Copyright (c) 2025 MindMesh Academy. All rights reserved. This content is proprietary and may not be reproduced or distributed without permission.

2.1.3. Batch Data Ingestion (S3, Snowball, DataSync)

First Principle: Batch data ingestion fundamentally involves transferring large volumes of historical or periodically generated data to cloud storage, enabling cost-effective and scalable data processing for offline ML training.

For large volumes of historical data, or data that is generated periodically (e.g., daily logs, monthly reports), batch ingestion is the most efficient and cost-effective approach.

Key Concepts of Batch Data Ingestion:
  • Offline Data: Data that doesn't require immediate processing.
  • Large Volumes: Optimized for terabytes to petabytes of data.
  • Cost-Effective: Leverages services designed for bulk transfer, often cheaper than continuous streaming for large historical loads.
  • Scheduling: Transfers can be scheduled periodically.
AWS Services for Batch Data Ingestion:
  • Amazon S3 (Simple Storage Service): (Scalable object storage.) The primary destination for raw and processed batch data. Data can be uploaded directly via console, CLI, SDKs, or integrated with other services.
  • AWS DataSync: (Online data transfer service.)
    • What it is: A managed data transfer service that makes it simpler to automate moving data between on-premises storage (NFS, SMB, HDFS, S3-compatible storage) and AWS storage services (S3, EFS, FSx).
    • Benefits: Faster than manual copying, supports incremental transfers, handles encryption and integrity checks.
  • AWS Snow Family: (Offline data transfer devices.)
    • What it is: A family of physical devices for securely transferring very large amounts of data (petabytes to exabytes) into and out of AWS when network transfer is infeasible.
    • Devices:
      • Snowcone: Small, rugged, secure edge computing and data transfer device.
      • Snowball Edge: Data migration and edge computing device, comes in Storage Optimized and Compute Optimized versions.
      • Snowmobile: An exabyte-scale data transfer service that uses a 45-foot ruggedized shipping container.
    • Use Cases: Large-scale migrations, initial data seeding for data lakes, remote edge computing.
  • AWS Transfer Family: (Managed file transfer service.) For secure file transfer directly into and out of Amazon S3 or Amazon EFS using SFTP, FTPS, and FTP.

Scenario: Your company has 500 TB of historical sensor data stored on a Network Attached Storage (NAS) appliance in your on-premises data center. You need to move this data to an Amazon S3 data lake for a new ML training project.

Reflection Question: How do batch data ingestion services (e.g., AWS DataSync for large online transfers, AWS Snowball Edge for offline petabyte-scale transfers) fundamentally enable cost-effective and scalable transfer of large volumes of historical data to cloud storage for offline ML training?