AI initiatives don’t stall because models aren’t good enough, but because data architecture lags the requirements of agentic systems.

Vector databases explained through speed vs velocity: why AI needs vectors, not rows and columns, to manage context, similarity, and next-gen RAG workloads, dqdeeptech ...

"Microsoft (US), Elastic (US), MongoDB (US), Google (US), AWS (US), Redis (US), Alibaba Cloud(US), DataStax (US), SingleStore (US), Pinecone (US), Zilliz (US), KX (US ...

Graphs are widely used to represent complex relationships in everyday applications such as social networks, bioinformatics, and recommendation ...

You’re investing too much to get the basics wrong. Here’s what architecture, infrastructure, and networking look like when ...

If your AI feels slow, expensive or risky, the problem isn’t the models — it’s the data, and cognitive data architecture is ...

With AI’s power demands intensifying, SSDs are primed to overtake HDDs as the default choice for maximizing performance, ...

Retrieval-augmented generation breaks at scale because organizations treat it like an LLM feature rather than a platform ...

It is crystal ball gazing time, and who better to talk about what technology trends will emerge next year than a senior leader from Dell?

1. Risk: AI Monoculture (Shared Blind Spots). This is the most critical and overlooked systemic vulnerability. Building your ...

It has become increasingly clear in 2025 that retrieval augmented generation (RAG) isn't enough to meet the growing data ...