Michael Tarallo

Do you use any of the source datasets below? Congratulations, you're already connected to Overture Maps Foundation's GERS IDs. Last month, Overture quietly shipped BRIDGE FILES, a new type of data artifact created with every release. They're basically join tables, connecting each source dataset's ID to the Overture data elements they inform. Read the whole write up: https://lnkd.in/g72NqAmW

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Did your Qlik reloads suddenly stop picking up dashboard variable values? Qlik has made critical changes to how front-end variables interact with the back-end load script. The old method of relying on UI inputs automatically carrying over during a reload is no longer reliable. If your ETL process depends on user input from the dashboard, you need a new approach. In my latest video, I break down exactly what changed and demonstrate the modern, robust solution using Qlik Application Automation to bridge the gap. Watch the full walkthrough here: https://lnkd.in/dtwdqv8B #Qlik #QlikSense #QlikAutomation #DataEngineering #QlikCloud

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Claude, ChatGPT, Snowflake Intelligence, Cortex Analyst, Databricks Genie, Zenlytic, TextQL. Think about any AI agent you connect to your data. They all get questions from business users like: “What was the average number of daily active users in the last quarter?” These AI agent connects to Euno’s MCP server to retrieve metadata. That’s when Euno gives these agents the missing context they need to understand your data. ** Just a reminder that Euno is a metadata platform so we only access your metadata ** Euno runs a semantic search and looks for dashboards that use DAU (which is semantically equivalent to Daily Active Users). It filters for certified dashboards using the trust signals you’ve already set in Euno. In this example Euno finds two candidates: 1. DAU used in the User Engagement dashboards 2. DAU_NEW used in the Product Statistics dashboard Both dashboards are certified. But Euno also shows how often each has been used in the past 30 days, along with the owner or creator. With that information, the business user can confidently choose the right one and the AI agent can deliver the correct answer. The same applies to tools like Cursor. Let’s say you’re an engineer and ask: “Can you find me the Tableau dashboard for Customer Success?” Cursor will reach out to Euno’s AI assistant to get that BI context because Cursor is focused on dbt development and doesn’t have access to that layer. You can even go further and ask which dbt models power that dashboard and how those models are calculated. Euno feeds Cursor with that real-time context as you build. It’s not just for business users. It’s for developers too.

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🚨 𝗥𝗲𝗹𝗼𝗮𝗱 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗤𝗹𝗶𝗸 𝗖𝗹𝗼𝘂𝗱 Did you notice that Qlik recently added two powerful options to the Reloads API? 🔹 𝗥𝗲𝗹𝗼𝗮𝗱 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 Pass variable values with the reload request and use them right in your script. Great for parameterized reloads and flexible deployments. 🔹 𝗥𝗲𝗹𝗼𝗮𝗱 𝗪𝗲𝗶𝗴𝗵𝘁 Control when your reload runs in the queue by assigning it a weight (1–10). Higher values = higher priority. Both of these are already available in 𝗜𝗻𝗽𝗵𝗶𝗻𝗶𝘁𝘆 𝗙𝗼𝗿𝗺𝘀 inside your Qlik Cloud. Just choose reload type = Trigger task and start experimenting. 👉 Links to the Inphinity and Qlik technical docs will be in the comments.

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If you're trying to figure out Qlik Sense vs. Qlik Cloud, or just keep up with the latest plan structures (yes, even the Starter tier has nuances!), this guide is for you. We cut through the jargon to explain: ✅ How Qlik Sense and Qlik Cloud licensing actually work. ✅ What's included in each Qlik Cloud tier. ✅ How "data for analysis" impacts your bill. Stop guessing and start understanding. Get the clear breakdown here: https://lnkd.in/emU_5Pms #Qlik #QlikSense #QlikCloud #Licensing #DataAnalytics #BusinessIntelligence #NoMoreHeadScratching

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The next evolution of Qlik Open Lakehouse is here! It’s a major leap forward for real-time, open, AI-ready data architectures. Qlik's Vijay Raja Vijay Raja shares what’s now possible with our latest release on the Qlik blog: https://bit.ly/3M7F7DW

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🚀 HelpButton.qs 2.0 launched, with support for Qlik Sense Cloud! A few weeks ago qs-help-button was launched — a way to add a custom Help button to the Qlik Sense toolbar on client-managed QSEoW. Using the help button you can define custom menu items, opening any web page or submitting bug reports to a backend server - from within any client-managed Qlik Sense app. Today we level up significantly. Version 2.0 is here, and the project has a new name: HelpButton.qs. The big news is that there's now a proper Qlik Sense extension alongside the original HTML injection variants. 🌐 One extension, two platforms The new extension works on both Qlik Sense Cloud AND client-managed Qlik Sense — same features, same configuration, automatic platform detection. Drag it onto a sheet, configure it in the property panel, switch to analysis mode, and the help button appears in the toolbar. That's a big deal. The original HTML variants required editing server files and only worked on client-managed. The extension approach is standard Qlik Sense deployment — upload a zip, drag and drop, done. ✨ What's new in 2.0: 🧩 Qlik Sense Extension — a native extension built with Nebula.js, configurable via the property panel. Works on Cloud and client-managed. 🔗 Context-aware links — template fields ({{appId}}, {{sheetId}}, {{userId}}) in the extension, same as the HTML variants. 🐛 Bug report dialog — submit structured bug reports to any webhook, with auto-captured Qlik context. Now available in the extension too. 👁️ Invisible footprint — hide the extension's hover and context menus in analysis mode. Users see the toolbar button, not the grid cell. 🎨 Fully customizable — configure all colors and texts used in the extension. 🌍 Multi-language support — Predefined translations for English, Swedish, Danish, Norwegian, Finnish, German, French, Spanish, Polish. The original HTML injection variants (Basic and Bug Report) are still available for those who prefer the lightweight, no-extension approach on client-managed QSEoW. Same permissive MIT license. Open source. Free to use. 🆓 👇 Link to the release and more info in the comments. #Qlik #QlikSense #QlikCloud #SenseOps #PtarmiganLabs #HelpButtonQS #Butler 👋 I'm Göran, aka #mountaindude https://lnkd.in/daZxKnFJ 🤓 Creator of the Ctrl-Q, Butler and, Onboard.qs, HelpButton.qs and other open source tools for Qlik Sense. 🔗 Check out ptarmiganlabs.com for SenseOps related resources.

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Your Graph DataWindows could look a lot better. PowerBuilder's Graph DataWindows get the job done — but they haven't aged well visually. And replacing them usually means rewriting code. Ultimate Suite for PowerBuilder takes a different approach: → Convert existing Graph DataWindows into modern charts automatically → Build animated dashboards with drill-down — printable in PB reports → Configure charts visually with a painter, no graph coding Try it for 30 days: https://bit.ly/4sHlwdu #PowerBuilder #DataVisualization #LegacyModernization

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Ever needed to figure out which of your customers are coming back year after year in Qlik Sense? That exact question came up on Qlik Community, so I put together a step-by-step demo showing how to build an app that identifies repeat customers. 👉 Using alternate states, filters, and charts, you can create an intuitive and powerful tool that you can use to find repeat customers with just a couple of clicks. Here’s the walkthrough: https://lnkd.in/eK9fNa2Z #QlikSense #QlikCommunity #QlikTips #QlikDevelopment #RepeatCustomers

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Olu J. shows you how to setup a sync between Sharepoint and Snowflake using #Openflow in under 10 minutes! Then you can create a #CortexSearch service on those documents, combine that with #CortexAnalyst and a semantic view on your structured data, and you've got an amazing #SnowflakeIntelligence agent! https://lnkd.in/gGcycYxu

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What if your teams could integrate FHIR data without writing a single transformation rule? Healthcare interoperability shouldn’t feel like an endless maze of schemas, constraints, and validation errors. Yet for most organizations, that’s the reality. With ExfFHIR Data Mapping Engine it is less time wrestling with data and more time delivering better care, compliance, and outcomes. https://lnkd.in/gN-KuTvR

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Snowflake just released Iceberg V3 support into public preview today. A few highlights. Nanosecond timestamp precision – Iceberg tables can now match the same precision as native Snowflake tables. Row lineage metadata – better visibility into how individual rows evolve across updates and deletes. VARIANT support – yay 🙂 Deletion vectors – enabled by default in V3. Improves performance from reducing the overhead of managing many delete files. Can mean faster ingestion and transformation! Nice to see Iceberg tables continuing to close the gap with native tables in Snowflake.

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Three days ago I shared the Agentic Horizons demo — a fully autonomous AI podcast production team running on CognOS, defined entirely in YAML configuration. 55+ CRD resources, 5,200 lines, six agents, zero application code. https://lnkd.in/ePCbxxq9 Now I've built the second major workload. This one isn't entertainment — it's intelligence. The DIANA workload implements a complete counter-terrorism intelligence cycle — collection, processing, analysis, production, and dissemination — as a governed, multi-agent CognOS workload. It's the beginning of my application to NATO DIANA, the alliance's accelerator for dual-use emerging and disruptive technologies (https://lnkd.in/eAZB_DPe). For those new here: CognOS is a Cognitive Operating System — an infrastructure layer where AI agents, workflows, knowledge graphs, data pipelines, and governance policies are defined as declarative YAML resources. You describe what the system should do. CognOS handles how. The DIANA workload: 88 CRD resources across ~4,000 lines of YAML. 4 specialized agents. 5 workflows containing 24 tasks. 4 human-in-the-loop review gates. The agent team: - OSINT Collector — scans news, social media, and open sources for threat indicators - All-Source Analyst — fuses multi-INT intelligence using structured analytic techniques (Analysis of Competing Hypotheses) - Report Producer — generates finished intelligence products: threat assessments, SITREPs, target packages - Collection Manager — identifies intelligence gaps and tasks new collection The infrastructure: - 12 intelligence streams across 4 collection disciplines (OSINT, SIGINT, HUMINT, IMINT) - 17 entity schemas modeling the threat knowledge graph (persons, organizations, cells, locations, events, communications, networks, and more) - 9 data pipelines for entity extraction, signal processing, and multi-source fusion - 5 structured document templates (threat assessments, SITREPs, target packages, intelligence information reports, ACH evidence matrices) - 4 channels, 4 real-time alert triggers, and 6 analytical dashboard views - Admission control policies requiring human analyst approval before dissemination The demo produced three classified intelligence products: a SECRET-level threat assessment on a Western Balkans extremist network (threat level: HIGH), a daily SITREP, and a target package — all with source attribution, analytic confidence levels, and alternative hypotheses. What changes between podcast production and intelligence analysis is the declarative model. Every agent operates under strict policy. Every product passes through a human review gate. Every action is audited. The infrastructure enforces this — it's not up to the AI agent. That's the point of a Cognitive Operating System. The architecture doesn't change between workloads. The proof: Drop this into your favorite AI chat app and ask about it. https://lnkd.in/evnMWPv9 #AI #Agents #DefenseTech #NATO

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