Systems | Development | Analytics | API | Testing

The Future of AI in the Enterprise

As AI continues to rise in importance across all industries, the cost of implementation, readily available access to cloud computing, and practical business use cases make AI-powered offerings a competitive advantage for product managers, engineering, and data leaders. However, AI isn’t without its fair share of risks and challenges.

A Memory-centric Approach to System Strategy: 6 Takeaways from Supercomputing 2025

Artificial intelligence workloads are reshaping how memory is produced, priced, and prioritized. Not because the supply chain has fundamentally broken, but because manufacturers are making deliberate decisions about where to place capacity and capital. Wafer lines are being steered toward high-margin, long-term AI demand, not toward broad, undifferentiated expansion. HBM, advanced DRAM, and other AI-optimized memory now command the majority of investment and forward planning.

How to Use Databox MCP in Claude to Get Revenue Metrics

See the Databox Model Context Protocol (MCP) in action inside Claude. In this video, we demonstrate how to connect your business data to Claude AI to instantly audit your revenue metrics. Instead of navigating through multiple dashboards, we use the Databox MCP to: Stop guessing if your data is accurate. Start verifying it with Claude and Databox. About this series: This video is part of our "Chat with Your Data" series, where we explore the Databox MCP.

Supermetrics MCP vs. Databox MCP: Choosing Between Data Pipeline and Analytics Platform

If you’re evaluating MCP servers for your analytics stack, you’ve probably noticed that “MCP support” can mean very different things depending on the vendor. I’ve been working with both platforms, and the distinction matters more than most comparison articles let on. Supermetrics and Databox both offer MCP implementations, but they’re built for different jobs.

What is AI Analytics? A Complete Guide for 2026

Stop looking for an AI Analytics tool. Start looking for an analytics protocol. That advice sounds counterintuitive. Everyone’s searching for “the best AI analytics platform” or “which BI tool has the best AI.” But that framing misses what’s actually happening in the market, and why most AI analytics implementations fail to deliver on their promise.

Qlik for Snowflake - From Ingestion to Insights

In partnership with Snowflake, Qlik helps customers modernize data estates end to end—from ingestion to insight—using Qlik Talend Cloud Data Integration and an internal data marketplace. With no‑code/SQL pipelines, thousands of secure connectors, and built‑in governance and lineage, Qlik automates trusted data movement into Snowflake and turns it into certified, reusable data products—an “app store for insight”—that accelerate time to value and power advanced analytics and AI.

Why Every AI Deployment Needs a Pre-Flight Data Checklist

You’re in the cockpit of a small plane, cruising a few thousand feet in the air. Then, out of nowhere, the airspeed dips and an alarm rings out. The nose drops, and you're in a full-out stall by the time instinct kicks in. You pull back on the yoke, trying to steady the plane, stop the descent and patch things up midair. But that’s exactly the move that seals your fate, sending you into a deeper spiral.

Unifying Snowflake & Apache Iceberg in Logi Symphony via Simba's ODBC Driver

How do you connect Snowflake and Apache Iceberg to embedded analytics without adding complexity? In this video, we demonstrate:→ Setting up the Simba Snowflake ODBC driver via system DSN→ Why pushdown queries matter for performance→ Building governed, reusable metrics in Logi Symphony→ Delivering fast, interactive dashboards on live retail data The result: unified sales and inventory analytics without the ETL pipelines, Python scripts, and custom services that create support headaches.

Snowflake Build London Keynote

Tune into the BUILD London Keynote. Hear what’s new from Snowflake - Shared Workspaces and the GA release of Cortex Code to Snowflake Postgres, semantic view autopilot, and interactive workloads for low-latency analytics. See demos across Snowflake ML, including notebooks in Workspaces, model registry, and online inference, plus how Cortex Agents API and Snowflake Intelligence help teams build trusted agent apps. The keynote also covers Snowflake’s partnership with OpenAI and why governance stays central as AI moves from answers to action.