Private Preview · Google Cloud Qlik Open Lakehouse comes to Google Cloud Sign up for the Private Preview and get hands-on with real-time Iceberg tables in your own Google Cloud project — no lock-in, no re-architecting. Sign up for the Private Preview! If you're running analytics on Google Cloud, you already know the tension. BigQuery is fast and familiar, but the more data you pour into it, the more your ingestion and storage bill climbs, and the more your data ends up shaped around one engine. You want the performance without the lock-in, you want your data to stay yours, and you want to free up some budget for more valuable workloads. That's exactly the problem Qlik Open Lakehouse solves — and we're getting ready to launch a Private Preview on Google Cloud this September that you can be part of. Qlik Open Lakehouse gives you a fully managed way to ingest real-time data straight into Apache Iceberg tables that live in your own Google Cloud Storage buckets. No proprietary formats, no copies, no re-architecting your data layer. Query those same tables from BigQuery, Trino, or any Iceberg-compatible engine, and pay a fraction of what you'd normally spend moving and storing that data. If you've followed our work on AWS, this will feel familiar — and that's the point. We're porting a proven architecture to Google Cloud, not building a science experiment. How it works: a walk through the architecture Open Lakehouse builds the bronze layer of your medallion architecture, ingesting and landing real-time data as optimized Apache Iceberg tables, with the heavy lifting executed in your own Google Cloud project. From there, push-down transformations in Qlik Talend® Cloud pipelines create your silver and gold layers directly on BigQuery. Here's the path your data takes, stage by stage. 1 · Ingestion at the edge Your sources land through two paths. For change data capture and bulk loads from databases, SAP, and mainframe systems, the Qlik Data Movement Gateway sits in your managed environment and handles CDC and bulk ingestion. For streaming and event data, you connect Kafka topics or Google Cloud Storage directly. Both routes feed the landing stage without your data ever leaving your control. Later in the Private Preview, we'll also add support for Qlik Replicate as a source, so if you're already a Replicate customer, you can onboard data from your existing CDC sources into Iceberg without migrating a single pipeline or switching tools. 2 · Compute in your project Two components run on Google Compute Engine inside your environment. The Network Integration Agent runs on a standard Compute Engine VM and brokers secure connectivity between the Qlik Talend Cloud tenant and your resources. The Open Lakehouse Cluster — the engine that writes and optimizes your Iceberg tables — runs on a Compute Engine managed instance group built on Spot VMs. That Spot-based design is deliberate: it's where a large part of the cost savings comes from, because you're running the ingestion and optimization workload on Google Cloud's most cost-efficient compute instead of always-on, premium-priced infrastructure. 3 · Open storage you own Everything lands in Google Cloud Storage, split across a landing bucket for incoming data, a config bucket for pipeline metadata, and table storage where your data comes to rest as Apache Iceberg tables. These buckets are in your account. The data is written in an open table format from the start, so there's never a moment where it's trapped in something only Qlik can read. 4 · A catalog any engine can use Iceberg tables are registered in the Google Lakehouse runtime catalog, so your tables are discoverable and governable through Google Cloud's native catalog rather than a Qlik-proprietary one. That's what makes zero-copy querying real: BigQuery reads the same tables through ELT, and so can Trino or any other Iceberg-compatible engine, all pointing at one physical copy of the data. 5 · A managed bronze layer, ready to build on The pipeline moves through managed stages — landing, storage, and mirror — to produce clean, governed Iceberg tables without you hand-coding each hop. You get Type 1 and Type 2 slowly changing dimensions or append-only patterns out of the box, so history and current-state tables are handled for you. We'll also add zero-copy mirroring to BigQuery during the Private Preview, so you can surface your Iceberg data straight in BigQuery. If you want to replace an existing native table, you can, since downstream queries keep working exactly as before. The result: real-time data flows from your sources into governed, query-ready Iceberg tables in your own Google Cloud Storage, optimized continuously, queryable from BigQuery and beyond — and you never gave up control of a single byte. Built on proven AWS foundations We didn't start from zero for Google Cloud. Qlik Open Lakehouse first shipped on AWS, where it's already running these same patterns: CDC and streaming ingestion, Spot-based compute for cost-efficient optimization, Iceberg tables in customer-owned object storage, and open querying across engines. Bringing it to Google Cloud is a deliberate step toward feature parity across clouds. The architecture mirrors what's proven on AWS, adapted to Google Cloud's building blocks: Compute Engine in place of EC2, Google Cloud Storage in place of S3, BigQuery and the Google Lakehouse runtime catalog as the native query and catalog layer. Same engine, same principles, GCP-native execution. This matters because of what it says about our direction. We believe you should be free to choose where your data lives, which engine queries it, and to change your mind later without a migration project. Delivering the same open lakehouse capability across AWS and Google Cloud, with more to come, is how we make that freedom real rather than a slide in a pitch deck. Why it matters Everything above maps to three things we care about: TRUST Build governed data products in Qlik Talend Cloud on top of your Iceberg tables. Your data stays in open Apache Iceberg format, in your own account. FREEDOM Query from BigQuery, Trino, or any Iceberg-compatible engine, and swap or add engines later without re-architecting. No lock-in to a single vendor or format. COST OPTIMIZATION Spot VM compute and zero-copy, single-storage architecture mean you spend far less to move and hold your data at scale. Join the Private Preview We're accepting participants now, and we're prioritizing organizations already standardized on Google Cloud and working with BigQuery or Apache Iceberg in their accounts. Request your spot in the Private Preview → Opt in through the form and the Qlik team will reach out to schedule a welcome call. Already signed up? Sit tight — we'll be in touch. ...View More

The World Cup is over, Spain are champions, and the third and final episode of our Model vs. Mastery podcast series has wrapped up with a verdict on the question we've been asking all along: when it comes to predicting football's biggest tournament, does the edge belong to the model or the human expert? 

Host Adam sat down with Steve Palmer and Nick Magnuson to close out the series, and the results were more decisive than anyone on the losing end expected. 

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What if data quality borrowed its playbook from DevOps? Software teams learned long ago that the cheapest bug to fix is the one you never let reach production. The same principle applies to data. Learn how shifting quality and governance left, combined with agentic data engineering and observability, helps stop bad data before it impacts dashboards, AI, and business decisions.

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For two decades, the job of a data engineer was deceptively simple to describe: move data from system A to system B so that someone downstream could analyze it. That era is ending. As agentic AI moves out of the lab and into core operations, a new and far more demanding consumer has arrived at the table — the autonomous agent. And agents are unforgiving about the data they are fed.

Welcome to agentic data engineering: the practice of both using and building agentic AI capabilities to deliver trusted data for AI workloads. It marks a shift from building pipelines by hand to orchestrating self-sustaining systems — and it changes what the modern data team is measured on.

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Everybody wants to rule their data world — and this release gets you closer. Twelve new Stitch-powered connectors land in Qlik Talend Cloud, from Circle CI to Shopify to Microsoft Teams. But the bigger news drops on 1 July: every Stitch-originated connector now travels the full journey — Replication, Pipelines, and Qlik Open Lakehouse. No more "Replication only." Read on.

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We are extending our agentic architecture with new analytics agents and capabilitiesthat deepen AI reasoning, broaden access, and drivemore effectiveaction. Thislaunchbuildson the agentic experience we introduced earlier this year andcontinuesour vision of Qlik asthetrusted intelligence layerfor AI, helping you movebeyond analytics consumption toward a system where AI reasons, predicts, acts, and assists—grounded in trusted data and powered by the Qlik analytics engine. Here iswhat’snew. Drive Action from Insight with Automate Agent With the new Automate Agent, you can now use natural language to trigger actions and workflows directly from your analytics experience. The agent executes workflows acrossQlikand downstream systems based on user requests or AI-driven recommendations. Itdeterminestheappropriate automation, prompts confirmation when needed, and executes tasks end-to-end. Insteadof stopping at an insight, you can moveimmediatelytoexecution,helpingyour teams respond faster and close the loop between analytics and outcomes.     SeeWhat’s Likelyto Happen with Predict Agent Our new Predict Agent lets you ask forward-looking questions in plain language and generate predictions without requiring data scienceexpertise. Built on Qlik Predict, the agentguides youthrough thefulldatasciencelifecyclefrom understanding the problem,buildingandvalidatingmodels, to generating predictions and interpreting results—with full explainability. Make it easier for more people to use predictive analytics to make proactive and better-informed decisions. Get Faster, Richer Answers from Qlik Answers We are introducingseveralnew improvements to your Qlik Answersthat improve speed, expand input types, and increase control over AI interpretation. Fast Mode: Get faster answersformore simple questions. Fastmodewilldeliverconcise, low-latency responsesfor quick exploringand faster-paced workflowswhile stillbenefitingfrom the accuracy and trust of theQlikanalyticsengine.   Multimodal Capability: Coming soon QlikAnswerswill be able toreadimages, charts, and documents. Byunderstandingand reasoningover visualcontent,Qlik Answers will unlock insights froma wider rangeofbusinessmaterials,reducingmanual effortofinterpretingnon-text data. Semantic Customization: Coming soon Providecontext foryour datato guide Qlik Answers. Customizesemantic information by addingdefinitions for fields and master items directly within the logical model,to give QlikAnswers abetterunderstanding of what your data means.Thiscreates a transparent and correctable processto review, edit,and enrich business definitionsand contextto prevent misinterpretations before they influence answers or impact users. Unlock powerful insight through MCP with newadvancedreasoningtools Coming soon Newengine-drivenadvanced reasoning MCPtoolsdelivermathematically rigorous analytics directly to AI agents and assistants. These capabilities extend analysis beyond basic querying and enable advanced decision intelligencedriven byQlik’s analytics engine. Analyze the Gray:Discover meaningful patterns hidden in excluded data and understand what may be missing from the current view Prove Causality:Estimate the causal effect of one variable on another and evaluate alternative scenarios OptimizeDecisions:Determinethe best allocation of resources under real-world constraints Simulate Risk:Explore thousands ofpossible outcomesand understand probabilities and confidence ranges Predict Outcomes:Train and deploy machine learning models directly against governed Qlik data Explore Alternatives:Re-run history under different conditions to understand the impact of actions not taken The next frontier in AIwon’tbedeterminedby which LLM you choose, but by howmuchanalyticalpoweryouhaveunder the hood.Thesecapabilitieswillenable assistants and agents to reason more effectivelyusing governed data and engine-driven analyticsthatdrive more informed, explainable, andactionabledecisions. The Next Step in Agentic Analytics With this June release, you can now go further with AI across every stage of decision-making.This helps transform analytics into trusted intelligence your teamscanrely on. Get started and learn more: From Intent to Trusted Data: Agentic Data Engineering is Now Generally Available|June 2026 Data Launch Innovation Blog What’s new in Qlik Cloud | Qlik Help Qlik Delivers Agentic Data Engineering in Qlik Cloud | Qlik Press Release Qlik Agentic Data Engineering is Generally Available. Here's What That Actually Means. | Qlik Blog ...View More

Qlik’s Agentic Data Engineering is now generally available in Qlik Talend Cloud, bringing AI assistance to the entire data engineering workflow and not just for code generation. There are five new capabilities, from declarative data pipelines where coding agents can use YAML to build data ingestion, to conversational catalog and glossary, data quality, and data product agents, that help data teams move faster from intent to trusted, AI-ready data. Best of all, these new features do so without trading away the governance, lineage, and quality controls that make data worth staking a decision on, keeping your team’s expertise firmly in the loop.

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The 2026 World Cup is in full swing, and so is our experiment in AI-powered football prediction. In Episode 2 of Model vs. Mastery, we've called halftime — and it's time to head to the dressing room, review the data, and ask the big question: how well can AI really predict the world's most unpredictable sport? 

 

Watch Episode 2 “Inside the Tournament” and create or update your bracket today!  

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Understand how Dynamic Engine and Dynamic Engine Environments help unlock scalability and efficiency with real time and scheduled data-integration workloads

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Nearly every enterprise is investing in AI. Very few are deploying it at scale. The difference isn't the model, the budget, or the use case. It's readiness. Organizations that succeed treat AI readiness as a core business capability built on trusted data, scalable architecture, skilled teams, and governance that keeps pace with innovation. Continue reading this article to learn more ...

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