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.
Building an Agentic SOC on a Stream
Modern Security Operations Centers (SOCs) are transitioning towards agentic architectures. Traditionally, SOCs relied on rigid, rule-based automation and high human effort. We built an Agentic SOC powered by AI agents that don't just follow rigid, hardcoded rules, but also autonomously investigate, reason, and act like human analysts.
Build Invoice Automation Workflow with AI Agents | Astera ReportMiner
See how Astera ReportMiner uses AI Agents to build invoice automation workflows without the time consuming manual configuration. Upload your invoice requirements, and the Agent builds the workflow for extraction, transformation, validation, and delivery to your preferred destination. It shows each step as it works, so your team can review and verify the process before deployment. Once approved, the workflow can be scheduled to run unattended.
Live Demo | Build a Reliable Data Foundation for Enterprise Analytics & AI
See how Astera turns data preparation and analytics into a conversational experience. Ask questions in plain language, prepare and analyze data, and turn the results into live dashboards without manually building every step. Astera brings structured and unstructured data into a governed data warehouse, then lets teams explore it through natural language. Every chart is traceable to its source, transformation steps, and underlying logic, so teams can validate how each answer was produced.
Simplify Data Integration | Do It Better with Infometry & Snowflake"
Discover how Informometry’s Snowflake-native applications streamline data integration, governance, and analytics. With no-code connectivity, replicate enterprise data from platforms like Netsuite and Salesforce directly into Snowflake. Eliminate complex ETL processes and reduce integration costs while keeping data secure.
Snow Report: What's Happening At Snowflake in September
The September Snow Report is LIVE Three GA launches, AI cost controls, and Expedition 2026 registration is open. What's new Per-User Quotas are GA Set daily or monthly spending limits per user for CoCo, CoWork, Cortex AI Functions, or warehouse compute — and Snowflake blocks access automatically when limits are hit. Deep Research in Snowflake CoWork is GA Multiple agents analyze your structured data, unstructured content, and external context together and deliver a fully cited report with recommendations in minutes.
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.
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.
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.
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.