Iceberg Everywhere: Disaggregated Cloud Data Moves from Lakehouse Theory to Platform Default
Cloud data platforms are converging on disaggregated architectures and open table formats, with Iceberg emerging as the shared substrate for analytics, AI, and even observability data stored in...
CTOs are watching cloud bills climb while AI workloads push data gravity to uncomfortable places. A new pattern is forming across analytics, observability, and platform architecture: teams want the elasticity of cloud services without surrendering data custody or getting trapped in a single vendor’s storage format.
Snowflake’s recent messaging lands squarely on that fault line. The Snowflake and Google Cloud “open lakehouse” positioning uses Apache Iceberg as the interoperability layer, emphasizing AI readiness and reduced lock-in through open tables stored in cloud object storage (Snowflake, Iceberg Lakehouse). Snowflake also extended the idea beyond analytics by putting telemetry into Iceberg tables in a customer-owned S3 bucket, queryable by multiple engines (Snowflake, Observe on Iceberg). Observability data as open tables is a strategic tell: open formats are no longer a “data lake” concern, they are becoming the substrate for multiple operational datasets.
Cloud economics explains why the shift is accelerating. InfoQ’s coverage of disaggregated systems highlights the architectural move to decouple compute from storage to unlock elastic scaling and cost efficiency, especially for database and analytics systems built for cloud constraints (InfoQ, Disaggregated Systems). The same economic pressure shows up in adjacent platform updates, such as AWS Lambda allowing deployment packages to reference customer-owned S3, reducing a class of account-level quotas while keeping function limits intact (InfoQ, Lambda S3 code storage). Different domain, same theme: vendors are acknowledging that customer-controlled storage is becoming a default expectation.
The CTO implication is a reframing of “platform choice.” Data platform selection is shifting from picking a warehouse versus a lake to selecting an execution and governance layer on top of open data. Iceberg reduces switching costs, but it also increases the importance of operational excellence: catalog strategy, table maintenance, compaction, schema evolution discipline, and cross-engine compatibility testing start to matter as much as query speed.
Practical moves for engineering leaders:
- Treat Iceberg as a control plane decision, not a storage detail. Decide who owns table lifecycle (platform team versus app teams), and standardize on catalogs, naming, and retention policies early.
- Plan for multi-engine reality. Even if a single vendor runs most workloads, validate a second engine path for critical datasets (analytics, ML feature generation, and now telemetry) to keep exit options real.
- Unify observability and analytics governance. Telemetry in open tables creates opportunities for shared access controls, lineage, and cost attribution, but only if security and data governance teams align on policies across “ops data” and “business data.”
- Revisit cost models. Disaggregation shifts spend from bundled services to a mix of object storage, compute bursts, and background maintenance jobs. Finance-friendly unit economics (per product area, per dataset, per pipeline) becomes easier, and more necessary.
The next competitive battleground looks clear: open data foundations with differentiated execution, governance, and developer experience layered on top. CTOs who standardize on open tables while investing in the operational muscle to run them will keep leverage as vendors race to build the best engines.