#Databricks Summit

Use DATA2 for a $450 off your ticket price. See you at Gartner Data & Analytics Summit 2026! #data #ai #datascience #gartner #dataanalytics

Big data initiatives require strategic planning and expertise

Is your AI strategy worth the electricity? In the latest Data & Drams episode, Craig Hayes MAICD shared a statistic that stopped me in my tracks. During a seminar he attended a few years ago it was shared that 6% of all power generated in the US was being consumed by AI queries. Even more staggering? A single query on GPT-3 utilised the same amount of electricity as charging your smartphone from 0% to 100%. Personally, I don’t know if using high-compute AI for simple tasks yet autonomous agents alone is an operational drain we have fully accounted for. More data centres are being built, but are we really looking at the cost of our environment? What are your thoughts? Check out the whole episode dropping tomorrow!

🚫 The biggest myth about AI in production: that the model is the hard part. The reality? Most AI projects fail before the model ever runs, when teams can’t securely access governed production data. At Gartner Data & Analytics Summit, we’ll show how teams are solving this without data copies, brittle pipelines, or governance gaps. 📍 Orlando | March 9–11 📌 Booth #329 🎤 Speaking Session: March 9 at 1:40pm AI has a production data problem. We solved it with Simba Intelligence. #EnterpriseData #AIInProduction #GovernedAI #EnterpriseAI #SimbaIntelligence #AIHallucinations #AITrust

Data engineering is often the most overlooked discipline, as it lays the groundwork that allows other fields to perform effectively.

if you were in a tech role stuck under 130,000 per year you’re probably working harder but not working smarter. If you’re interested in, not just getting your next job, but a better one, especially the data space, comment “DATA ENGINEER” below.

A data ecosystem is the complete infrastructure an organization uses to turn raw data into usable insights—the hardware, software, processes, and people needed to collect, store, process, and analyze data.  Think of it like a “data supply chain.” • Hardware: servers, laptops, edge devices, cloud infrastructure • Software: databases, BI tools, ETL/ELT tools, analytics platforms • Processes: governance, security, quality checks, documentation, change management • People: data engineers, analysts, scientists, product teams, and business owners Why it matters: AI and analytics don’t fail only because of the model. They fail when the ecosystem is chaotic—data lives in silos, definitions don’t match, pipelines break silently, or access is unclear. A healthy data ecosystem creates trustworthy data, faster decisions, and more reliable AI outcomes. #dataecosystem #dataanalytics #ai #cpmai #natework

Crafting data solutions to solve real world problems

Nashville set the tone! Senior data leaders came together to tackle the realities of analytics, AI readiness, and governance—through practical conversations, real insights, and meaningful connections. This forum set the foundation for a powerful data leadership community and it’s only growing. Didn’t make it to Nashville? Join us this summer in Austin, TX. https://hubs.la/Q03-CWbl0 📍 Austin, TX 📅 June 10-12, 2026 #QuartzNetwork #PoweredByQN #DataStrategy #AnalyticsLeadership #ExecutiveEvent

From data chaos to AI-powered clarity 🐼❄️ This is exactly what Winter Data & AI Meetup 2026 is about. Real use cases, real challenges, real solutions! Join us and see how teams work with Data & AI in practice. 👉 https://datazen.top/xh8o

This week's signal: Data Engineering and Lakehouse momentum, while AGI hype cools. The infrastructure layer is having its moment. Track these signals daily. Link in bio. #DataEngineering #Lakehouse #AITrends #DataStrategy #SQL

Amazing DAMA Q1 Meeting AI at Scale! Big thank you to everyone who joined our 2026 first chapter session. The questions and discussion were 🔥 𝐀𝐈 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐦𝐨𝐝𝐞𝐥𝐬, 𝐢𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬. Grateful to learn and grow with this data community! #DAMARMC #AIatScale #DataGovernance #Metadata #DataQuality