This webinar covers the migration patterns organizations actually use when moving data into a lakehouse architecture, with AIStor Tables as the target. Four approaches are examined: lift-and-shift, phased migration, strangler fig (dual-write), and hybrid, with practical guidance on when each applies and what it costs in time, budget, and risk. The session addresses the antipatterns that cause migrations to fail: migrating related entities out of sync, applying old schema designs without rethinking for Iceberg's compaction model, and validating with record counts instead of KPI-level output matching. A live Python demo migrates sample customer, order, and product data into AIStor Tables, demonstrating schema flattening, record validation, and business metric comparison between old and new systems before cutover.
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The strangler fig (dual-write) pattern is the most broadly applicable migration approach: it forks incoming data to both old and new systems simultaneously, allowing full validation of latency, SLAs, cost, and data accuracy before any cutover, with real projects typically running 6 to 18 months.
Migrating to AIStor Tables requires rethinking schema design for Iceberg's compaction and scan model; flattening nested structs and promoting high-frequency filter fields to first-class columns directly determines query performance in the new environment, and indexes from the old system do not transfer.
KPI-level validation is required to confirm a successful migration: matching record counts alone is insufficient, and key business metrics must match cent-for-cent between old and new systems across daily, weekly, and monthly reporting dimensions before cutover.
Data engineers, data architects, and platform leads planning a migration from an existing data warehouse, database, or data lake to an Iceberg-based lakehouse, particularly those evaluating AIStor Tables as the target platform.
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