SAP HANA Cloud Data Lake

SAP HANA Cloud's data-lake tier — extends the in-memory HANA database with **virtual tables that provide read-only access to Apache Iceberg data sitting in external object storage** (AWS S3, Azure Blob, ADLS Gen2, Google Cloud Storage). Pairs with **HANA Native Storage Extension (NSE)** to tier warm/cold data directly to S3-compatible endpoints without application refactoring. Lets SAP-shop analytical workloads query Iceberg-on-S3 lakehouses through standard HANA SQL, sharing identity, permissions, and observability with the rest of the SAP stack.

SAP shops have decades of investment in HANA's in-memory analytical engine, but the per-GB cost of HANA's primary tier makes large historical / archival data economically prohibitive — and the modern lakehouse pattern (Iceberg on S3) lives in a completely separate query stack. SAP HANA Cloud Data Lake closes that gap: keep the hot transactional state in HANA in-memory, push warm/cold + historical analytical data to S3-compatible object storage, query both as virtual tables from the same SQL session. The 2025-2026 evolution added direct Iceberg-on-S3 reads, which is the key 2026 lakehouse interoperability story for SAP customers.

SAP shops needing to query Iceberg-on-S3 lakehouses without moving data into HANA primary, tiering HANA warm/cold data to S3 via Native Storage Extension, historical/archival analytical queries where in-memory cost is prohibitive, integrating non-SAP data lakes (vendor S3 buckets, ADLS Gen2, GCS) into HANA-native analytical pipelines, and hybrid stacks where SAP Datasphere is the federation point.