How Alibaba Cloud Handles Petabyte-Scale Data Lake Migration Without Downtime

In an era of information overload, massive amounts of data flow across the internet, from social media interactions to photos and shipments, forming a "data flood" that drives daily life and work.

Hundred PB‑Level Data Lake Cloud Migration Challenges

Traditional data sync only moves ODS data, but data‑lake migration involves full‑link integration with data warehouses, complex data and business validation, and overall IT system migration. When data scales to hundreds of petabytes, challenges intensify:

Massive, complex legacy data – Existing data can reach terabytes to hundreds of petabytes, with billions of tables and tens of thousands of scheduling tasks, many of which are non‑standard and lack maintenance.

Dual‑run business and heavy validation – Migration cannot pause business; both historical and incremental data must be synchronized while ensuring rapid, accurate consistency checks with a core data deviation rate below 0.1%.

Multi‑team coordination and high standardization – Numerous teams, especially business units, must follow standardized processes to avoid project delays and resolve inconsistencies quickly.

Hundred PB‑Level Data Lake Cloud Migration Practice

Alibaba Cloud Data Lake Formation (DLF) provides petabyte‑scale migration capabilities. It standardizes product functions to move hundreds of PB of historical data and several PB of incremental data, solves small‑file performance issues, maintains high bandwidth utilization, and ensures core data deviation under 0.1% with zero‑failure dual‑run validation.

1. Bulk and Incremental Migration

One‑time bulk migration of historical data; incremental data is auto‑detected and synchronized after full sync completes.

Metadata locations are automatically replaced without manual edits.

Optimizations reduce small‑file overhead and improve sync speed and bandwidth usage.

2. Dual‑Run Phase

The dual‑run involves S3, Alibaba Cloud, and the customer's custom scheduling platform. Over 50,000 tasks may exist, with dependency depths up to 53 layers. Validation must finish within 15 minutes per key dataset, keeping core data deviation below 0.1%.

Bidirectional Dual‑Run Strategy

Customer platform → DLF : After the customer’s task finishes, DLF runs, but upstream differences can amplify downstream.

DLF → Customer platform : DLF syncs first, then the customer platform runs, reducing inconsistency checks and improving parallelism.

Execution steps include hierarchical task ordering, multiple rounds of run‑validate‑fix, parallel validation, and up to 3‑4 days of processing depending on data volume.

3. Data Validation

DLF offers productized data comparison, automatic repair, and re‑run capabilities. Users can define table‑level validation templates, customize functions, precision, and tolerance to meet diverse business needs.

4. Cutover Phase

For non‑standard Spark‑jar jobs that retain original S3 paths after migration, JindoSDK automatically routes S3 paths to OSS without external proxies, minimizing risk and performance loss while supporting multiple cloud storage providers.

5. Migration Project Management

DLF provides a migration dashboard showing real‑time progress for tables and directories, task status distribution (not started, dual‑run, syncing, validating, stopped), migration metrics, and audit logs, enabling teams to monitor and resolve issues promptly.

Alibaba Cloud operates over 100,000 compute clusters globally, allowing the data lake to scale to millions of cores during peak loads. Its OpenLake solution integrates big data, search, and AI services to build an AI‑era data infrastructure.

Through these capabilities, Alibaba Cloud demonstrates how to manage petabyte‑scale data lake migration with high reliability, low latency, and minimal data deviation.