Systems | Development | Analytics | API | Testing

Cloudera Open Data Lakehouse: Seamless Data Management and AI #Cloudera #AI #Tech #Shorts

Modern enterprises are currently overwhelmed by massive, fast-moving data in various formats that traditional legacy warehouses simply cannot manage. Cloudera addresses these complexities with its open data lakehouse powered by Apache Iceberg, providing a single, seamless, and optimized view of all your information.

Your Client's Growth Looks Good... But Is It Competitive?

Most agencies report on growth. But growth alone doesn’t answer the real question clients care about: Are we actually competitive? In this walkthrough, 42 Agency shows how they use the Databox MCP with Claude to benchmark client performance against relevant peer groups — filtered by size, revenue, and industry. Instead of relying on generic industry averages, they combine: The result? Stronger strategy conversations, clearer goal setting, and more confident planning grounded in a real market context.–

I Let AI Audit My LinkedIn Strategy (Here's what happened)

If you’re consistently posting on LinkedIn, the hard part isn’t getting data — it’s analyzing it. Most people review posts one by one, compare impressions manually, and try to “spot patterns” by eye. That’s slow. And it makes strategy reactive. In this walkthrough, Kamil Rextin, founder of 42 Agency, uses the Databox MCP with Claude to run a fast, AI-driven analysis of his LinkedIn performance — the kind of first-pass review you’d normally assign to a junior analyst.

Build a Data Input App with Kai

This is a Data App that collects structured product submissions from a team, validates them, queues them for approval, and writes approved entries directly to a Keboola table. I built it with Kai in one conversation. No Google Sheets. No broken column headers. No emailing CSVs. If you've ever needed your team to submit structured data - new products, budget inputs, campaign briefs, vendor details - and the spreadsheet approach keeps falling apart, keep reading.

Full Autonomy, Full Security: ClearML and SUSE k3k Bring Virtual Kubernetes Clusters to Enterprise AI

Kubernetes has become the de facto substrate for enterprise AI infrastructure. Its ability to handle complex, long-running workloads, self-healing capabilities, and rich ecosystem of GPU operators, storage drivers, and networking tools make it the natural platform for organizations scaling AI beyond the lab.

Introducing Native Spreadsheets in ThoughtSpot

Every analyst has been there: Deadline looming, data in hand, and a BI tool that either requires a workflow you haven't learned or a colleague you can't reach. So you open Excel. It's familiar, it's flexible, and it works right now. So that's where the work happens—and now where insight stays, ungoverned and invisible to your team, your analytics stack, and your agents.

From Chaos to Clarity: How Spotter Unifies Healthcare Data for Better Decisions

Most healthcare teams are making decisions from multiple different dashboards and systems that don't talk to each other, which means someone is manually stitching together the picture—one that's always slightly out of date by the time it's ready. Outdated or incomplete data can lead to fragmented patient care and experiences. And no health system wants that. Whether tracking enrollment targets or auditing claims denials, Spotter applies standardized clinical logic to your unified dataset so you can focus on what matters: the patient. Go from chaos to clarity.

Discovery Agent is Here! - Quick Explainer

Discovery Agent brings automated anomaly detection directly into Qlik Cloud. It monitors the metrics you care about, identifies meaningful changes, and delivers contextual insights right where you work. Today it’s a monitoring capability inside Qlik Cloud. Over time, it becomes one of the agentic services supporting autonomous workflows across the platform.

What CTOs Need to Know About Modern AI Storage

As organizations scale their AI initiatives from experimentation into production, CTOs face a pivotal architectural challenge as storage emerges as one of the most common—and most expensive—constraints. While organizations continue to invest aggressively in GPU compute, studies consistently show that infrastructure inefficiencies outside the GPU account for the majority of wasted AI spend.

The New Requirements for Mission-Critical Storage in an AI-Driven Enterprise

Most enterprises have made the commitment to AI. They’ve approved the budgets, stood up the pilots, and named it a strategic priority. So why are 95% of them getting zero return on $30–40 billion in GenAI investment? According to MIT research cited in Hitachi Vantara’s 2025 State of Data Infrastructure Global Report — which surveyed more than 1,200 IT leaders across 15 markets — the failure isn’t the model. It’s the infrastructure underneath it.