New DataMiner Platform Features for Professionals

Monitoring used to be about thresholds. But today’s operations are different. Cloud, software defined infrastructure, and highly virtualized ecosystems have changed the game. Failures no longer start with a single metric crossing a line. They start when behavior drifts across complex, interdependent resources. This is why monitoring itself is evolving. The upcoming Relational AI capabilities in DataMiner 10.6 reflect that shift. Instead of evaluating metrics in isolation, the platform learns how parameters and resources behave in relation to each other and detects early deviations even when everything still looks green. This is more than a feature update. It reflects a broader reality about AI in operations: It is not all about GenAI. Operational environments require a different kind of intelligence: 👉🏼 AI that works continuously, in real time, at the moment data is collected 👉🏼 AI that understands context, relationships, and operational behavior 👉🏼 AI that is embedded in the foundation, not added on top That is exactly what an xOps Operating System delivers out of the box. From thresholds to behavior From isolated metrics to system understanding From reactive alarms to early risk detection 💡The executive takeaway: If intelligence is not built into the operational fabric, it cannot keep up with the speed and complexity of modern environments. Because operational AI is very much also understanding your ecosystem in real time and detecting risk before impact occurs. https://lnkd.in/e64VXi_T

👀 Got a sneak preview of Ascend.io upcoming Agentic Data Engineering launch, and they’re not holding back. As a data engineer turned Snowflake solutions architect, I’ve seen more than my share of flashy AI launches. But this one stuck with me for a different reason. Yes, the AI features are 🔥 (agentic pipelines, smarter automation, error explainers). But what really impressed me? How smooth and familiar the experience still is for classic data engineering workflows. Even running Ascend on top of Snowflake, I was still able to connect to BigQuery and Databricks – all within a single project. No clunky hand-offs, no stitching together brittle pipelines across environments. Just clean, composable data flows that meet you where you are. What’s more: Ascend makes it easy to optimize and centralize your existing processes, so when you’re ready to go agentic, the leap feels more like a natural step than a full rebuild. That kind of gradual, guided transition is rare, and it makes a big difference. This is the kind of tooling consolidation I know people are looking for. Keep everything in Snowflake, but still interoperate with the rest of your ecosystem? That’s a big win. If you're in the data engineering trenches and tired of duct-taping platforms together, this might be worth a look. 💬 Curious: How are you all thinking about cross-platform data engineering right now? Are you consolidating tools or adding more? #DataEngineering #Snowflake #AI #AgenticDataEngineering #Ascend #ModernDataStack #Interoperability #SnowflakeNativeApp

BREAKING – Agentic Data Engineering is LIVE!!!! Over the past few weeks, I’ve been listening closely to data engineers talk about what slows them down the most: -- Constantly checking if pipelines broke (and why) -- Manually documenting lineage and logic for onboarding -- Chasing down schema changes after they cause issues -- Writing status updates that don’t reflect the real impact of their work -- Feeling like half their time is spent managing tools—not building That’s why Ascend.io’s announcement on Agentic Data Engineering is getting a lot of attention right now—because it speaks directly to those problems. Here’s what they’ve launched:  https://hubs.li/Q03n44B60 An intelligence core that tracks everything via unified metadata This includes: -- Schema versions -- Pipeline lineage -- Execution state -- Diffs across time And it does this automatically, with no extra config. A programmable automation engine Engineers can write their own triggers, actions, and logic tied to metadata events. It goes beyond traditional orchestration—because the system knows what’s happening inside each pipeline component. Native AI agents built into the platform These aren’t just chat interfaces. They operate on real metadata and help engineers: - Flag breaking changes while you were OOO - Convert components (like Ibis to Snowpark) - Create onboarding guides for new teammates - Trace full lineage of any column - Suggest QA and data quality checks - Summarize your weekly work for 1:1s - Even help prepare resumes by pulling your real impact from work you’ve done The biggest takeaway I’ve heard from engineers so far? This actually feels like it was built with us in mind. Not to replace the role—but to remove the repetition, surfacing the knowledge we usually have to explain again and again. It’s early days, but this looks like a shift in how modern data platforms could be designed: metadata-aware, programmable, and agent-powered from the start. If you want to take a look at the full experience and the agent capabilities, check it out here: https://hubs.li/Q03n44B60 I’m curious—what part of this would help your team the most? Or what’s missing from your current stack that a system like this could take off your plate? #ai #agenticengineering #ascend #theravitshow