If you’re reading this, you’re probably managing a data estate that’s outgrown its original design. Maybe you’re supporting both a warehouse for BI reporting and a separate data lake for data science teams. Maybe your storage costs are climbing faster than your budget. Or maybe you’re struggling to apply consistent governance across fragmented systems while leadership asks when AI initiatives will finally ship.
The lakehouse offers a path forward. But timing matters. Migrating too early can create unnecessary complexity. Waiting too long means falling further behind on cost, agility, and competitive advantage.
Here’s how to know if it’s time.
You’re managing multiple analytical and data science workloads and struggling
If your organization runs both structured BI reporting and advanced analytics or AI projects, you’ve likely split your data estate. Financial analysts query the warehouse. Data scientists work in the data lake. ML engineers want both, but getting timely access requires multiple approvals, duplicated pipelines, and weeks of data engineering effort.
Consider a regional bank managing fraud detection. Transaction data lives in an enterprise data warehouse for regulatory reporting and compliance dashboards. Meanwhile, behavioral clickstream data, customer service interactions, and third-party risk signals sit in an Amazon S3 data lake where data scientists build ML models. When the fraud team needs to correlate real-time transaction patterns with historical behavioral signals for a new detection algorithm, it triggers a month-long project. All of this means new ETL pipelines that require data to be copied multiple times, leading to separate governance reviews and fragmented lineage tracking.
A different approach is necessary.
Iceberg as the default data lakehouse technology
An Iceberg data lakehouse built on Icehouse architecture collapses this complexity. With federated query capabilities through Trino and transactional table guarantees from Apache Iceberg, teams can query both structured warehouse data and semi-structured or unstructured data lake files through a single access point. Data federation and the lakehouse work together to provide governed access without forcing data centralization.
More importantly, using this approach, governance becomes unified rather than fragmented. Role-based access controls, column-level masking, and audit trails apply consistently whether data lives in the lakehouse, remains in the warehouse, or sits in a production database. This approach overcomes the limits of centralized data architectures while maintaining strong governance, particularly important for financial services organizations.
You’re spending too much on data warehousing costs
One of the most common pressures prompting a move to an Iceberg data lakehouse is rapidly escalating warehouse costs. As financial institutions collect new data types (trade surveillance logs, fraud detection models, customer interaction records, streaming market data), warehouse storage and compute pricing often escalate quickly. Traditional warehouses are overkill for “cold” or infrequently accessed data, especially when most historical data won’t be used in structured reporting.
An Iceberg data lakehouse architecture lets you store all types of data in file formats such as Parquet on cost-effective cloud object storage. Hot data requiring fast query performance can leverage Iceberg’s advanced indexing and caching. Meanwhile, massive historical datasets remain economically stored until needed, without sacrificing the ability to query them when required.
For instance, an insurance company might be archiving terabytes of claims-processing logs and policy-interaction data in a warehouse for occasional actuarial deep-dive analysis or regulatory lookback requirements. This multiplies costs unnecessarily. Offloading that data to an Icehouse-based lakehouse immediately reduces storage costs while preserving query access and governance controls. The data remains queryable via Trino when needed, but you’re no longer paying premium warehouse rates for rarely accessed archives.
You need robust governance across all data
As regulations tighten and expectations around data privacy, lineage, and access control grow, piecemeal governance capabilities are insufficient. Data warehouses typically excel at security and data governance. But applying similar controls in a separate data lake can be challenging and error-prone due to less mature tooling for lineage tracing, fine-grained access, and compliance.
Iceberg data lakehouses built using an Icehouse architecture inherit warehouse-style schema enforcement, auditing, and role-based access control. These capabilities are consistently applied, even to semi-structured and unstructured data. By building on open standards and modern open table formats like Apache Iceberg, organizations like financial institutions or healthcare providers can confidently expand analytics and AI initiatives without increasing regulatory risk or suffering from governance blind spots.
With Iceberg’s rich metadata layer and Trino’s governance capabilities, you can enforce data sovereignty requirements (keeping EU customer data within EU regions), apply column-level masking for PII, maintain complete data lineage for audit trails, and implement attribute-based access controls that follow the data wherever it’s queried. These three pillars of AI data architecture are especially important for data intelligence in financial services.
You’re hindered by vendor lock-in or closed ecosystems
Shifting data and workloads between the warehouse and the data lake in proprietary toolsets can result in expensive lock-in and limit ecosystem integration. Many organizations find their analytics ambitions stymied by closed data formats, proprietary storage, or dependence on a single analytics vendor.
Data lakehouse architectures built on Icehouse are, by design, more open. They leverage Apache Iceberg and integrate easily with a range of compute engines and tools for SQL analytics, data science, and machine learning. This flexibility is essential for organizations planning to future-proof investments, migrate cloud providers, or quickly adopt emerging technologies without being locked into proprietary formats or platforms.
The separation of storage and compute in an Icehouse architecture means you’re not locked into a single vendor’s pricing or roadmap. Your data in Apache Iceberg format can be queried by Trino today, Spark tomorrow, or new engines as they emerge, all while maintaining transactional consistency and governance.
You’re aiming to accelerate data-driven innovation
Organizations at the forefront of digital transformation need faster time-to-value from their data without technical roadblocks between ingestion, management, and insight. Data engineers need to iterate quickly without months-long data preparation cycles. Data scientists want to experiment with new models without waiting for data to be provisioned. Business analysts need production-ready datasets with reliable query performance. AI teams need governed access to fresh data without compliance delays.
If innovation velocity is a top priority, the Iceberg data lakehouse’s unified approach to analytics, real-time data processing, and AI removes friction that traditionally slows teams down. Instead of maintaining separate systems with incompatible interfaces, teams work from a single foundation with consistent tooling and governance.
Key questions to ask before moving to an open hybrid data lakehouse
Before you invest in a data lakehouse architecture, ensure you’ve addressed key readiness questions:
- Are your teams blocked by fragmented data access, waiting days or weeks for data provisioning that should take hours?
- Are you managing ballooning data warehouse costs for historical or infrequently accessed data that doesn’t need premium storage and compute?
- Do you need to enforce consistent governance, access controls, and data lineage across warehouse, data lake, and operational database systems?
- Is vendor lock-in or proprietary data formats limiting your ability to adopt new tools, migrate cloud providers, or experiment with emerging technologies?
- Are AI or advanced analytics workloads delayed because data isn’t accessible, governed, or fresh enough?
