Qlik simplifies the design, development, and automation of iceberg lakehouses. With native support for Apache Iceberg, it enables efficient management of structured and semi-structured data, reduces query times, and optimizes performance – while maintaining open architecture and a universal catalog
Iceberg Optimization
Qlik optimizes high-performance iceberg lakehouses with built-in partitioning, indexing, and schema evolution, reducing storage costs and boosting query speed with automation built for scale.
- Iceberg optimization for faster performance
- 50% reduction in storage costs
- 2.5x–5x improvement in query speed
Open & Flexible Architecture
Qlik provides complete flexibility, enabling management of enterprise-grade iceberg lakehouses using preferred tools and query engines. With support for Apache Iceberg and object storage, Qlik ensures compatibility without vendor lock-in.
- Integrates with major BI and ML stacks
- Dynamic indexing and schema evolution
- Lakehouse management with open formats
Real-Time Data Ingestion
Qlik supports real-time data ingestion from hundreds of sources, with low-latency and accurate replication. Built-in schema evolution keeps pipelines flowing smoothly, without bottlenecks, and ensures data is always analytics- and AI-ready.
- Keeps iceberg lakehouses continuously updated
- Supports hundreds of sources, including databases, SAP, SaaS, mainframes, S3, Kafka, and Kinesis
- Maintains schema evolution automatically
Open Lakehouse Across Platforms
Qlik Open Lakehouse allows you to seamlessly connect and query data across platforms, engines, and catalogs – eliminating duplication by mirroring iceberg tables directly into cloud data warehouses. With native integration across leading iceberg catalogs and support for top query engines, it keeps your entire enterprise architecture consistently in sync.
- Works with AWS Glue, Polaris, Snowflake Open Catalog
- Query-ready with Athena, Trino, Spark, and Snowflake
- Data warehouse mirroring without copying or complexity
Ready to get more from your data?
How Teams Solve Their Data Challenges
Cloud Architect
Cloud Architect
Data Engineer
Data Engineer
Platform Lead
Platform Lead
Boost Performance and Reduce Costs with Open Lakehouse
Let’s talk. Leave your details below
FAQs
Apache Iceberg, Delta Lake, and Hudi are open table formats designed for large-scale analytics. They differ in architecture, metadata handling, ecosystem support, and optimization methods, but all aim to improve reliability and performance for lakehouse environments.
Yes. Open Iceberg architectures allow the same datasets to be queried across multiple engines and platforms, including Snowflake, Databricks, Spark, Athena, Trino, and others.
Qlik helps automate and optimize lakehouse operations including ingestion, schema evolution, partitioning, indexing, and interoperability, while allowing organizations to manage infrastructure according to their preferred architecture and cloud strategy.
Apache Iceberg supports scalable, high-performance analytics with features such as schema evolution, partitioning, time travel, and open interoperability across query engines and platforms.
Yes. Qlik supports continuous real-time ingestion into Apache Iceberg environments from databases, SAP systems, SaaS applications, Kafka, cloud storage, and other sources.
Qlik optimizes Iceberg lakehouses through automated partitioning, indexing, and file management strategies that improve performance while reducing unnecessary storage overhead.