Many enterprises run parallel data warehouses and data lakes with duplicated pipelines, inconsistent definitions of the same metric, and expensive synchronization jobs stitching the two together. The lakehouse pattern—open table formats providing warehouse-grade transactional guarantees directly on lake storage—removes the need to choose between structure and flexibility.
What migration actually requires
Moving to a lakehouse is not simply a storage change; it requires re-establishing data quality rules, access controls, and governance on the new platform before decommissioning legacy systems, not after. Metric definitions that drifted across teams over years should be reconciled during migration, since carrying forward inconsistent business logic defeats the purpose of consolidation.
Performance tuning matters as much as architecture: file compaction, partitioning strategy, and query engine selection determine whether the platform delivers on its promise of unifying analytics and machine learning workloads on one copy of data.
JIG guides data platform modernization programs from assessment through migration, focused on consolidating truth, not just consolidating storage.