Zero-Copy Mirroring in Qlik Open Lakehouse

There’s a moment in every data team’s journey when a bill arrives with unexpected costs. It might be an egress charge you didn’t see coming, a query cost that spiralled when usage spiked, or the quiet realization that migrating your data to a different platform would take months of work and a significant budget. If you’ve been there, you know what vendor lock-in really costs, and it’s not just dollars.

Qlik launched Open Lakehouse as a new capability in Qlik Talend Cloud last year to combat vendor lock-in and to provide customers with the freedom to own and use their data on their terms. Since launch, Qlik has supported Amazon S3 as the main lakehouse destination, but interoperability has been a key pillar of what Open Lakehouse offers. As such we supported zero-copy mirroring to Snowflake and Amazon Redshift so customers could access a single copy of their data from their chosen data warehouse platforms without duplicating data or workloads.

This week, Qlik has added Databricks as a mirroring destination for Open Lakehouse so data teams working on AWS, Snowflake and now Databricks can benefit from the freedom that comes with adopting an open lakehouse architecture with Qlik.

The news that Qlik Open Lakehouse now supports Zero-Copy Mirroring to Databricks Unity Catalog is a signal worth paying attention to.

It confirms what forward-thinking data teams already know: the industry is converging on Apache Iceberg as the standard for open, interoperable data storage, and the ability to query data without moving it is fast becoming a core expectation, not a premium feature. That’s a genuinely good development for the data ecosystem, and it’s a direction Qlik has been moving in for some time. And we’re not stopping there. We have ambitious plans to add additional lakehouse destinations later this year so watch this space!

However, a single mirroring capability solves only part of the problem. True data freedom means more than being able to query your lakehouse from one warehouse. It means working with your data however and wherever you choose, without being penalized for your decisions.

That’s exactly what Qlik Open Lakehouse in Qlik Talend Cloud is built to deliver.

One copy. Every engine. Total control.

The principle at the heart of Qlik Open Lakehouse is straightforward: land your data once, in open Apache Iceberg format on your own cloud storage, and let every tool you rely on query it from there. No copies. No migrations. No re-ingestion. Your data stays where you put it, and you remain in control.

With the Mirror data task in Qlik Open Lakehouse, you can make that Iceberg data immediately accessible to your cloud data warehouses without ever moving it. Qlik automates the DDL statements needed to create external tables and views in your warehouse, so your data consumers can query data as if it were native to their environment. The warehouse reads from the data you manage; it doesn’t own it, replicate it, or charge you for storing an additional copy.

This matters because an external view is just that: a view. Your data lives in one place, Qlik continues to manage and optimize it, and your warehouse simply reads from it. You get high-performance queries and a single source of truth, without the cost and complexity of duplicating data across platforms.

The freedom to use more than one warehouse

Here’s where it gets particularly compelling: a single Qlik Open Lakehouse dataset can be mirrored to multiple cloud data warehouses simultaneously. Your analytics team in Amazon Redshift, your partners working in Snowflake, your data science workflows running elsewhere, maybe Databricks - all of them can query the same underlying Iceberg data without a single byte being copied between platforms.

Think about what that means in practice. You’re not choosing a warehouse. You’re choosing which warehouses work best for each use case, and you’re not paying to store the same data three times over.

The mirroring experience is tailored to each platform. Redshift mirrors refresh automatically with no scheduling or manual intervention needed. For Snowflake, you have the flexibility to choose between Qlik-managed refresh, which is ideal when you need downstream transformations and consistent cross-table metadata updates, or Snowflake-managed, a serverless option better suited to simpler consumption. And with the new Databricks mirror, Unity Catalog reads Iceberg metadata directly from the Open Lakehouse Catalog and the latest metadata is refreshed automatically with each query. Each mirror task can be configured independently, giving you fine-grained control over how and when your data refreshes across every environment it touches.

Lock-in is a strategy. So is freedom.

The rise of open lakehouse architectures isn’t just a technical trend, it’s a direct response to a very real business risk. When your data is locked into a proprietary format or a single platform’s ecosystem, every contract renewal is a negotiation you enter from a position of weakness. Every price increase is a cost you’re forced to absorb. And every new capability your team wants to explore is conditional on whether your incumbent vendor decides to build it or support it.

Apache Iceberg changes that calculation. Because your data is stored in an open format on your own cloud storage, you’re not beholden to any single query engine, platform, or vendor roadmap. You can query it with Amazon Athena, Snowflake, Redshift, Spark, or any engine that natively supports Iceberg, whether today or in the future.

Qlik Open Lakehouse builds on that foundation by managing your Iceberg storage independently: optimizing file layouts, handling schema evolution, and maintaining transactional consistency so your data is always performant and query-ready. You get the performance benefits of a managed lakehouse without trading away the openness that makes it valuable.

The milestone for Qlik Open Lakehouse adding support for Databricks mirroring is a reminder that the ecosystem is moving in this direction. The question isn’t whether open lakehouse will become the standard, it’s whether you’ll get there on your own terms or someone else’s.

Start from freedom

If you’re planning a lakehouse initiative or rethinking how you manage data across multiple warehouse platforms, Qlik Open Lakehouse gives you a way to do it without starting a new dependency. You can ingest data from hundreds of sources, store it in Apache Iceberg on Amazon S3, and make it accessible to any warehouse your teams rely on — all from a single control plane in Qlik Talend Cloud.

Your data. Your storage. Your rules.

When you build on open standards from the start, you’re not just avoiding lock-in. You’re preserving the freedom to make better decisions as your needs evolve, your team grows, and the technology landscape shifts around you. That kind of freedom doesn’t just save money. It creates options. And in data strategy, options are everything.

Learn more about Qlik Open Lakehouse →

Learn more about mirroring to cloud data warehouse destinations in Qlik Open Lakehouse