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Two data challenges exist across almost all organizations: access and trust. These issues scale exponentially as an organization grows to the point that it can no longer hand around sheets of paper or approve database access. The demand for better data access drove the history of data warehousing, following the ethos that better decisions come from more data and that compute would catch up with demand. However, the hunger for collecting more data didn’t come without a cost.

A few months ago, at Qlik Connect, we announced the launch of Qlik Open Lakehouse, a new, fully managed capability that brings together the best of the Upsolver platform together with Qlik Talend Cloud. And today, we are excited to announce the general availability of Qlik Open Lakehouse within Qlik Talend Cloud that makes it easy and cost-effective for organizations to ingest, optimize, and manage massive amounts of data directly in Apache Iceberg–based lakehouses.

With growing data volumes, organizations are forced to rethink how they store and manage data. Traditional data warehouses, while powerful, became expensive and rigid when faced with the volume, variety, and velocity of modern data - leading to the rise of data lakes as a promising alternative. However, organizations soon found that data lake in themselves were not a panacea, and often provided limited utility due to their unstructured nature.

Let’s face it. Data lakehouses are the new normal, but that does not mean they are easy to use. Apache Iceberg gives you version control, schema evolution, and fine-grained partitioning. Trino lets you query it all with blazing speed. When it is time to plug that into your BI tools or analytics pipelines, things often grind to a halt. The problem is not your data or your engine. It is your connector. Architecting a data lakehouse is one thing. Getting it to actually perform is another.

Open lakehouses powered by Apache Iceberg are redefining the landscape of data management. By bringing together the scalability of data lakes with the performance and reliability of data warehouses, lakehouses give you unprecedented flexibility and control over your data, while embracing open standards.

You will need the following architectural decisions and migration process to make a data lake the center of your architecture.

Build an interoperable data architecture for analytics and AI using Fivetran Managed Data Lake Service and Google’s Cloud Storage.

Adopting a data lake in combination with the Fivetran Managed Data Lake Service reduces data ingest compute costs by 77% to 95% over traditional data warehouses.