Daniel Neff

Pragmatic Engineer did a great deep dive on "Building MCP servers in the real world": https://lnkd.in/ghBu9fwV Great insights on the growing use of *internal* MCP servers, like for documentation, data analytics, debugging, issue triaging, etc. That's why MCP auth and networking is so important. I highly recommend the Pragmatic Engineer newsletter -- I finally upgraded to a paid subscription today, reimbursed by Microsoft in PD budget.

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There's been some talk around storage unification and zero-copy lakehouse integrations recently, and I wanted to better define these terms as well as look at ways we should evaluate different design decisions in this space. So I’ve published a new post: A Conceptual Model for Storage Unification Storage unification isn't about “one storage format to rule them all,” but virtualizing them into a unified logical model. In this post, I lay out a conceptual framework for thinking about storage unification (aka data virtualization). I explore: 🔹 Internal vs. shared tiering (and why shared tiering is so tricky). 🔹 When materialization makes more sense. 🔹 Client vs. server-side stitching. 🔹 Lifecycle and schema management across evolving formats. 🔹 Seven key considerations for evaluating different approaches. This is the groundwork for a set of posts where I’ll dig into how some real-world systems deliver storage unification, primarily about unifying real-time and historical data. https://lnkd.in/dBiS2kxJ

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See why OCI leads the AI infrastructure market with RoCE-optimized networks, demonstrated scalability to over 32,000 GPUs, and ultralow latency at scale. https://lnkd.in/dnt_ksDb

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 Observability: The Hidden Hero in System Reliability 🕺 🏈 During a major live event—think Super Bowl or award show night—everyone’s watching. Not just on TV, but in our dashboards too. API response times across our p90, p95, and p99 percentiles kept us glued to the dashboards and email alerts like a war room on high alert. But that wasn’t always the case. We’ve had moments where missing observability slowed us down: • 🚨 A silent failure in a downstream service… only caught because error email alerts from our API started firing • 🧯 A circuit breaker saved us from a cascade failure—but that pain also led to smarter caching and prefetching • 🖼️ A frustrating image update issue on client—data was fine, but the cache never got the memo. We only found out after an escalation 😬 That last one really made us ask: 🔍 Could we have seen this earlier with better observability at the edge? Here’s what I’m learning as I go: ✅ Observability isn’t just “we have logs” 😴 It’s “I trust our system enough to sleep during a release” 🔁 And it’s never “done”—it evolves with every failure 👀 I’m curious: What’s one observability blind spot you discovered too late? Or something your team built that saved your day? Let’s swap lessons—we’re all learning. #observability #softwareengineering #nfr #leadership #techstories #reliability

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The majority of companies interviewing developers are asking questions relevant 5-10 years ago - the tectonic plates have shifted, learn an AI-Native development workflow or become obsolete. Algorithms are secondary, structure and process are now king. 👑

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✨ AWS Lambda durable functions! Since its release, I've been gathering some reflections on how it compares and contrasts with AWS Step Functions, particularly its 'standard' workflows. To better understand the ins and outs of a Lambda durable function, I've been reading up on the #AWS docs and other resources... until I came across the following blog. (Link in the comments as well). https://lnkd.in/edmE3gCA My friend Andres Moreno has written a great article that provides everything you need to fully understand the durable functions. 🙌 Just loved the way he has structured the article so anyone can understand it with supporting code. ❤️ My only comment is that perhaps Andres could have avoided the overused picture. 😜 Now with Lambda durable functions and Step Functions, you can walk along with both in your serverless journeys. 😂 Amazon Web Services (AWS) #serverless #awslambda #awsstepfunctions #cloud #durablefunction

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With Finch (AWS's container development tool that provides an alternative to Docker), AWS SAM can provide a local environment similar to AWS Lambda as a container to build, test, and debug your serverless applications.

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