Beyond centralization: Why APAC retail and CPG teams need dbt
Learn how dbt helps APAC retail and CPG teams test, govern, and trace their data before it drives critical inventory, promotion, and reordering decisions.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Ep 89 | Running with Scissors: The Enterprise AI Reality
Enterprise AI loves a shiny object. Sol Rashidi would rather talk about procurement. After years of leading AI deployments, Sol has learned that some of the biggest wins come from the decidedly unglamorous parts of the business. Her favorite function to transform? Procurement.
Small File Problem in Data Lakehouse - Apache Iceberg Compaction
Small files are a common problem in Lakehouse tables such as Apache Iceberg, particularly with streaming workloads that continuously commit data in small batches. In this episode, Dipankar explores how Apache Iceberg compaction helps address the small file problem.
Introducing SpotterCode in Developer Playground
Imagine handing a new developer an SDK, a stack of docs, and a deadline. They read for twenty minutes, write ten lines, break something, go back to the docs, second-guess a prop name, and try again. Now multiply that by every component they'll embed, every agent, every color scheme, and every project they'll touch. That gap, between "I know what I want this to look like" and "I know the exact syntax to make it happen," is the tax every developer pays on embedded analytics.
Qlik Script Editor Gets a Major Upgrade
Qlik Cloud just introduced a modern, IDE-grade editing experience for the Data Load Editor and Script Editor — now available in Public Preview. In this video, Michael Tarallo walks through what's new, including dark mode, improved syntax highlighting, code folding, smarter navigation, autocomplete, and code refactoring with Rename Symbol. Learn how to activate the Next-Gen Script Editor in your tenant and see how these updates make everyday Qlik scripting faster and more intuitive.
Why Your AI Strategy Needs Both Shared and Shared-Nothing Storage
As enterprises scale AI initiatives from the core data center to the edge, many organizations are thwarted by weak data practices and fragmented systems. The attempt to use a one-size-fits-all architectural approach for workloads that have fundamentally different needs is no longer a solution. If your AI ambitions are outgrowing your storage: the solution isn’t just adding more capacity, but matching the right architecture to the right stage of the AI lifecycle.
Rethinking Analytics For The Agentic Era
Databox is now an agentic analytics platform. AI that analyzes, reports, and acts on your performance, built on your data, your metrics, and your business context.
Stop your AI agent's $100,000 reordering mistake
See how Fivetran and dbt help an AI reordering agent make more reliable retail decisions. This end-to-end demo shows how trusted data supports smarter inventory decisions across the enterprise.
How to solve ecommerce analytics without manual reporting
Automate the way you use data to understand customers, predict demand, and optimize supply chains.
Cheap Tokens, Exploding Bills: The Hidden Cost of AI Agents
80% cheaper AI model. Nearly 2X the data warehouse bill. Switching to a cheaper AI model can cut your token costs by 80%, but it might accidentally double your public cloud data warehouse bill. In this video, we break down the "Agentic Cost Shift"—how autonomous AI agents generate hidden, high-compute SQL queries that drive up data lakehouse costs—and how to fix it using Cloudera. See where the real cost of AI is shifting.