Honeycomb Telemetry Pipeline: A New Era of Observability

Gartner outlines best practices for data warehouses, lakes, lakehouses, and hubs but says little about real-time. Discover the key limitations of these architectures and how SingleStore enables real-time performance, concurrency, and AI application serving. https://ow.ly/Vogq50X3zqQ

Gartner outlines best practices for data warehouses, lakes, lakehouses, and hubs — but says little about real-time. Discover the key limitations of these architectures and how SingleStore enables real-time performance, concurrency, and AI application serving. https://ow.ly/unBJ50X61jY

Yesterday, I had the opportunity to attend the CxO Institute conference, where I connected with engineering, product, and data executives and moderated a discussion about the challenges of making data AI-ready—not just for lab environments, but for real production workloads. A few key themes stood out: 1. Cost and complexity. Many teams found that projects were far too expensive and took too long to deliver. 2. Elusive quick wins. One of the biggest hurdles was figuring out how to unlock value incrementally without overbuilding infrastructure. 3. Production disappointments. Most of the execs I spoke with had already invested heavily in AI initiatives but struggled to take them from 90% complete to production-grade systems that can earn stakeholder trust. In the session I moderated, our discussion covered topics like keeping vector data up to date, building data lakes that unify analytical and operational data, and creating a digital twin of a business that agents can use to quickly gain context needed to move towards their goals. We also explored the core technologies that would be needed to bring these systems into production. What surprised me most was that everyone knew what architectural building blocks were required to make this work. The problem was that the tradeoffs and limitations in practice from various vendor solutions made the execs I met with think their ideal architectures were just out of reach given the current state of the data infrastructure ecosystem. Excited to show that Materialize can actually deliver in my follow up demos!

In a Q&A with Roberto V. Zicari of ODBMS, Stephen Goodman, Principal at Vector Capital Management, shares why SingleStore stands out in the evolving enterprise AI data platform space: from market dynamics to technical edge. Don't miss this deep dive into the strategic thinking behind the acquisition: 🔗 https://ow.ly/qNMf50X6p4i

Last week we held ObservabilityCON - The Grafana flagship event of the year. Here are my 5 favourite product announcements of last week 👇 Adaptive Traces Grafana’s new trace sampling engine dynamically adjusts sampling rates based on signal quality and service criticality. This ensures you retain the right traces at the right time. Customers are already seeing up to 90% reductions in trace storage costs while increasing sampling volume — improving visibility without breaking budgets. Database Observability (DB o11y) A unified approach to query-level and performance observability across heterogeneous databases. This allows teams to correlate query patterns, latency, and resource usage within Grafana’s composable stack. It’s a massive opportunity — a space traditionally dominated by legacy vendors — and Grafana is entering it the Grafana way: transparent pricing, interoperability, and real value for money. The Knowledge Graph A graph-based correlation layer that connects metrics, logs, traces, and profiles into a single contextual knowledge model. The result: root cause analysis in seconds through contextual linking and intelligent relationship mapping. Grafana Assistant A generative AI-driven assistant integrated into the observability workflow. It helps teams write queries, interpret dashboards, and surface insights using natural language — reducing cognitive load and accelerating MTTR. Adaptive Profiles A new generation of scalable, continuous profiling that adapts sampling dynamically to workload intensity. This provides deep performance insight across fleets without unpredictable cost growth. All these innovations address the three core challenges Grafana solves within the O11y industry: > Managing exploding data volumes and cost > Avoiding vendor lock-in through a Big Tent, composable architecture > Maintaining Open Source and open standards (OTel) at the core Philippe Stevens Jens Breuring Daniel Axelsson Carlos Silverberg Pietari Kankare Marco Donkers

Heading into the weekend thinking about two conversations I had this week. Company A (major financial institution): Built a data lake: $5M Hired 30 data scientists: $6M annually GPU clusters: $3M AI platform licenses: $2M Integration layers: $8M Governance committees: Priceless 18 months later: 3 proof-of-concepts, 1 pilot in production, abandoned after 6 months. Total spend: $30M+ Company B (regional manufacturer): Unified 47 legacy systems into single source of truth: $150K 30 days to data unified Hired 2 ML engineers: $300K annually Built predictive maintenance model: 60 days 90 days to full production Result: 47% reduction in equipment downtime, $4.2M saved first year. The real difference: Company A: Data scattered across 47 systems. Data scientists spending 80% of their time on data plumbing, 20% on actual models. Company B: All data in one unified architecture. Those same ratios flipped. AI models need fast access to complete data. When you can query across your entire business in 5ms instead of 500ms, when your data is real-time instead of yesterday's batch run, when engineers build models instead of building data pipelines—AI stops being theoretical. The objection I always hear: "But our enterprise is more complex than a manufacturer." Exactly. That's why you need simpler infrastructure. Complexity should live in your business logic, not your data architecture. The more complex your operations, the MORE you need unified data. Otherwise you're asking AI to solve problems it can't even see. Company A's post-mortem: "AI is harder than we thought." Company B's CEO: "Turns out AI is easy when your data isn't scattered across five decades of systems." Weekend question: How much is your organization spending to stay "AI-ready" versus actually deploying AI that changes outcomes? Have a good one. 🥃

🔄 Streaming data is everywhere — but how you process it can make or break your architecture. In our latest blog, we break down stateless vs stateful stream processing: what they mean, how they work, and when to use each. If you’re building real-time applications or modern data pipelines, this guide will help you choose the right approach and avoid common pitfalls. 👉 Read the full post: https://lnkd.in/gNeY8Eji #StreamProcessing #RealTimeData #DataEngineering #Kafka #Flink #StreamingAnalytics #DataArchitecture

Most data headaches look different on the surface… but they often come from the same root problem. Dashboards that never agree Pipelines breaking after every schema change Skyrocketing compute costs Governance gaps that keep auditors awake AI pilots that stall before they scale These aren’t random issues. They’re symptoms of a data foundation that isn’t built to last. That’s where the Medallion Architecture comes in. Layered by design, it delivers: reliability, trust, cost efficiency, and AI readiness. 👉 Swipe through this carousel to see the 5 biggest signs your data architecture needs a Medallion upgrade #DataArchitecture #MedallionArchitecture #MicrosoftFabric #DataEngineering #DataStrategy #ModernDataStack #DataGovernance

🚀 I just published “A Vision for Application Observability”, where I explore how our systems and our understanding of them can and should evolve beyond dashboards and metrics into something more meaningful: context. Observability isn’t just about collecting logs or wiring up Prometheus. It’s about understanding how things connect, where they fail, and why they matter within the system context. Some of the key ideas I unpack: - Mapping end-to-end service flows to see how work actually moves through your system. - Ensuring logs and events are anchored in that flow, not just timestamps floating in space. - Treating user impact and cost as part of observability, not separate concerns. - Building observability systems that are self-documenting, that is, where insights emerge naturally from how they’re designed. What started as a reflection on metrics based on some current work I am doing became a philosophical exploration that turned into a call to re-examine our assumptions. We build sophisticated systems, but sometimes we forget to ask: Do we really understand how they behave? How can we do that as complexity explodes? New Math Data #Observability #SoftwareEngineering #DataPlatforms #CloudArchitecture #NewMathData #SystemDesign

The Convergence Point: Where GenAI Meets Data Engineering The next disruption isn’t model scaling — it’s data convergence. LLMs aren’t replacing your data stack. They’re exposing every semantic gap inside it. Hallucination isn’t a model flaw; it’s a governance failure. Lineage, metadata, and contextual signals now define accuracy ceilings. Data engineering is evolving fast: • Pipelines → Context Flows • Catalogs → Graphs • Observability → Explainability The boundary between data and intelligence is gone. Enter the GenAI-Data architecture — reasoning systems built on governed, traceable context. #GenAI #DataEngineering #AIArchitecture #DataGovernance #EnterpriseAI