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What hands-on AI-102 preparation really looks likeThe State of Apache Airflow2026We asked 5,800 data engineers about how they're using Airflow in 2026. Find out what they had to say.Join Airflow committer Vikram Koka and other experts on February 5 to learn:Latest trends in how teams are using Airflow todayEmerging use cases from the annual Airflow surveyHow to make the most of Airflow for data orchestration in 2026Join us to hear directly from leaders in the community and discover how to get the most out of Airflow in the year ahead. February 5, 2026 | 11am ET / 4pm GMT.Reserve My SpotSponsoredWelcome to DataPro #163. This week, we’re introducing a new format with a hands-on case study from Microsoft Senior Solution Architect Peter T. Lee. Using a real multi-agent Azure AI system for student loan processing, the piece shows how practical projects can support Azure AI-102 preparation while building real-world system design skills.This case study focuses on what matters most when building AI systems in practice: orchestration, document intelligence, and clear decision-making. You’ll see how multi-agent design helps turn messy PDFs into explainable workflows, and why this approach is valuable not just for certification prep, but for anyone working with data and applied AI. Dive in to see how it all comes together.Cheers,Merlyn ShelleyGrowth Lead, PacktSubscribe|Submit a tip|Advertise with UsBook Now & Save 30% [Use code FINAL30]From Exam Prep to Production: Building a Multi-Agent Student Loan Assistant on AzureThe real problem: intelligence without orchestrationModern AI systems rarely fail because the models are weak. They fail because the surrounding system is poorly designed. Data is fragmented across documents, decisions are opaque, workflows are brittle, and human review is bolted on as an afterthought. This becomes especially visible in document-heavy financial workflows such asstudent loan processing.A typicalstudent loan application still depends on static web forms, manual document checks, and backend rules that are difficult to explain or adapt. Applicants upload PDFs, operations teams reconcile mismatched data, and decisions take days or weeks to arrive. From a data and AI perspective, the core issue is not intelligence but orchestration: it's how unstructured data, business logic, and decisions are connected in a controlled, auditable way.Reframing the workflow as a conversational systemTheMulti-Agent Student Loan Assistantaddresses this problem by rethinking loan processing as a conversational, agent-driven workflow. Instead of forcing users through rigid forms, the system allows applicants to interact naturally, upload documents directly in chat, review extracted data, and receive a clear, explainable decision.The emphasis is not automation for its own sake. It is about building structured intelligence where users remain in the loop, data is validated before decisions are made, and outcomes can be explained in plain language.Book Now & Save 50% [Use code EARLY50]Why multi-agent architecture mattersRather than relying on a single, monolithic AI model, the system is built around a multi-agent architecture. Different agents are responsible for conversation handling, document extraction, cross-document validation, and decision-making, coordinated through an orchestration layer.Crucially, business-critical logic such as debt-to-income calculations and interest rate evaluation is handled outside the language model. This separation between probabilistic AI and deterministic computation is what makes the system testable, governable, and suitable for regulated environments.For data professionals, this is the central architectural lesson: real-world AI systems demand decomposition, clear responsibilities, and deliberate boundaries between reasoning and rules.Document intelligence as a practical data problemPDFs remain one of the most common and problematic data formats in enterprise workflows. The student loan assistant demonstrates how OCR, structured extraction, and cross-document validation can be combined into a single, coherent process.Equally important is the human confirmation step. Extracted data is reviewed before automation proceeds, reinforcing trust and reducing downstream errors. This pattern is increasingly important as organizations move generative AI into operational systems.Why this matters for Azure AI-102 certification prepTheAzure AI-102 examincreasingly tests architectural thinking rather than isolated service knowledge. Candidates are expected to understand how Azure OpenAI, deployment choices, identity, containers, and operational constraints fit together in real solutions.This project mirrors that reality. It forces you to reason about service integration, workflow design, responsible AI considerations, and deployment trade-offs. The same mental models that help you understand this system also help you navigate scenario-based exam questions.How the book and this project help you learnThis case study follows the same learning approach used throughoutAzure AI-102 Certification Essentials. The book is written by Peter T. Lee, a Senior Solution Architect at Microsoft, with over 25 years of experience working with data platforms, cloud systems, and applied AI. Rather than focusing only on theory or exam definitions, the book is built around real scenarios drawn from practice. Projects like the Multi-Agent Student Loan Assistant show how Azure AI services are combined to solve realistic problems, helping readers understand not just what a service does, but when and why to use it.For learners, this creates a clear connection between certification study and hands-on work. The same concepts you revise for the exam are reinforced by building and inspecting real systems.What this means for data and BI professionalsFor data analysts, BI practitioners, and engineers moving toward applied AI roles, the shift is already underway. AI is no longer limited to dashboards or isolated models. It increasingly shows up as intelligent workflows that work with documents, validate data across sources, and support decisions in a transparent way.TheMulti-Agent Student Loan Assistantis best viewed as a learning example rather than a finished product. By exploring how it is designed and deployed, readers gain experience that carries over to bothAzure AI-102 exam scenarios and real project work. This blend of understanding and practice is what helps Azure AI skills hold up beyond the certification itself.A practical next stepFor many people preparing for Azure AI-102, the challenge is not finding information, but knowing how to connect it into a working system. Case studies like the Multi-Agent Student Loan Assistant help bridge that gap by showing how individual Azure AI services behave once they are combined, deployed, and constrained by real-world requirements.If you are studying for the certification, exploring projects like this alongsideAzure AI-102 Certification Essentialscan help ground abstract topics in concrete examples. The book provides structured guidance through the exam objectives, while hands-on systems like this one help you test your understanding against realistic design decisions.Taken together, they offer a way to prepare that is less about memorization and more about developing the judgment needed to design, build, and reason about AI systems on Azure, both in an exam setting and beyond it.If you want to explore this learning approach in more depth, you cancheck outAzure AI-102 Certification Essentialshere.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

.CloudPro #117Every week, AI helps your team work faster, but it also increases your data’s exposure. Files move between new tools, models use sensitive data, and traditional DLP often misses the most important context.OnFebruary 3 at 11:00 AM PT, we’ll introduce Cyberhaven’s data lineage powered and unified DSPM and DLP platform. You’ll see how one AI-native solution can finally keep up with the way data really moves.Join us live to see:The first public demo of our unified AI and data security platform, designed for the challenges of 2026 and beyond, including SaaS sprawl, shadow AI tools, and constantly moving data.How security teams gain x-ray vision into data usage, so they can spot the risky handful of actions hidden in millions of “normal” events—and stop them in real time, not after the damage is done.Hear honest stories from security leaders about where legacy DLP and standalone DSPM fall short, and how they are rethinking data protection by focusing on context instead of fixed rules.Get a preview of what’s next for DLP, insider risk, AI security, and DSPM from Cyberhaven’s product and leadership teams, along with our future investment plans.Register NowDon’t wait for another AI-related incident to reveal gaps in your data security. Reserve your spot and be among the first to see how a unified DSPM and DLP platform can change how your organization protects its most important data.The official Kubernetes Dashboard is getting archived after a decade. No active contributors or maintainers left. End of an era for one of the earliest K8s UI projects.Meanwhile, someone trained LLMs on three years of incident postmortems and built systems that predict outages 15-45 minutes before alerts fire. We're also covering K8s 1.35's in-place pod restarts, why learning Linux primitives makes Kubernetes finally click, and a Palo Alto DoS flaw that crashes firewalls into maintenance mode.Plus: 20+ tools that auto-generate K8s diagrams and a game where you fix 50 broken clusters to learn.Cheers,Shreyans SinghEditor-in-Chief3 Days Remaining: Book Your Seat NowGet 30% OffUse code FINAL30This Week in CloudKubernetes 1.35 lets you restart entire pods in-placeK8s 1.35 adds in-place pod restart (alpha, behind RestartAllContainersOnContainerExits gate) which is huge for AI/ML workloads. Previously if an init container corrupted the environment or a sidecar failed, you had to delete the entire pod and let the scheduler recreate it: slow and expensive. Now you can trigger a full restart that preserves pod UID, IP, network namespace, sandbox, volumes, everything except ephemeral containers. All init containers rerun from scratch, giving you a clean state.Training AI on your incident history predicts outages 15-45 minutes earlySomeone trained LLMs on three years of incident postmortems and built systems that predict failures 15-45 minutes before traditional alerts fire.The trick is extracting causal embeddings. Not just "symptom and cause are related" but learning the transformation from "what we observed" to "what was actually wrong." They decompose incidents into structured reasoning chains, create separate vector spaces for symptoms/causes/resolutions/precursors, then continuously pattern-match current system state against historical precursor embeddings.Every tool that generates Kubernetes architecture diagramsHuge GitHub repo comparing 20+ tools that generate K8s architecture diagrams from manifests, APIs, Helm charts, etc.KubeDiagrams leads with 47+ resource types supported, reads from manifests/kustomize/Helm/API, outputs to PNG/SVG/PDF/DOT, supports namespace/label clustering. Most tools use Python with Diagrams library, some use Go/TypeScript/Java. Common pattern: 60% support KIS (Kubernetes Icons Set), 45% do namespace clustering, 95% show Services, 80% show Deployments.Learn Kubernetes by fixing 50 broken clustersOpen source game-based K8s training with 50 progressive challenges across 5 worlds (Core Basics, Deployments, Networking, Storage, Security). Each level breaks something in K8s and you fix it using kubectl. Has real-time monitoring with "check" command, progressive hints, step-by-step guides, post-mission debriefs explaining why your fix worked.Palo Alto patched a DoS flaw that crashes firewalls into maintenance modePalo Alto patched CVE-2026-0227 (CVSS 7.7), a DoS vulnerability in PAN-OS firewalls with GlobalProtect enabled that lets unauthenticated attackers crash firewalls into maintenance mode. PoC code already exists and a researcher reported it, though no active exploitation yet. This is almost identical to CVE-2024-3393 from late 2024 which was a zero-day.Early Bird Offer: 40% Off for 72 HoursGet 40% OffUse code EARLY40Deep DiveWhy you should learn Linux before diving into KubernetesDocker didn't invent containers. It wrapped existing Linux features (cgroups, namespaces) that Google had been using for years into a simple interface anyone could use. Every K8s feature relies on Linux primitives: pod isolation uses namespaces (PID, network, mount, user, IPC), resource limits use cgroups, networking uses iptables/nftables for ClusterIP services and NAT, network policies use packet filtering, images use OverlayFS for layered filesystems, Cilium uses eBPF for high-performance networking instead of iptables. When you create a Pod, you're orchestrating Linux isolation and resource management tools. Understanding namespaces, cgroups, network filtering makes K8s and Docker click—you realize they're just convenient wrappers over powerful Linux capabilities. Learn the foundation first, the abstractions make way more sense after.Auto-comment K8s manifest changes on PRsGo tool that receives GitHub webhooks for PRs, auto-discovers ArgoCD apps configured with that repo as source, generates diffs against live state using ArgoCD CLI, and comments on PRs with markdown showing what would change. No per-repo configuration needed.How etcd actually works (and why Kubernetes uses it)etcd is a strongly consistent distributed key-value store using the Raft consensus algorithm. All writes go through an elected leader, changes replicate to followers, new elections happen if leader dies. Production clusters typically run 3 or 5 nodes (odd numbers only since you need majority for availability). K8s stores everything under /registry prefix with naming like /registry/pods/<namespace>/<pod-name> , uses prefix queries and watch subscriptions for real-time updates. This is how controllers and operators subscribe to resource changes.Kubernetes Dashboard is being archived after a decadeThe official Kubernetes Dashboard project is getting archived after no active contributors and maintainers running out of time to work on it. Started in 2015 when K8s was still new, it served the community for over a decade but ecosystem needs have changed significantly. End of an era for one of the earliest K8s UI projects, but makes sense given how much the tooling landscape has evolved since 2015.Self-healing infrastructure is running in production right nowAutonomous healing infrastructure isn't science fiction. It's operational in production serving millions of users, and the difference from past attempts is reasoning capability. The architecture needs four pieces: decision engine combining rule-based policies with LLM reasoning for edge cases, safety sandbox that never executes directly in prod (snapshots state, enhanced monitoring, automatic rollback on any degradation), graduated action library (green/yellow/red based on risk), and learning loop where every action generates training data to improve confidence scores.📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.If you have any comments or feedback, just reply back to this email.Thanks for reading and have a great day! *{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}