Furō

We're pleased to be joining the ClickHouse House Mates partner ecosystem. House Mates is ClickHouse's global partner program, bringing together technology, services and reseller partners building solutions around ClickHouse. Looking forward to collaborating with the ClickHouse team and broader partner community! #ClickHouse #Furō #Observability #DataEngineering

Most FinOps teams hit the same wall when they try to run chargeback for Databricks. The request is standard: attribute platform costs to the teams or projects that incurred them. Chargeback 101. The data is the problem. Databricks charges in DBUs, consumption units that abstract the underlying compute. Your cloud provider charges for the infrastructure Databricks provisions — VMs, storage, networking — without knowing which workload generated which cost. This isn't a tagging problem or a process gap. The two systems were never designed to talk to each other. Neither one gives you the cross-referenced attribution a credible chargeback model requires. What most teams end up with: estimates that engineering can't explain and finance can't verify. lumin8 correlates both billing streams at the job level inside your Databricks environment. No estimates. No manual reconciliation. Cost attribution finance can actually trust. See how it works → https://lnkd.in/gJ8sYftj #Databricks #FinOps #DataEngineering #CostObservability #CloudCost #DataPlatform #lumin8 #CloudFinOps

You can see your application in production. Can you say the same about your pipeline? Most engineering teams have invested heavily in application observability. Their CI/CD systems? Fragmented at best. GitHub's 2026 Actions security roadmap is direct: as automation becomes more powerful, organisations need continuous observability across CI/CD execution — not fragmented, retrospective monitoring. CI/CD isn't a development tool. It manages your releases, your secrets, your deployments. An unobservable pipeline doesn't just create operational blind spots — it creates an undetected attack surface. Supply chain compromise and lateral movement through deployment infrastructure are the failure modes that compound silently. What we observe across enterprise clients: CI/CD governance is treated as a delivery concern until it becomes a security incident. By that point, proactive remediation is no longer on the table. The organisations that get this right treat CI/CD and application governance as the same discipline. GitHub's Actions Data Stream addresses this gap — but only for teams whose governance model is already positioned to consume it. This is the problem we work on with enterprise teams across APAC. Talk to us at → https://lnkd.in/gY_2WY4A #DevSecOps #PlatformEngineering #CICD #Observability #GitHub #CloudSecurity

Most Databricks teams can see their costs. Explaining them is a different problem entirely. Visibility is having the numbers — DBU reports, cloud invoices, usage dashboards. Most teams have all of that. Explainability is being able to answer the questions those numbers raise: → Which job drove last month's spike? → Which team owns the spend? → Did that cluster optimisation actually reduce total cost — or just shift it from the Databricks bill to the cloud bill? Neither billing system answers those questions on its own. And bridging them takes more than a notebook. Our latest article breaks down exactly why — the structural reason the two systems can't reconcile themselves, and why having the data is not the same as having the answer. Full article linked in the comments → #Databricks #FinOps #DataEngineering #CostObservability #CloudCost #DataPlatform #lumin8

Yesterday we attended #OpenSummitAI, seeing firsthand how quickly the conversation around AI is evolving — from experimentation to operational reality. A lot of focus on agentic AI, orchestration, automation, and what it actually takes to deploy AI meaningfully inside organisations at scale. Interesting discussions around governance, operational complexity, and the practical challenges organisations face as AI adoption matures across enterprise environments. It was great to see how different industries are approaching AI with a stronger focus on operational implementation and real-world application! #AI #AgenticAI #DevSecOps #FinOps #PlatformEngineering #CloudGovernance #Furo

The worst time to find out you have a cost problem is when the invoice arrives. By then, a job has been running unchecked for weeks. A cluster auto-scaled without anyone noticing. And your DBU reservation may be burning faster than expected — with no visibility into whether you'll exhaust it early and tip into on-demand pricing. Without automated monitoring, these problems compound quietly. By the time they surface, the spend has already happened. lumin8 surfaces cost drift, anomalies, and reservation utilisation inside your Databricks environment — before they become budget problems. Keep unexpected bill surprises at bay with lumin8 → See it in action https://lnkd.in/gg5ArUnJ #Databricks #DataEngineering #FinOps #CloudCost #CostObservability #DataOps #lumin8

Why are Databricks costs so hard to attribute? It starts with how Databricks billing works. Databricks charges DBUs. Your cloud provider charges for the underlying compute, storage, and networking. Two independent systems, measuring different things. Here's where it gets complicated – your cloud bill includes everything running on cloud, not just Databricks. So before you can attribute cost to a team or project, you need to answer two questions first: → Which portion of your cloud spend belongs to Databricks? → Which specific jobs or queries generated it? And this is exactly why we built lumin8. lumin8 runs inside your Databricks environment, correlates both billing streams at the job and query level, and attributes cost by any business dimension — team, project, cost centre, business unit. This means less time troubleshooting cost issues and more time on the work that matters. If cost attribution is a challenge for your team, speak to us about lumin8 today —> https://lnkd.in/g8fKZykp #Databricks #lumin8 #FinOps #CloudCost #CostObservability #DataPlatform #DataEngineering #Furō

Everyone's adopting AI coding tools. Few teams are thinking about what happens to their security posture when hundreds of engineers are using them at scale. AI accelerates delivery. It also accelerates the introduction of vulnerabilities — unless your SDLC has the controls to catch them before they ship. Most organisations are still treating AI adoption as a tooling decision. The ones getting this right are treating it as a DevSecOps problem before it becomes a security incident. Secret scanning, code scanning, supply chain visibility — these aren't optional extras when AI is writing code in your pipeline. They're the baseline. The tools have changed. The discipline hasn't. If your SDLC hasn’t evolved since introducing AI tools, your risk profile already has. #DevSecOps #ApplicationSecurity #PlatformEngineering #AICoding #CyberSecurity 

We built lumin8 to make Databricks costs explainable, attributable, and governable. If you're running Databricks, these challenges will sound familiar: understanding what a job actually costs across both DBUs and cloud infrastructure, knowing which teams are driving spend, and catching cost issues before they compound. In this carousel, we highlight five common Databricks cost challenges and how lumin8 solves each one. See lumin8 in action: https://lnkd.in/gi_TBJXD #Databricks #FinOps #CloudFinOps #DataPlatform #DataEngineering #AzureCloud #AWS #PlatformEngineering #Furō

Four platforms. 7,500+ repositories. 1,800 users. No repeatable model to consolidate them without starting from scratch each time. The difference between a migration that stalls and one that scales isn't resourcing. It's architecture. IAG needed to move from Bitbucket, GitLab, Azure DevOps, and GitHub Enterprise Server into a single GitHub Enterprise Cloud environment — at scale, across every business unit, without external support at every step. We designed and delivered a self-service migration framework using GitHub-native tooling — GitHub Actions, reusable workflows, automated validation — so their engineering teams could run migrations independently, with the internal capability to keep going without vendor lock-in. The outcome: 80% reduction in migration cycle time. 2,000–4,500 hours in projected savings. A repeatable model that scales across every business unit. The framework you build matters more than the headcount you throw at it. Read the full IAG case study — https://lnkd.in/gHvNf3QY #GitHub #DevSecOps #PlatformEngineering #SourceControl #GitHubMigration #GitHubEnterpriseCloud #Furō #IAG