prioritized, both companies note growing interest from border control, emergency response, and civil surveillance entities in North America and Europe. Designed for operation in equatorial zones and summer polar latitude, it also supports missions across diverse geographic environments.

This UAV reflects a transformative leap in aerial surveillance. By merging solar-powered endurance with AI-enhanced sensing, the Thales–Skydweller collaboration introduces a new paradigm: a cost-effective, emissions-free, and semi-autonomous system tailored for long-duration oversight of maritime and border zones.

As interest grows and initial military contracts emerge, this solar-powered UAV could redefine maritime security frameworks—serving as a complement to satellites, ships, and manned aircraft. With endurance measured in months rather than hours, the initiative could reshape how nations monitor increasingly critical environments like Exclusive Economic Zones and contested waters.

Ultimately, this initiative signals how solar propulsion, advanced radar, and AI are converging to chart the future of unmanned surveillance, offering persistent, intelligent, and sustainable aerial capability for a broad spectrum of defense and civilian missions.

IBM Consulting’s Pushpinder Singh Highlights Agentic AI as Game-Changer for Supply Chains

IBM Consulting’s Global Supply Chain Transformation Leader Pushpinder Singh has provided fresh insight into the transformative power of agentic AI, a next-generation form of artificial intelligence that autonomously understands, plans, and executes complex tasks—suggesting it could soon redefine supply chain operations worldwide.

Singh explains that while traditional AI systems act as analysts by evaluating data, and generative AI operates like assistants by creating content based on prompts, “agentic AI acts like an autonomous planner—able to perceive, reason, act, and learn” in real time. He describes it as “high-intensive, human brainpower–type activity” capable of taking over and improving what were previously manual tasks.

In practical terms, Singh sees agentic agents initially deployed in areas such as procurement, third-party risk assessment, and dynamic planning. For example, agents might generate and analyse supplier contracts, identify and monitor vendor risk through continuous data evaluation, or respond dynamically to fluctuating demand signals, rerouting shipments or reallocating inventory without human intervention.

Singh’s five-part framework defines a fully agentic system—comprising an LLM core for reasoning, planning capability, integration with enterprise tools, memory retention, and feedback loops to learn from results. This structured design ensures agents not only make decisions but execute actions within an organisation’s systems.

A common concern with AI, the “hallucination” of erroneous outputs, appears less prevalent in such task-focused agents, says Singh. These systems operate within defined boundaries, reducing the risk of incorrect or irrelevant actions . Additionally, due to shorter training cycles and improved self-directed learning, companies are observing 3–6 month deployment timelines with rapidly increasing accuracy.

While acknowledging that agentic AI remains in early stages, Singh says organisations are already achieving tangible benefits. He predicts agentic intelligence will transform supply chains by shifting control from humans to autonomous agents—hundreds of them, collaborating on tasks across procurement, logistics, and inventory management.

Importantly, Singh maintains a human-in-the-loop model, emphasising the need for governance—especially around data accuracy, algorithmic bias, and security. Corporate leaders must align AI initiatives with strategic objectives, invest in quality data infrastructure, and define clear decision boundaries to balance autonomy with oversight.

The broader context is clear: according to IBM’s Institute for Business Value, 75% of operations leaders expect AI agents to autonomously execute transactional workflows within two years. Singh believes agentic AI represents a fundamental shift—a move from reactive systems to proactive, self-operating supply chains.

In essence, IBM Consulting’s view, as articulated by Singh, positions agentic AI as a transformative force—one that promises operational efficiency, resilience, and agility by layering autonomous reasoning over enterprise systems. As this technology evolves from experimental pilot projects to integrated supply chain networks, it may redefine how modern industries respond to the unpredictable and complex challenges of global commerce.

Adobe’s Agentic AI Strategy Gains Early Recognition as BofA Hails It as Differentiator

Adobe is staking a growing claim in the burgeoning field of agentic artificial intelligence, technology designed to make autonomous decisions and execute complex workflows within software tools. According to Bank of America Securities, while Adobe’s agentic AI initiatives are still in their early phase, they stand out as a key differentiator that could yield significant business advantages, especially in areas like productivity, customer experience, and content creation.

The term “agentic AI” refers to advanced AI systems acting more like digital employees than standard chatbots, able to reason, plan, and carry out tasks with minimal human oversight. Adobe has already introduced several of these capabilities through tools such as Acrobat AI Assistant and early versions of agents within its Creative Cloud environment, allowing users to delegate tasks like document research, design refinement, and visual enhancement.

BofA’s recent analysis highlights that 64% of companies are preparing for agentic AI adoption by 2025, though most are still experimenting with pilots or in exploration mode—only about 6% have deployed it at scale . The potential is underscored by a forecasted $155 billion in global software spending by 2030 driven by agentic AI, approximately triple current industry estimates. Adobe is specifically mentioned among the top firms poised to benefit, alongside Salesforce, Microsoft, and ServiceNow .

Adobe’s roadmap centers on an ecosystem of purpose-built, context-aware agents—integrated into Acrobat, Express, Photoshop, Premiere Pro, and its Experience Platform. For instance, Acrobat AI Assistant can answer complex queries about document contents or generate role-specific agents, such as academic tutors or sales aides. In Creative Cloud, users may soon see agents recommending and applying image edits or preparing animated visuals via Adobe Express—all guided by natural language commands.

The company’s broader Adobe Experience Platform Agent Orchestrator, unveiled at Adobe Summit 2025, lets brands deploy ten specialized agents—like Content Production or Audience Segmentation Agents—to automate marketing and customer experience tasks. These agents can plan, execute, monitor, and optimize multi-step workflows in real time, enhancing both creativity and operational efficiency.

BofA’s endorsement suggests that Adobe’s early work in embedding agentic AI across its product portfolio may provide a competitive edge—particularly as companies seek to shift from generative AI experimentation to scalable, autonomous workflows that deliver measurable ROI. With generative AI adoption still evolving, agent-based automation has emerged as the next frontier in enterprise software.

Adobe appears well-positioned for this shift, having transitioned from generative model experimentation to integrating embedded agents that act on user intent. Its strategy emphasizes user empowerment, letting people stay in control while automating repetitive tasks and enhancing creativity .

While full maturation of agentic AI may still be years away, early-stage adoption across Acrobat, Creative Cloud, and Experience Cloud is demonstrating tangible benefits. As BofA notes, moving from experimental to production-grade deployments will be the key test. Adobe’s head start in this space could define its positioning as agentic AI becomes the next major platform battleground.

In summary, Adobe’s agentic AI push—rooted in contextually aware agents and autonomous workflows, is being recognized by financial analysts as a meaningful differentiator. With industry-wide attention turning to scalable AI-driven productivity, Adobe is betting that agency-powered tools will shape the future of creative and enterprise software.

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HCLTech Strengthens Its Bet on Agentic AI through Expanded Salesforce Partnership

HCLTech shares surged as the company announced an expanded strategic partnership with Salesforce to accelerate enterprise adoption of agentic AI, a form of artificial intelligence designed to perform autonomous tasks and decision-making. The immediate market reaction reflects optimism that this collaboration will position HCLTech at the forefront of the rapidly growing AI and digital transformation wave.

While financial specifics were not disclosed, the partnership builds upon HCLTech’s existing status as a member of Salesforce’s Agentforce Partner Network—a collaboration initiated earlier this year that positioned HCLTech to bring AI agent innovation to enterprises, especially in financial services. The expanded engagement signals deepened integration: HCLTech will now support a broader array of agentic AI deployments across sectors, leveraging its digital modernization experience and Salesforce’s Agentforce platform to deliver complex multi-channel AI services.

The timing of the announcement is key. As firms increasingly seek to automate not just rote tasks but decision-driven workloads, agentic AI represents a major frontier. HCLTech’s expanded role signals to investors that it is capitalizing on this shift. Analysts expect that its enhanced AI service portfolio will translate directly into new projects, higher client spend, and sustainable revenue growth—factors currently influencing its stock valuation.

HCLTech executives had previously stated that the Agentforce partnership involved piloting AI agent solutions at organizations like PenFed Credit Union—highlighting tangible use cases and returning demonstrable value. The expanded partnership is expected to catalyze widespread adoption, allowing enterprises in sectors such as retail, healthcare, and manufacturing to deploy AI agents capable of automating workflows, providing customer support, and enabling intelligent decision-making.

This collaboration aligns with Salesforce’s growing momentum in agentic AI—with its Agentforce AI tool earning investor confidence and industry attention. Salesforce’s acquisition of data management leader Informatica and strong analyst upgrades have highlighted Agentforce as a key strategic pillar. By deepening implementation capabilities through a trusted partner like HCLTech, Salesforce strengthens its competitive positioning in the enterprise AI services space.

From a market standpoint, analysts point out that HCLTech’s strengthened relationship with Salesforce can unlock incremental opportunities. As firms look to shift from pilot AI projects to enterprise-wide adoption, partners with seasoned delivery experience matter. HCLTech’s track record, scale, and global reach should help convert AI interest into revenue. Shareholders appear to have taken note—HCLTech’s stock has been gaining attention following the announcement.

In summary, HCLTech’s expanded partnership with Salesforce around agentic AI represents a strategic pivot in its digital services portfolio—from systems modernization to intelligent automation. It poises the company to lead with AI agents across complex industries and provides investors confidence that HCLTech is well-positioned to monetize the ongoing enterprise AI transformation.

Google Cloud Appoints Former Oracle Finance VP to Lead Cloud Finance Team

Google Cloud has recently added a seasoned finance leader to its ranks, appointing Nitin Agrawal, previously Vice President of Finance at Google Cloud and former Oracle Cloud finance executive, into a key leadership role to steer the unit’s financial strategy.

Agrawal brings deep expertise to his position, having served as finance chief for Oracle Cloud’s infrastructure business. Prior to that, he spent nearly four years in strategic finance roles at Google Cloud, as well as significant tenure at Microsoft—giving him a well-rounded understanding of cloud finance, revenue optimization, and cost management.

His new responsibilities include enhancing financial planning and analysis, optimizing spending across infrastructure and R&D, and strengthening investment frameworks for scalable cloud services. His dual insights from Oracle’s infrastructure-led financial models and Google’s large-scale cloud operations position him particularly well to drive profitability and financial discipline.

According to industry insiders, Agrawal has led finance teams through rapid investments and structured planning critical in an era where cloud providers must balance technology innovation with tight margin control. His appointment aligns with Google Cloud’s broader push to improve profitability while sustaining strategic investment in AI, data centres, and enterprise services .

Google Cloud’s move reflects a broader trend among cloud providers to bring in executives with both provider-side and vendor-side finance experience. By leveraging Agrawal’s cross-industry background, Google Cloud aims to fortify its financial operations—ensuring its growth narrative remains both expansive and fiscally disciplined.

With cloud competition heating up, strategic finance leadership becomes essential. Agrawal’s combined experience at Oracle, Google, and Microsoft positions him as a valuable asset to Google Cloud’s leadership team as it enters the next phase of growth driven by AI integration and global infrastructure scale.

Coursera Launches Upskilling Guide to Help Professionals Navigate the AI Transition

As generative artificial intelligence reshapes the global job landscape, online education leader Coursera has unveiled a comprehensive “AI vs Your Career” upskilling guide designed to help professionals adapt and thrive. Drawing on a blend of in-depth industry insights and practical learning pathways, the resource aims to equip workers with the knowledge and tools needed to stay relevant in an AI-augmented economy.

According to Coursera, demand for AI skills is surging across sectors, with employers increasingly prioritizing competencies such as prompt engineering, model deployment, and data ethics. A recent Coursera study found that professionals who add AI-related skills often see substantial gains in productivity, competitive advantage, and compensation—growing budgets for AI talent indicate that mastery of these skills is fast becoming a baseline requirement .

The guide begins by helping individuals assess where they stand. Whether you’re a software engineer, marketer, or business leader, understanding the AI use cases in your industry is crucial. Coursera recommends breaking down familiar roles into skill components—programming, data analysis, and problem-solving—and then anchoring AI learning around those domains. Python, statistics, and machine learning are highlighted as foundational skills, while non-technical competencies like critical thinking, communication, and ethical judgment are emphasized as increasingly valuable .

Its comprehensive approach lays out a clear progression: start with entry-level tools and concepts like generative AI, then advance toward specialized fields such as machine learning, NLP, and computer vision. Each stage is paired with recommended projects—like creating a sentiment-analysis model, automating a routine business task, or developing a small AI-powered app—to reinforce hands-on learning .

One distinctive feature of the guide is its spotlight on AI’s complementary roles. Drawing on economic research, Coursera underscores that while some routine tasks may be automated, demand is rising for human skills that augment AI—like ethical oversight, domain expertise, and creative problem-solving . The messaging is clear: AI does not replace humans entirely, but it does change what skills are most valuable.

Coursera also offers clear pathways for those considering a full career change. Through partnerships with top universities and tech firms, the platform now provides role-aligned credentials and micro-credentials in fields such as AI engineering, data science, and AI product management. These flexible programs allow for targeted upskilling or reskilling without committing to full degree programs, appealing to professionals seeking rapid transitions .

Importantly, the guide engages with ethical and social dimensions of AI. It encourages learners to understand bias in algorithms, data privacy implications, and the broader societal role of intelligent systems. Courses covering AI ethics, regulation, and responsible AI form a core part of the recommended curriculum—preparing professionals not just to build AI tools, but to build them responsibly.

To support learning, Coursera integrates practical tools on its platform. Features such as AI-powered course suggestions, virtual coaching, and performance analytics allow individuals to track progress and adapt learning paths. These embedded recommendations ensure ongoing relevance and continuous improvement .

As digital transformation accelerates worldwide, “AI vs Your Career” provides a structured roadmap for navigating disruption. By balancing foundational technical training, real-world application, and ethical awareness, the guide offers professionals a robust template for future-readiness. In the age of AI, Coursera is betting that those who actively learn and adapt will not just survive—but lead the next frontier of work.

Coursera Named a Leader in Forrester’s Q2 2025 Technology Skills Development Platforms Report

Coursera, a leading online education platform, has been recognised as a Leader and Customer Favourite in the recent Forrester Wave: Technology Skills Development Platforms, Q2 2025. This prestigious ranking reflects Coursera’s strength in delivering enterprise-grade tech reskilling solutions that meet the evolving demands of today’s workforce.

The Forrester evaluation highlights Coursera’s excellence across several key dimensions. The firm earned top marks in eight criteria, including its partner ecosystem, AI-powered personalization, course creation tools, and individual and team skills assessment capabilities. These strengths position Coursera as a premier choice for organisations pursuing comprehensive technology skill development programs.

Central to Coursera’s recognition is its collaboration with renowned universities and industry leaders. By delivering high-quality content from institutions such as Stanford, Yale, and Google, Coursera provides learners with access to cutting-edge expertise. Moreover, its AI integration enables personalized learning paths that adapt in real-time, ensuring learning programs remain relevant and effective .

Coursera also excels in its assessment tools, offering benchmarks for individuals and teams to measure learning progress against industry standards. This data-driven approach empowers organisations to identify gaps, tailor learning strategies, and demonstrate measurable outcomes from reskilling investments, a key concern for modern businesses.

In today’s fast-changing tech landscape, the ability to respond quickly to emerging trends is crucial. Coursera’s agility is evident in its capacity to incorporate new content—like AI tool training—within 48 hours of industry demand. This rapid content delivery helps businesses stay ahead of technology shifts and equips learners with actionable skills.

The combination of an expansive content library, AI-driven program development, robust assessments, and rapid content adaptation makes Coursera a compelling platform. Forrester’s designation of Coursera as a leader strengthens its position in the enterprise learning market, signalling its ability to meet rigorous evaluation standards and enterprise needs .

Coursera’s global reach amplifies its impact. With over 170 million learners engaging with its content, Coursera continues to expand its influence in both professional and academic arenas. Its tools not only support individual advancement but also help organisations build a digitally capable workforce equipped to meet future challenges.

The platform’s recognition also reflects a broader trend: companies increasingly prioritise skills-based learning to bridge talent gaps. Micro-credentials, professional certificates, and role-based curricula are becoming essential instruments in building workforce readiness in areas like AI, cybersecurity, cloud computing, and data science. Coursera’s ability to provide these scalable, verifiable credentials cements its strategic relevance.

As businesses across industries aim to stay ahead of digital disruption, platforms like Coursera play a vital role in enabling efficient, targeted upskilling. Coursera’s elevated standing in Forrester’s evaluation underscores its capability to deliver high-impact learning, grounded in content quality, technological sophistication, and outcome-driven design.

In conclusion, Coursera’s recognition in the Forrester Wave report affirms its leadership in enterprise technology skills development. By combining world-class content, AI-enhanced personalization, strong analytics, and rapid responsiveness, Coursera emerges as a trusted partner for organisations driving next-generation workforce transformation.

LinkedIn Introduces Conversational Queries to Revolutionize Candidate Search

LinkedIn has introduced a new AI-powered conversational query feature within its Recruiter tool, allowing hiring teams to search for candidates using natural, plain-language prompts instead of relying on traditional filters or Boolean search strings. Recruiters can now type requests such as “Find me marketing managers in Dublin with sales enablement experience” or “Find someone like [ideal candidate],” and AI transforms these queries into refined candidate searches, filling filter criteria with relevant skills, roles, and context shared in the prompt.

This innovation is built atop LinkedIn’s broader AI strategy, aligned closely with its parent company Microsoft’s investments in generative AI through OpenAI. Platforms like Vertex AI and GPT models drive the underlying intelligence, supporting features across LinkedIn from conversational search to profile summaries.

The new feature aims to simplify talent discovery and save recruiters time previously spent constructing complex searches. With AI assistance, recruiters can uncover better-fit candidates, even those not actively job seeking, by using conversational descriptions akin to speaking with a seasoned colleague. Recruiter users with this feature see AI-generated filter suggestions, including job titles, skill sets, locations, and experience levels. They can then adjust results by accepting or refining insights, creating a conversational search experience that becomes smarter over time.

Recruiter conversational search is currently available to customers using the Recruiter and Recruiter Professional Services editions, with languages including English, Dutch, French, German, Italian, Polish, Portuguese, Spanish, and Turkish. Analyst feedback suggests the tool could transform hiring workflows by giving recruiters more time to engage with candidates. LinkedIn highlights that around 20 hours per week are spent on administrative recruiter tasks — translating job briefs into technical searches — and conversational AI aims to offload a significant portion of that burden .

While the feature remains in rollout, early adopters such as Siemens and Robert Walters report productivity improvements: candidate outreach that once took hours is now completed in minutes, aided by AI-generated candidate lists and message drafts. Despite concerns about AI-driven bias, LinkedIn reports that the system incorporates guardrails to mitigate unintended exclusion and promotes inclusivity. Recruiters remain in full control of final decisions and can review every AI-suggested filter or candidate .

In essence, LinkedIn’s conversational Recruiter search is a pivotal step toward making recruitment more intuitive and intelligent—reducing complexity while still preserving recruiter expertise. As AI takes on routine search tasks, recruiters are freed to focus on deeper interactions and human-centered decision-making, reshaping how talent is discovered and engaged.

Hitachi Vantara Modernizes Belgium’s Largest Wastewater Facility with Advanced Storage Solution

Hitachi Vantara, the digital infrastructure arm of Hitachi Ltd., has partnered with Belgian wastewater operator Aquiris to modernize the data storage infrastructure at the Brussels-North Wastewater Treatment Plant, the country’s largest facility of its kind. Announced on June 24, the upgrade equips the plant with Hitachi’s Virtual Storage Platform One Block, delivering high performance, resilience, and sustainability to mission-critical operations.

Processing over 110 million cubic meters of wastewater annually, the Brussels-North plant relies on round-the-clock data collection—capturing more than one million data points daily—to oversee purification cycles and environmental compliance. To support this data-heavy monitoring regime, Aquiris replaced aging hybrid storage arrays with all-flash VSP One Block systems. The new setup delivers up to 30,000 IOPS, a 4:1 data compression ratio, and seamless support for over 50 virtual machines via VMware vSphere vMotion.

Guarding against downtime has been a top priority. The platform comes with a 100% data availability guarantee, ensuring operational continuity even during peak system usage or unexpected incidents. This reliability is especially important for wastewater treatment infrastructure, where delays can disrupt service and endanger compliance.

Sustainability was another core focus of the project. The VSP One Block features Hitachi Vantara’s Sustainability Guarantee, enabling Aquiris to monitor and reduce energy consumption and carbon emissions. Combined with advanced data compression and deduplication, the infrastructure promises both efficiency and lower environmental impact.

Juan Ochoa, Managing Plant Director at Aquiris, noted the transformative effect of the upgrade, stating that increased storage speed and resilience will enable the team to respond more effectively to operational anomalies and optimize purification performance. The modernization was carried out through Aretech, Hitachi’s trusted integration partner in Belgium, who oversaw the migration from hybrid to all-flash storage. The move enhances system responsiveness and provides the versatility needed for scalable future operations.

From Hitachi’s perspective, the deployment demonstrates its dual commitment to innovation and sustainability, especially in critical public infrastructure. By embedding strong cyber-resilience, high performance, and clear sustainability metrics into the VSP One platform, Hitachi Vantara aims to deliver unified infrastructure solutions aligned with ESG goals.

This initiative builds on Hitachi’s longstanding presence in water and wastewater management, reinforcing its track record in supporting intelligent infrastructure across more than 40 countries—linking its storage and hybrid cloud expertise with environmental stewardship.

In summary, this project positions the Brussels-North facility as a benchmark for digitalized water treatment. By combining high-performance storage, guaranteed availability, and sustainability features, Hitachi Vantara and Aquiris are setting new standards for resilient, eco-conscious critical services infrastructure.

LinkedIn Debuts Northguard and Xinfra to Replace Kafka for Enterprise-Scale Log Handling

LinkedIn has unveiled Northguard, a cutting-edge internal log storage system, alongside Xinfra, a new virtualization layer, both designed to supersede Apache Kafka and address the platform’s growing scalability and operability challenges.

After relying on Kafka for over 15 years, LinkedIn encountered limitations as its operational scale expanded—serving more than 1.2 billion members and processing tens of petabytes of data daily. These pressures motivated the development of Northguard, which introduces a modern architecture aimed at high-threshold performance and ease of management.

At the core of Northguard is a decentralized metadata management system that shares both data and metadata. By minimizing global state and balancing loads through log striping and segment-level replication, the solution avoids bottlenecks common in Kafka’s traditional partition-based design. It also uses Raft consensus for fault tolerance and supports advanced storage policies for data placement, replication and retention.

Complementing Northguard is Xinfra, a virtualization system for publish/subscribe infrastructure. Xinfra abstracts the underlying log systems, enabling applications to interact with unified Pub/Sub interfaces regardless of whether the data is stored in Kafka or Northguard clusters. Key features include transparent topic migration, dual writes during rollouts, epoch-based ordering, and unified metadata services. It also manages consumer group functionality using MySQL, Vitess, and ZooKeeper with low-latency caching from Couchbase.

Together, these innovations allow LinkedIn to transition thousands of mission-critical topics to Northguard without service interruptions. Xinfra currently supports over 90% of LinkedIn’s internal applications, ensuring a smooth migration path for existing Kafka deployments. The shift signifies a broader trend within LinkedIn’s infrastructure: prioritizing decentralized control, operational simplicity, and system resilience. Future enhancements include support for auto-scaling topics and even greater fault tolerance for virtualized workloads.

Reactions from the engineering community have been mixed. While Northguard addresses significant scalability issues, some developers have raised integration concerns, as it diverges from Kafka’s existing ecosystem. LinkedIn has mitigated these challenges through Xinfra’s compatibility layer, though some translation complexity remains .

Introduced during an internal meetup in April, the announcement featured talks led by senior engineers Onur Karaman (Northguard) and Wesley Wu (Xinfra), both formerly core contributors to Kafka’s scalability features within LinkedIn.

Northguard and Xinfra mark LinkedIn’s move beyond its own open-source contribution, Kafka, toward a self-optimized streaming infrastructure capable of sustaining future growth. Although not open-source, the platform’s in-house design may influence next-generation Pub/Sub solutions across the industry. As large-scale event streaming becomes a critical enterprise need, LinkedIn’s experience may offer valuable lessons in designing scalable, resilient, and tamed log management systems.

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