I am currently in the process of migrating several hundred TB of data (millions of files), and while it’s taking a long time due to the file count, I have noticed duplicate data and a very unorganized structure. This is causing me issues, requiring manual verification, and I have also encountered incorrect permissions.
Here are a few tips when archiving your data for the long term to avoid these issues.
1) Implement Proper Naming Conventions
Consistent and meaningful naming conventions are essential for long-term data retrieval. Five, ten, or even twenty years from now, poorly named archives will create challenges. Establish, maintain, and adapt a structured naming system to prevent data from becoming unusable due to disorganization.
2) Use Compression to Optimize Storage and Performance
Uploading and managing millions of small files can be inefficient, expensive, and time-consuming. Compression helps mitigate this issue, especially when leveraging cloud storage or archiving to tape. However, consider the trade-offs—compressing large datasets into a single archive may make partial restores cumbersome. Evaluate your data structure and determine logical points to apply compression.
For example:
- Large file sets benefit from compression for better storage efficiency and faster transfers.
- If individual file access is frequent, avoid bundling everything into a single archive.
- Tools like RoboCopy and LTO tape systems handle larger files more efficiently than many smaller files.
- Different datasets require different approaches depending on how they are restored.
3) Plan for Long-Term Storage Costs and Growth
Understand the access patterns of your archived data. Factor in data retrieval frequency and growth projections over 5, 10, or even 20 years. Storage needs often grow exponentially, so early planning can help mitigate unexpected costs. When budgeting, consider:
- The savings from offloading data from production storage.
- The cost implications of long-term retention.
- The potential need for migrating data to different platforms in the future.
4) Avoid Over-Archiving
Archiving too much recent data can lead to frequent restore requests, defeating the purpose of archiving in the first place. Strategies to optimize archiving include:
- Sorting data by year to reduce restore frequency.
- Allowing users to move infrequently accessed files to a designated archive folder.
- Monitoring restore activity—if users frequently retrieve archived data, reconsider the timing or structure of future archives.
5) Plan for Data Retrieval and Exit Strategies
Cloud providers often make it easy to upload data, but retrieving large amounts of data can be costly. While storing data is one expense, other costs such as retrieval (reads), metadata operations (lists), and network transfer fees can quickly add up.
To mitigate risks:
- Maintain a secondary copy of critical archives on an alternate platform (e.g., Wasabi, tape storage) to reduce high retrieval costs.
- Understand cold storage limitations—retrieving petabytes of data from deep archive tiers could cost millions if not planned properly.
- Regularly review your cloud provider’s pricing models and policies for potential cost changes.
6) Test Restore Processes Regularly
Archived data is often overlooked until it's urgently needed. Testing restores periodically ensures that:
- The archived data is intact and retrievable.
- Restoration times align with business continuity objectives.
- Permissions and access controls remain correct after long-term storage.
Without testing, emergency restores can lead to unexpected delays, data integrity issues, or even complete failures due to unnoticed archiving mistakes.
7) Organize Data for Efficient Access
Sorting data into structured categories simplifies searches and retrievals. Consider:
- Creating a searchable index of archived data.
- Applying metadata tagging to enable quick identification of stored files.
- Implementing logical folder structures to separate different data types or retention periods.
A well-structured archive not only makes restores more efficient but also improves long-term data governance.
By following these best practices, you can ensure that your data archiving strategy remains scalable, cost-effective, and efficient. Have additional insights or strategies? Share them in the comments!
