When your sales team is closing a deal, your operations manager is optimizing warehouse inventory, or your customer service rep is resolving a complaint, they can't afford to wait for the monthly analytics report. They need answers right now, in the tools they're already using, without jumping through hoops or waiting for the data team to get back to them with updates.

Slack will be the Waterloo of closed data.

In the fast-paced world of digital analytics, understanding your users isn't just about collecting data. It's about making sense of it in ways that drive real business decisions. Enter cohorts, a powerful tool that helps you segment users based on shared behaviors and characteristics. Whether you're a marketer trying to boost retention, a product manager analyzing user engagement, or a business owner seeking deeper insights, cohorts can transform how you view your audience.

At Confluent, our mission is to provide the world’s most secure and scalable data streaming platform. So we’re aware and planning for a future where the threat of a large-scale, cryptographically relevant quantum computer is able to break the public key cryptographic algorithms in use today. In fact, the Quantum-Safe Working Group of the Cloud Security Alliance set April 14, 2030, as the deadline by which companies should have their post-quantum infrastructure in place.

The appetite for real-time data continues to grow. Across industries, the ability to act on data as it arrives is increasingly central to how leading organizations compete, from IoT and fraud detection to event driven analytics and AI agent architectures. Streaming data is no longer a specialist workload. It is becoming a core requirement. I am excited to announce that streaming ingestion is generally available in Qlik Open Lakehouse, part of Qlik Talend Cloud.

Learn what reverse ETL is and how it enables operational analytics. Explore its process and applications within sales, marketing, and support.

Author: Adam Wolf Efficient resource allocation is a foundational requirement for scaling AI workloads, particularly as organizations move from isolated experiments to shared infrastructure supporting multiple teams, models, and environments. GPUs, CPUs, and high-performance storage are costly and finite, and without coordination, utilization often degrades as usage grows.