Featured Issues

Details shared, maybe worth a look?Least Agency Beats More AgencyWhat the AI-native platform discussion got right, and why it matters next for network and Linux operationsHi, welcome back.Starting with the next edition, you’ll receive this newsletter directly from our Substack, so keep an eye out for it there.Last week, we looked at AI-native platform engineering, and the conversations around it raised a useful argument for anyone working with agents: the future is not "give agents more autonomy." It is "give them the smallest useful amount of autonomy", a more uncomfortable idea than it sounds, since most AI demos move the opposite way: more tools, more context, more access, more actions. It's impressive because the agent can do a lot, but real infrastructure optimizes for known scope, clear ownership, safe failure, and boringly good recovery.The phrase that stuck with me was least agency, in the same spirit as least privilege. Least privilege asks, "What is the minimum access this thing needs?" Least agency asks, "What is the minimum decision-making power this thing needs?" That is the real split: autonomous by default vs constrained by design.You can see that problem play out in two places most CloudPro readers know well: network operations and the Linux command line.Engineering Agentic Network Operations, LAST 10 SEATS!That's why, on June 30th, we're bringing together William Collins and John Capobianco for a live, hands-on workshop to help you move from AI experimentation to production-ready Agentic NetOps.You'll learn how to:• Build MCP skills that are composable, reusable, and production-ready, not just proof-of-concepts• Apply spec-driven development to define and constrain agent behavior before it creates operational risk• Design resilient agentic loops with failure recovery patterns and safe handoffs for real-world network environmentsThrough live instruction, demonstrations, and guided labs, you'll gain practical experience across a modern AI networking stack including MCP, OpenClaw, NetClaw, Python, Claude, Selector AI, and Containerlab.🚨 Only 10 seats remain for the live session.Use code LIMITED40 at checkout to receive 40% off your registration, available exclusively for you.BOOK YOUR SEAT NOW📅 Tuesday, June 30 | 9:00 AM–12:00 PM EDTAgentic Linux – From Commands to AI AgentsThis eventis on27 June, and it brings the same question to the shell.Taught by Imran Afzal, a best-selling Udemy instructor who's trained 1M+ students and spent 25+ years running infrastructure at Fortune 500 companies before he started teaching it, this session brings the same question to the shell. AI can write commands and explain logs in seconds, but Linux does not forgive blind execution.You'll work hands-on with ChatGPT CLI, Shell Genie, Aider, and Goose CLI, with review and human approval kept at the center throughout. If you're a Linux admin, DevOps engineer, SRE, or technical learner, this is a way to try agentic CLI workflows without treating your terminal like a toy, guided by someone who's actually run that terminal for a living.Join it live while there's still room.Use code EARLY40 to get 40% offBOOK YOUR SEAT NOWRemember, an agent should know when it is missing context, when it is guessing, when the next step changes state, and when to hand control back. Otherwise, you get the ops version of the "Automation is great" meme;That is where "human in the loop" gets exposed, if the agent moves faster than the review path, the human is not in the loop, just watching the replay.The platform engineering conversation made one thing clear to me:AI-native operations will not be won by the teams that hand agents the biggest set of keys. It will be won by the teams that design thesmallest usefulloop, then make that loop observable, reviewable, and safe enough to run again.I’llbe in attendance atthesesessions as well.If this is already showing up in your team’s roadmap, this is the wrong one to bookmark and forget. The useful questions usually come up live, in the labs, when the agent does something slightly awkward,and everyonehas totalk through what should happen next.Cheers,Apramit BhattacharyaEditor-in-Chief.P.S. If you attended the platform engineering session or have an opinion on today’s topic, do reply to this mail. A good question from the chat counts.*{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} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;display:none;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}.social_block .social-table{display:inline-block!important}}

Our Data Engineering Byte Newsletter gives data engineers and practitioners what they often lack today: clear, real-world insights—where every byte tells a story.Subscribe here to stay ahead in data engineering. Back from Data + AI Summit 2026: The Announcements That Matter for a Data Warehouse Modernization Program By Laurent Leturgez, Lead Product Specialist - Data Warehouse Modernization, Databricks   More than 30,000 attendees filled Moscone this year. Most of the coverage went to agents, but for anyone responsible for modernizing a data warehouse the more useful story was elsewhere: transactional, real-time and analytical workloads converging onto one governed copy of data, open table formats reaching general availability, governance and cost moving to the center of the platform, and sharing becoming an open protocol. The event made the target architecture clearer than it has been for some time, and it lowered several of the practical barriers that usually stall a migration.  The mood at Moscone Most booths on the expo floor opened with the same word: agentic. The conversations that held my attention concerned something more practical, namely the infrastructure that makes agents safe in production: where the data lives, which identities (human or machine) are allowed to act on it and how those actions get audited afterward. The co-founders carried most of the stage, with guest appearances from Satya Nadella, OpenAI's Greg Brockman, and PepsiCo on the customer side. The audience has clearly broadened, with application and platform teams now sitting alongside the data engineers. What follows is a grouped summary of the announcements, with a focus on the ones that change the calculus for a warehouse migration or modernization program. Architecture: one governed copy of data The headline architectural theme was consolidation. LTAP, Lake Transactional/Analytical Processing, unifies transactional, analytical and operational workloads on a single copy of storage in the lake under one governance model. The component that makes the idea concrete is Lakebase, serverless Postgres on open object storage, which reached general availability as the low-latency, transactional read/write layer. Alongside it, Lakehouse//RT brought real-time analytics directly onto the same governed data, and Reyden was introduced as the fastest query engine Databricks has built. For two decades the standard pattern placed an OLTP database on one side, a warehouse on the other, ETL in between and copies of data scattered across both. This set of announcements aims at that separation. The direction is consistent: fewer copies, fewer pipelines and one place to apply governance. Open formats and interoperability For anyone worried about lock-in, this was the most reassuring part of the week. Iceberg v3 and Managed Iceberg both reached general availability, geospatial types in Delta and Iceberg v3 went GA and external read access to managed Delta tables entered public preview. OpenSharing, an open protocol contributed to the Linux Foundation, extends the zero-copy approach of Delta Sharing to the agent era, letting agent skills, models and unstructured data move across organizations and platforms without copying files or depending on a proprietary marketplace. The pattern across all of it is that the industry is settling on open table formats and open protocols as the shared substrate, then competing on what gets built above them. That is a healthy signal whichever platform you currently run, and it matters directly for migration, which I come back to below. Governance and control Governance was where the heaviest general-availability work landed. Attribute-based access control reached GA for row filtering and column masking and external lineage went GA across upstream sources and downstream BI tools. A new Governance Hub entered private preview as a single place to monitor posture across data, AI, cost and performance. Unity Catalog also continues to operate as a single governance plane over external catalogs through catalog federation, querying that data in place with consistent access control, lineage and audit. Mastercard presented this running across Databricks and AWS. Cost as a first-class concern A more candid theme this year was cost. The message was that agentic workloads will get expensive, and that teams need visibility and control before the bills arrive. The Unity AI Gateway, now in beta with contextual service policies, governs every model, tool and agent through one set of access controls, cost monitoring and smart routing across both Databricks-hosted and external models. Treating cost discipline as a platform feature rather than an afterthought was a notable shift in tone. Agents and Genie, in brief On the agent side, Genie Ontology was announced as a self-improving context layer that learns business knowledge from data, documents and workplace apps, and Genie One reached general availability as an agentic coworker that answers questions against governed data through SQL and produces reports and artifacts. Omnigent was introduced as an open layer for supervising agents that orchestrate other agents. These are relevant to modernization mainly because they raise the value of having clean, governed, well-modeled data underneath, which is exactly what a good migration delivers. What this means for a migration or modernization program This is the part closest to my own work. Several of the announcements change the economics and the risk profile of moving off a traditional data warehouse. First, open formats lower the cost of moving. With Managed Iceberg and Iceberg v3 generally available, tables are no longer tied to a single engine,  a migration becomes then a staged exercise instead of a single high-risk cutover. Second, catalog federation removes the need to lift and shift on day one. You can place a single governance plane over your existing catalogs and query the data in place while you migrate one workload at a time, which carries far less risk than the big-bang approach that has stalled so many programs. Third, consolidation through LTAP, Lakebase and Lakehouse//RT removes part of the original rationale for a separate warehouse. When transactional, real-time and analytical workloads share one governed copy of data, much of the pipeline sprawl that justified a standalone warehouse no longer needs to exist. Fourth, the governance and cost work matters more than it first appears. A migration is rarely blocked by technology alone. It stalls on the inability to predict spend and to prove control. ABAC at GA, external lineage, the Governance Hub and the Unity AI Gateway's cost routing give a program the guardrails that finance and security teams ask for before they sign off. None of this makes migration trivial. The hard parts remain especially on SQL or procedural SQL dialects translation. With the new agentic capabilities of Lakebridge that have been presented during DAIS, this will definitely ease the code migration process while reducing the time to migrate. Many of the themes announced at Data + AI Summit 2026 like open formats, governance, federation, and workload consolidation are the same patterns organizations are using today to modernize their analytics platforms. For readers interested in a deeper dive into the migration strategies and architectural trade-offs behind these shifts, my book, Modernizing Analytics Beyond the Data Warehouse, explores the topic in detail.  Closing thoughts Setting the agent narrative aside, Data + AI Summit 2026 was about removing seams: between transactional and analytical processing, between batch and real time, between platforms through open sharing and between separate catalogs through federation. For a modernization program the practical advice holds whatever your vendor preference: commit to open formats, plan the governance and cost layer early  and treat migration as a staged journey rather than a single event. Author BioLaurent Léturgez is a data platform specialist with over 20 years of experience in database systems. He works as a Product Specialist for data warehouse migrations at Databricks, where he helps organizations modernize their data warehouses on Databricks. Previously, as an Oracle Certified Master and Oracle ACE, he spent years working with Oracle technologies—from database administration and architecture to performance tuning and consulting across Europe. This rare combination of deep legacy database expertise and modern data engineering knowledge gives him a unique practitioner’s perspective on the challenges and opportunities of data warehouse modernization. He is based in Lille, France.