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How Hypebeast Reached 97% AI Adoption Without Fear or Layoffs
At Hypebeast, 97% of staff now use AI daily not out of fear, but choice. Director of Data & AI, Sami Rahman, reframed AI as a creative ally, not a threat. By focusing on practical wins, like speeding up research and cutting drudgery, he built trust and curiosity. Instead of pushing tools, he created demand through scarcity, measured impact rigorously, and deleted underused agents without sentiment. The result: adoption that stuck, creativity that flourished, and teams that saw AI as empowerment, not replacement. A playbook for leaders who want AI adoption to last built on trust, not hype.
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How DuckDB Cuts Development Costs Without Touching Production
Rising warehouse costs are pushing data teams to rethink how and where they run workloads. At our October LinkedIn Live, experts from Greybeam, Tasman Analytics, and Matatika unpacked how DuckDB helps teams cut unnecessary warehouse spend by shifting development, testing, and ad-hoc analysis to fast, local environments. The takeaway: DuckDB isn’t a warehouse replacement. It’s a cost-control companion. Successful teams use hybrid execution to pair local speed with cloud scale, measure true unit costs, and build flexible, future-proof stacks. With Matatika’s Mirror Mode, teams can validate savings before committing, achieving sustainable efficiency without disrupting production.
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How OLTP and OLAP Databases Differ and Why It Matters for Your Data Architecture
Most data teams misuse OLTP and OLAP systems by forcing mismatched workloads, leading to bottlenecks, high costs, and missed opportunities. Smart teams separate environments, optimise data flow with incremental syncing, and use safe migration tools like Mirror Mode to achieve both transactional efficiency and analytical power without disruption.
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How to Optimise OLAP and OLTP Systems for Better Performance
Most data teams struggle because inefficient architectures force them to choose between fast transactions (OLTP) and powerful analytics (OLAP), creating delays, high costs, and frustrated users. Smart teams separate systems by purpose, use efficient syncing like Change Data Capture, and adopt performance-based pricing to achieve real-time insights, cost savings, and scalable architectures without disruption.
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The FinOps Skills Every Data Engineer Needs in 2025
In 2025, data engineers are expected not only to deliver robust pipelines but also to integrate FinOps principles, ensuring systems scale economically as well as technically. Those who master cost attribution, pricing model evaluation, and cost-conscious architecture design are becoming business-critical, as financial awareness now defines engineering success.
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