Once you understand what Apache Iceberg is and the operational challenges involved (covered in the first blog of this series), the natural next step is choosing how to run Iceberg in your organisation. Although many vendors now support Iceberg, the choices fall into three clear categories:
Each approach comes with different costs, trade-offs and operational responsibilities. Below is a pragmatic comparison using real platform examples.
This is Part 2 of a two-part series. Part 1 explains what Apache Iceberg is and the operational challenges teams face in production.
This approach means your organisation builds and operates the Iceberg platform yourself using open-source tools and cloud components. Typical stacks include:
This is the most flexible approach, but also the highest engineering and operational burden.
In practice, this approach makes sense when total control is a higher priority than time to value or operational simplicity.
The next category includes platforms that support Iceberg and can simplify some aspects of the architecture, but which do not fully automate Iceberg optimisation. These are partially managed solutions: they reduce operational work, but do not eliminate it.
Two major examples:
These platforms provide:
However, they still require Iceberg-specific engineering effort for:
This approach suits organisations that want reduced operational burden without fully outsourcing Iceberg optimisation.
The final category is a platform specifically designed to manage Iceberg for you. Rather than providing a general-purpose data warehouse or Spark environment, these platforms sit directly on Iceberg and provide intelligent, automated optimisation.
Qlik Open Lakehouse is the clearest example of this model.
In practical terms, Qlik provides the optimisation logic that most teams struggle to build internally.
This is the best fit when the goal is to use Iceberg for value creation, not sink time into building platform foundations.
A clear way to frame the decision is by plotting two dimensions:
Self-managed / open-source
Semi-managed
Fully managed
Another helpful breakdown is:
Across industries, data leaders are facing rising cloud costs, increasingly complex data estates, and ambitious expectations around AI. Many organisations have already modernised once, typically by moving to cloud data warehouses or early lakehouse models, but are now encountering structural limitations around cost, flexibility and governance.
Against this backdrop, the open lakehouse, built on open standards such as Apache Iceberg, is emerging as a strategic priority for CIOs, CTOs and CDOs who need a simpler, more flexible, and more economically scalable foundation.
Most enterprise data platforms have evolved gradually over time, creating complexity that is now difficult to unwind. Common challenges include:
These issues were manageable when analytics was simpler. But with AI, self-service analytics and near-real-time insight now core to business strategy, the cracks are showing.
The modern data estate must be more open, more governed, and more adaptable than the architectures of the past decade.
At an executive level, the summary is straightforward:
An open lakehouse brings warehouse-style governance to low-cost object storage using open standards, especially Apache Iceberg, to ensure that all analytics and AI workloads can operate from the same governed tables, without duplicating data across tools.
Put simply: store data once, use it everywhere.
Apache Iceberg provides the table format that makes the open lakehouse model work across multiple tools and clouds. For business and technology leaders, the advantages consolidate into several clear strategic benefits.
Iceberg tables are stored in inexpensive cloud object storage, while still delivering warehouse-like structure. This eliminates redundant storage layers, reduces data duplication, and avoids locking data inside high-cost proprietary engines. When paired with intelligent optimisation (see earlier section), compute usage also drops significantly.
Iceberg is open and engine-neutral. The same tables can be accessed by Snowflake, Databricks, Spark, Trino, Qlik and emerging AI tooling, without conversion or duplication. This reduces dependence on any single vendor and creates freedom to adopt new capabilities as they mature.
Iceberg's snapshot model provides full historical traceability. Combined with a central catalogue, this enables consistent access control, lineage, quality checks and regulatory compliance across the estate.
AI initiatives rely on consistent, high-quality, well-governed data. Iceberg creates a unified data layer that supports analytics, feature engineering, vector search and model monitoring from the same tables ensuring AI workloads operate on trusted data.
With one shared data layer, teams no longer rebuild pipelines for each tool. They create reusable, governed data products that can serve BI, AI and operational analytics simultaneously. This improves delivery speed and frees talent to focus on high-value work rather than pipeline duplication.
By decoupling data storage from compute, Iceberg provides a stable foundation that can evolve as tools, vendors and AI paradigms change without replatforming.
Together, these benefits demonstrate why the open lakehouse is increasingly seen as a long-term strategic asset, not a tactical upgrade.
Most successful open lakehouse journeys follow a similar pattern:
The move to an open lakehouse is not about swapping one vendor for another. It is a structural response to long-standing challenges: fragmented data estates, duplicated storage, rising cloud costs, governance inconsistencies and barriers to AI enablement.
The open lakehouse, powered by Apache Iceberg, consolidates data into a single, governed foundation that scales economically, supports AI, reduces vendor dependence and accelerates delivery.
Iceberg makes this possible. Your chosen delivery model determines how quickly and easily your organisation can unlock the value.
For many data leaders, the open lakehouse is becoming the architectural backbone for the next decade of analytics and AI.
If you’re exploring Iceberg or modern lakehouse architectures, our experts can help you evaluate the right approach for your business. If you’d like to talk it through, schedule a call with our team.