Qlik MCP Server · Claude
Qlik and Claude: Trusted Analytics, Now Even Easier to Access
The Qlik MCP Server is now available directly in Claude's Anthropic Connectors Directory, making it easier than ever to bring your trusted Qlik data and analytics into Claude.
Good news for Qlik users! The Qlik MCP Server is now available directly in Claude's Anthropic Connectors Directory, making it easier than ever to bring your trusted Qlik data and analytics into Claude.
If you're already using Qlik Cloud, you can now connect Claude to your existing analytics environment to ask business questions, explore insights, and even create charts and sheets through natural language.
It's another way we're making Qlik's trusted analytics accessible wherever you choose to work with AI, giving you more flexibility to use the tools that work best for you.
Ask questions, explore insights, and build analytics
Imagine asking Claude:
“What's driving our margin decline this quarter?”
Claude can tap into Qlik's trusted data, business metrics, and analytical capabilities to help you find the answer, grounded in the same data and definitions your organization already uses.
Want to know which regions are contributing most? Ask a follow-up. Curious about the underlying trends? Keep exploring.
Build it in Qlik Cloud
You can ask Claude to create charts, visualizations, and entire sheets directly in Qlik Cloud.
Describe what you need, whether it's a regional performance comparison or a dashboard for your next team meeting, and Claude can build it in your Qlik environment.
From there, you can explore, refine, and share it just like any other Qlik content.
Your AI tools. Your trusted Qlik data.
As more AI tools become part of our daily workflows, we want to make it easier for you to use Qlik's capabilities with the tools you prefer, without losing the trust and governance you've already built into your analytics.
With Qlik and Claude, you get the best of both:
Trusted data
Work with data your organization has already prepared and validated.
Governed access
Your existing Qlik permissions and security controls continue to govern access to Qlik data and analytics.
Business context
Qlik's shared metrics, definitions, and analytical capabilities help Claude understand the meaning behind your numbers.
And because the connection uses Model Context Protocol (MCP), an open standard for connecting AI applications to external systems, it's part of a broader approach to making Qlik's analytics available across different AI experiences.
The idea is simple: you should have the freedom to choose how you work with AI, while continuing to get value from the Qlik data and analytics you already trust.
Ready to try it?
Find the Qlik MCP Server in Claude's Anthropic Connectors Directory, connect it to your Qlik Cloud environment, and start exploring.
Try asking a question about your business, follow the insights, and see what you can build.
And if you give it a try, we'd love to hear about your experience. What questions are you asking Claude, and what analytics are you creating in Qlik? Share your thoughts with the Community!
Your data. Your analytics. Your choice of AI.
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This week, Tesla needed nine years, ten SpaceX thrusters and about 600 liters of compressed air to get a Roadster off the ground. 🚗🚀 With Dynamic Engine 1.7, getting your first Talend job airborne takes one prerequisite less, and no FAA clearance!
We are excited to open the Public Preview of Qlik Talend Studio AI Assistant, the most significant addition to Qlik Talend Studio in years and the first step in bringing agentic assistance to the entire Studio development experience.
If you already build in Studio, this post walks through exactly what the assistant does today, component by component. If you are a Qlik customer who has never opened Studio, a quick orientation first.
Talking Heads sang "Once in a Lifetime" in 1980. Its central image - water flowing underground, endlessly, whether anyone's watching or not - was about sleepwalking through your own life while forces beneath the surface keep moving without you. It's also, unintentionally, a fair description of how enterprise data behaves: constantly flowing beneath the business, mostly unseen, doing its job whether a human ever looks at it or not. What's different isn't the flow. It's who's drinking from it. Qlik, SAP, and a cluster of vendors at API World have all recently opened the tap to a new consumer - AI agents -via a shared standard called MCP (Model Context Protocol). Same water. Different drinker.
For years, new Qlik Cloud capabilities have arrived the same way: finished. That sounds good until you notice what it means. We only hear what the broader user base thinks once something is generally available, which is the exact moment when changing it is most expensive.
Ask your coding agent a real question about your Qlik tenant and get a factual answer in real time. Qlik's MCP server now ships three new lookup tools purpose-built for declarative pipeline development, so Claude Code, Copilot, and Codex can check spaces, connections, and table structures against your live environment instead of guessing. No more tabbing out to the Qlik Cloud web app to hunt down a connection name or table ID, just faster, more accurate pipelines from the first prompt.
Qlik Talend Unlocked is a monthly series where engineers who have shipped this into production show you how. Each session takes on a real part of the modern Talend lifecycle, from CI/CD job promotion and test automation to migration, open lakehouse architectures, and real time integration. Practical approaches you can take straight back to your own environment, with results teams can measure.
Episode one is on demand now, episode two is open for registration. Register once and you are set for the whole series.
Something significant has shifted in the SAP ecosystem. SAP first prohibited ODP-RFC access for third-party tools back in February 2024. For over two years, that rule existed on paper without technical enforcement. In April 2026, that changed: SAP announced active enforcement was coming, and in June 2026, it arrived. Here is your plain-language explanation of what it means for Qlik customers, and where Qlik goes from here.
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.
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The World Cup is over, Spain are champions, and the third and final episode of ourModel vs. Masterypodcast 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.
