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I know why the Fivetran-dbt merger won't answer, "Why isn't our data delivering value?" They have 80-90% customer overlap. Most data teams already had both tools. The system trains us to shop for better architecture when the constraint is something else entirely. Happens time and again: Teams run both Fivetran and dbt with perfect pipelines moving millions of rows, elegant transformation logic, comprehensive testing—and data that sits unused. Maybe this merger creates tighter integration. Unified metadata, coordinated workflows, a single vendor relationship. That might be valuable once you know what you're building toward. But here's what's actually broken: Most organizations never defined which 2-3 business questions were worth $10M+ if they solved them. They built comprehensive infrastructure to answer every *possible* question. Then got frustrated when the dashboards gathered dust and the reports went unread. The system told them better tools would fix it.  The system was wrong. The teams I've seen get 10x returns from data in months rather than years did something different. 👉 They started with a critical business question.  👉 Identified the minimum data needed to answer it.  👉 Built exactly that, proved the value, then expanded. No comprehensive infrastructure.  No future-proofed architecture.  Just focused execution on defined outcomes. Platform consolidation accelerates execution once you know where you're going. It doesn't create clarity about where to go. — Making data work means defining the outcome before evaluating the platform.

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The Modern Data Stack promised best-of-breed tools that work together seamlessly. The paradox: the more tools you pick, the more integration work you create. One perspective I find helpful: Dagster as the connective tissue. A good orchestrator doesn't just schedule jobs - it helps integrate your entire stack. The downside of unbundling is the integration burden. The upside is flexibility. https://lnkd.in/ePcZhWSt

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Let's try this again 😆 Why Ontology Pays Off When your data fits together, everything gets easier. With BrickX, Switch clients are seeing: 🚀 Faster deployments 📉 Less manual engineering 📊 Consistent, validated data across portfolios What once took weeks of mapping now takes hours. Ontology doesn’t just organize data — it accelerates performance, enabling insight at scale. #BrickX #SmartBuildingResults #DataDriven #SwitchAutomation

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“It’s not whether you can build the system. It’s whether you can build the right one.” Modern tooling makes it easy to stand up production-grade data systems. The harder problem is defining shared concepts across an entire firm and ensuring those definitions hold as the organization grows. In our latest Substack post, we write about how data strategy has shifted from a technical challenge to a business one, and what that means in practice. 🔗 https://lnkd.in/ej3caCXp

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🎯 Role Spotlight: Data Engineers They’re not just wrangling datasets — they’re unlocking decision-making power across the enterprise. From building resilient pipelines to orchestrating seamless data flows, Data Engineers transform raw chaos into structured brilliance. Every dashboard you trust, every insight you act on — starts with their craft. 🧠 They work quietly but impact boldly. Powering leadership decisions, marketing precision, personalized products, and predictive planning — one SQL query at a time. 💻 Tools of the trade? Python, Airflow, Snowflake, Spark — but it’s not just about tech. It’s the strategic thinking, the clarity they bring, and the value they create. 🌟 As AI accelerates, fragmented data becomes the biggest blocker to clarity. Data Engineers aren't just relevant — they’re indispensable. At The Briminc Softech, we don’t just recognize this role — we amplify it. Whether you're building a high-performing data function or rearchitecting your data strategy, our solutions elevate engineering talent, streamline infrastructure, and unlock cleaner, faster, smarter data-driven decisions. #DataEngineering #AIReady #DigitalTransformation #EngineeringExcellence #TheBriminc

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If your datasets are poorly labeled, you’re inviting a host of issues like noise, bias, and inconsistencies that can seriously hurt your model's accuracy and reliability when it’s out in the wild. That's why data labeling has evolved from being just a preliminary step into a vital part of engineering and operations in the AI lifecycle.

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Databricks engineers share how they scaled database reliability using big data analytics tools like Databricks, shifting from reactive monitoring to proactive scoring to enforce best practices!

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The End of Manual Modeling For decades, data modeling was manual by design — a slow, meticulous craft that required expertise and patience. But AI analytics moves too fast for that. Today, data models must evolve in real time. Schema changes, new domains, and user-driven queries arrive faster than humans can reconcile definitions. The solution isn’t to replace modelers; it’s to augment them. AtScale’s AI-powered semantic modeling brings intelligence to the modeling process itself, learning from metadata, lineage, and query patterns to suggest and refine logic at scale. When your business changes daily, your models should too — without breaking trust or governance. Read more from our CTO David P. Mariani as he breaks down how AI is transforming semantic modeling in his latest blog:

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Dynamically generated, Ontology-powered fleets of agents. At DevCon 5, Palantir Product Manager Ben Radford walks through the architecture of AI Hivemind — covering the capabilities and tradecraft that enable the creation, orchestration, and optimization of specialized agent teams.

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Palantir Technologies recently released a new Foundry update that lets you build directly on your source tables, removing the need for manual syncing. As Ontologize’s Ben Thomas explains, most teams used to have to move data into Foundry before they could start modeling. With this update, you can create ontology object types backed by virtual or iceberg tables, so your data stays where it already lives, whether that’s in Snowflake, S3, or another external system. It’s a small change that removes hours of setup and makes it easier to model directly on your source systems. Try it out, and let us know what you think!  

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Snowflake Intelligence: From Data to Decisions - Atrium's AI: AI projects fail when data quality is poor and siloed. At its core, the Snowflake platform solves this by giving access to unified insights with ...

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Analytics Governance isn’t optional in the Public Sector. It’s urgent. From misconfigured dashboards to manual QA bottlenecks, public agencies face mounting pressure to deliver trustworthy data analytics. In a world where data drives decisions and public trust, governance at the Analytics layer is no longer a nice-to-have. 🔍 Discover the 4 key reasons why Analytics Governance must be part of every public agency’s strategy, and how Wiiisdom helps make it happen. 📖 Read the full article at https://lnkd.in/ehpjvPXD #AnalyticsGovernance #governance #decisionmaking #PublicSector

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