The hidden infrastructure tax that costs data teams hours of compute time and countless frustration
Forget shiny object syndrome, Spark is thriving while Hadoop shuffles toward its legacy sunset in the machine learning revolution.
Debunking Google’s ‘everyone can code’ claims and revealing why domain expertise trumps AI automation in data science.
HSBC’s sobering analysis reveals OpenAI faces a $207B funding gap with no path to profitability by 2030, raising existential questions about the AI business model.
Breaking down performance, quantization strategies, and practical trade-offs between two titans of the open model landscape
vLLM’s official support for AMD’s Ryzen AI MAX 395 and AI 300 series transforms the local inference landscape, finally giving NVIDIA some real competition.
With YouTube’s treasure trove of data, Google should be leading the AI race. Instead, Gemini trails competitors. Here’s why data alone can’t buy AI supremacy.
Anthropic’s coding agent escapes cloud confinement with llama.cpp integration, reshaping local AI development.
As OpenAI plants ad code in ChatGPT’s Android beta, developers question whether cloud AI’s ‘free’ models are worth the cost.
Facing 2 million files in S3? Here’s how to avoid the performance pitfalls and metadata mistakes that kill large-scale processing jobs.
After two years and 28,000 customers, is Microsoft’s unified analytics platform ready for mission-critical workloads?
Moving beyond chat interfaces to real agentic AI deployments in data engineering.
