Apache Iceberg is revolutionizing data lake architectures by providing a modern, open-table format that decouples storage from compute, enabling true data democratization. By leveraging Iceberg as your primary data lake/lakehouse, you eliminate vendor lock-in and gain full control over your data, ensuring cost-effective, scalable, and flexible data management.

We’re excited to introduce the ability to manage automations in shared spaces, enabling collaboration and execution through the context of a shared space. Teams can create, organize, and manage automations in shared spaces. This setup makes it easy to control who can run, duplicate, and view, offering flexibility and security. 

We’re excited to announce our new Learn page—designed to help you get the most out of Qlik. Whether you're just starting or looking to deepen your expertise, our platform offers outcome-based learning paths that guide you every step of the way. And it is integrated right into your Qlik Cloud Analytics experience, so learning is seamless and always at your fingertips.

When organizations began using data lakes about a decade ago, many discovered a significant issue. Although the technology excelled at storing large volumes of raw data, it lacked the ability for business teams to access and consume the data easily. This blog focuses on the evolution of Podium Data into Qlik Talend Cloud. 

This blog explores three ways Qlik Talend Cloud enhances data quality for Databricks assets, helping customers maximize the value of data for their AI initiatives. 

As part of the continued evolution of Qlik Talend Cloud, we’re excited to introduce a powerful new feature to help accelerate and standardize API contract creation.

You can now use a built-in AI assistant in the graphical API designer to generate complex API contracts—simply by describing the desired behavior in natural language. This AI-driven creation method offers a faster, more intuitive way to build and iterate on your APIs, especially during early design phases.

This enhancement makes it easier than ever to go from ideation to implementation while ensuring consistency and best practices across your API landscape.

The principle of “Garbage In, Garbage Out” emphasizes a key truth: the quality of input directly determines the quality of output. As organizations aim to harness the value of vast volumes of data, managing data quality centrally becomes increasingly complex. Many organizations are discovering that decentralizing data quality to domain experts allows them to profile, validate, and curate data into reliable products, fostering greater business confidence and use.

As organizations continue to scale their data operations, modern architectures like Iceberg-based open lakehouses are emerging as the go-to solution for flexibility, performance, and cost efficiency. To support this evolution, Qlik Talend Cloud Pipelines introduces two new capabilities designed to simplify and enhance the process of building open lakehouses with Snowflake: Lake landing for Snowflake and support for Snowflake-managed Iceberg tables.

The upgrade to Java 17 and Apache Camel 4 marks a significant milestone for Qlik Talend users, ensuring alignment with modern technological standards while maintaining security and performance. This article provides an in-depth guide to the "why," "how," and "what" of this upgrade, integrating detailed insights and actionable steps to ensure a smooth transition.

In today’s data-driven world, organizations are racing to unlock value from their data. But there’s a catch: garbage in, garbage out. If the data fueling decisions is flawed, the outcomes will be too. Poor-quality data doesn’t just create inefficiencies—it erodes trust, stifles innovation, and hits the bottom line.