Cut Data Costs 70% and Free Your Engineering Team
Rising cloud costs, fragmented data environments, and the growing demands of AI workloads are pushing enterprises to rethink their data infrastructure from the ground up. This Nucleus Research analyst report, based on interviews with real Qlik Open Lakehouse customers, quantifies the operational and financial returns of adopting an open, Apache Iceberg-based lakehouse, and the numbers are compelling. If your organization is still managing legacy data warehouses or DIY Iceberg setups, this report shows exactly what you're leaving on the table.
Published
2025-12-12
6 Pages
Nucleus Research
Technology
AUTHOR
Alexander Wurm
PUBLISHED
2025
AUDIENCE
Data Engineers & IT Leaders
INDUSTRY
Technology (Data & Analytics)
TOPIC
Qlik Open Lakehouse, Cost & Performance ROI
White Paper Snapshot
Everything you need to know in under 30 seconds
Key Topics
Real Customer Numbers: 60–70% Lower Ingestion Costs
Ingestion processing costs dropped 60 to 70 percent across organizations interviewed by Nucleus Research, with the most significant gains coming from organizations migrating off legacy on-premises infrastructure or proprietary cloud data warehouses to Qlik Open Lakehouse on AWS spot instances.
Query compute costs fell 25 to 30 percent at up to 5x faster processing speeds: By supporting multiple analytics engines on a single consolidated dataset, Qlik Open Lakehouse lets organizations route workloads to the engine offering the best cost-performance ratio for each use case.
Storage costs declined 30 percent by replacing proprietary data warehouse storage with customer-controlled Amazon S3 in Apache Iceberg format while simultaneously satisfying compliance and data sovereignty requirements for regulated industries.
Tool consolidation reduced data integration and management spend by 20 to 30 percent: Replacing multiple point solutions with a single platform covering ingestion, transformation, governance, and optimization eliminates the overhead of managing disconnected toolchains.
Deployments completed in as little as two months, dramatically lowering the initial cost and complexity barrier that typically deters organizations from data platform modernization.
Highlights
Automation Frees Data Teams for Higher-Value Work
The Adaptive Iceberg Optimizer eliminates manual compaction, partitioning, cleanup, and catalog syncing, the time-consuming maintenance tasks that consume data engineering capacity without generating business value.
Data engineering teams saved 25 to 33 percent in IT headcount equivalent, with organizations reporting 40 to 50 percent time savings on common platform management workloads, freeing engineers to build models and analytics rather than maintain infrastructure.
Zero-copy mirroring enables Iceberg tables to be accessed in Snowflake or other data warehouses without duplicating data, preserving interoperability and eliminating the cost and governance risk of maintaining redundant data copies across environments.
A European energy provider went live in two months, achieving 60 to 70 percent ingestion performance improvements while enabling real-time ERP integration via CDC, eliminating batch schedules and network bottlenecks across a petroleum station network with fewer than 100 IT specialists.
Results & Impact
Up to 70% Lower Processing Costs
Customer interviews conducted by Nucleus Research confirmed 60 to 70 percent reductions in data ingestion processing costs and 25 to 30 percent lower query compute costs with processing speeds up to 5x faster than legacy architectures.
50% Less Platform Management Time
Automated Iceberg optimization through the Adaptive Iceberg Optimizer reduced platform management time by 30 to 50 percent, translating directly into headcount savings and allowing data teams to shift from maintenance to strategic development work.