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Speed Up Your Data Flow for Business Results

Cloudera

The Olympics doesn’t reward slow times in swimming, track or any other clock-timed sport. Likewise, slow data speeds don’t win over customers or colleagues in the real-time business world. Actually, that metric has dropped to three seconds since that study was conducted a decade ago.

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StubHub’s Rockstar Summer Interns

StubHub

They are like our customers: they love concerts, festivals, sporting events, theater?—?basically Rudra Gandhi, Data Engineering intern, (San Jose State University, Mathematics and Computer Science Major): As a company, I thought that StubHub is an interactive platform for its audiences and accepts feedback very nicely.

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Extra Crunch roundup: How Duolingo became an edtech leader

TechCrunch

Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights. Luckily for us, Bill.com released a deck that provides a number of financial metrics relating to its purchase of Divvy.

Fintech 246
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Use Amazon Titan models for image generation, editing, and searching

AWS Machine Learning - AI

Outside of work, he enjoys traveling and playing any kind of sport, especially soccer. AI/ML Solutions Architect at AWS, focusing on generative AI and applies his knowledge in data science and machine learning to provide practical, cloud-based business solutions. Dr. Baichuan Sun, is currently serving as a Sr.

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Organise your engineering teams around the work by reteaming

Abhishek Tiwari

For instance, if you are fast-growing VC funded e-commerce startup and your number one business priority is multiplying current growth and performing exceptionally well on key financial metrics charted out by your investors. Thirdly, let engineers themselves choose the delivery teams and organise them around the initiative.

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Analytics Maturity Model: Levels, Technologies, and Applications

Altexsoft

Sometimes, a data or business analyst is employed to interpret available data, or a part-time data engineer is involved to manage the data architecture and customize the purchased software. At this stage, data is siloed, not accessible for most employees, and decisions are mostly not data-driven.

Analytics 102