James Conner

Data engineers can use Agent Bricks AI functions to modernize their data engineering practice by automating data extraction, summarization, and consolidation.

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🚨 AI success is impossible without strengthening data governance. CIO Dive highlights findings from the Actian 2025 State of Data Governance Maturity Report: ▪️Leaders overrate maturity by 12% ▪️90% of data professionals face scaling challenges ▪️Data quality, security, and compliance risks persist As our CTO Emma McGrattan put it: “AI acts as both a critical driver and a primary challenge for data governance.” The takeaway? Enterprises must address governance gaps now—or risk costly AI missteps later. Encouragingly, more than half of organizations are already prioritizing stronger data governance to improve AI initiatives, boost data quality, and increase trust in decisions. Full article🔗 https://bit.ly/41gaO2w #AI #DataGovernance #CIO

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🚀 Ready to elevate your AI/ML strategy? CData's Jerod Johnson joins industry experts to explore how data engineering can unlock the full power of intelligent applications. Learn how to reduce latency, scale pipelines, and enable real-time decision-making. 🎥 Watch the on-demand webinar hosted by Database Trends & Applications now: https://bit.ly/4esNOTg #DataEngineering #AI #ML #Analytics #RealTimeData #DataOps #CData #DBTA

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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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This foundational checklist from Augusto Rosa’s Snowflake Architecture Series is a must-read. Whether you're new to Snowflake or scaling your cloud data platform, this checklist breaks down the essential building blocks—like identity and access, cost governance, and data modeling—that every data team needs to get right. Set yourself up for long-term success with Snowflake—this is the perfect place to begin. https://lnkd.in/eYP8jZ2s

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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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Shell. Comcast. Nasdaq. Walmart. These companies didn't overhaul their data architecture on a whim. They did it because legacy systems became a bottleneck to growth. If your data team spends more time fixing pipelines than delivering insights, you're experiencing the same friction. The data lakehouse isn't emerging technology anymore. 70% of IT leaders expect most of their analytics will run on lakehouse architecture within three years 👇 Swipe through to understand what it is, why it matters now, and what the business case looks like for your organization.   #DataLakehouse #DataEngineering #DataArchitecture #BigData #CloudComputing #CTO #MLOps #Namasys

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AI's true power lies in breaking down data silos. Legacy systems can't scale to feed AI what it needs: massive, unified data streams. The future demands infrastructure that unifies your data ecosystem.

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