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Organizations are under pressure to feed data lakes and lakehouses with fresher data while keeping a tight lid on cloud spend. The problem is that most ingestion stacks weren’t designed for the real-time, high-volume workloads that power modern analytics and artificial intelligence (AI). They rely on layers of connectors, ETL jobs, and maintenance processes that quietly inflate both infrastructure and operational costs. Confluent’s Tableflow was built to change that equation.

The future depends on a unified data architecture that can support everything from analytics and reporting to AI agents.

Lakehouse governance requires observability, access control, data lifecycle management, and more. Fivetran MDLS makes it easy.

Learn what a data lakehouse is, its core architecture, and how it compares to data lakes and data warehouses to solve modern data challenges.

Why the catalog matters, how to configure it with popular query engines, and the essential best practices every data engineer should follow.

Earlier this year at Qlik Connect, we introduced Qlik Open Lakehouse, a fully managed, Apache Iceberg–based platform designed to make it easy and cost-effective for organizations to ingest, optimize, and manage data in open lakehouse architectures. And the first version of Qlik Open Lakehouse is generally available as of Sept 2025.

Real-time analytics has become a cornerstone of modern enterprises. Businesses are no longer satisfied with waiting hours or days for insights—they demand answers in seconds. The rise of AI, machine learning, and generative AI has only accelerated this need, putting immense pressure on data platforms to deliver reliable, scalable, and flexible architectures.