What are the best practices for maintaining a data warehouse archive?

In my own experience, I came across Lifecycle Management which in the context of Azure Storage, allows you to define policies and rules that specific data will adhere to. An example of this could be specifying that data that is of the blob type, that has not been modified in 30 days, moves from a low-cost access, high cost storage tier (hot) to a higher-cost access, lower cost storage tier (cold) as the file is being modified and accessed less frequently. This is super helpful, as it allows us to automate the storage and tier optimization of data, therefore saving on storage costs.

? Get defined a lifecycle for each entities in your data storage system and use for each stage a specific storage will store data: This could be hard because you define life time for entity coud use for several teams by some specific fields which can lead to various storage and data models!

Archived data warehouse data can be in different formats like compressed files, database dumps, CSV and Parquet and is moved to low cost cold storage tiers to reduce the cost of storage.

?This process will be based in how data modeling handle time for each entities: If data model can't handle well updating, creation and elimination operations; the process won't work well. Check your data modeling to support this fields and what strategies use like partitioning or index to improve performance and scalability.

Implement the data governance model to avoid access to unauthorized individuals and use Advanced Encryption Standard (AES) for storage and SSL or TLS for data transmission. Implement security measures like encryption, access control and user authentication.

??? ?? Part of Way of protect your archiving data is what backup strategy use to recovery because if well data is archiving by legal, operational or administrative reason, you must support it for specific usage cases, so you need to replicate it to avoid possible data corruption or lost. ??? Get defined a backup strategy for archiving data in wanted granularity!

Test and verify the inaccurate or irrelevant data in data warehouse data archives and any difficulties of data retrieval process from the data archives. Test the data archival process, data deletion process and data retrieval process so that we can reduce storage costs, minimize business outages, improve application performance and data compliance.

A key best practice is to have a data lifecycle management plan that defines retention policies based on business needs. For example, one time at work we set retention periods by data type - 5 years for financial transactions, 10 for customer data, etc. We would also periodically review the archive contents and purge unneeded data to reduce storage costs. Another good practice is to catalog the archive contents in a metadata store with rich descriptions so users can easily find the data they need. Overall having clear policies, managing costs, and making data discoverable help maintain a useful archive that meets business goals over time.

Identify the best archiving solution like Azure Archive Storage or Commvault Complete Backup & Recovery or Veritas to manage and monitor the archive data using data discovery, data visibility, data compliance and performance monitoring.

Regular Data Cleaning and Optimization: Periodic cleaning and optimization of the data warehouse archive are crucial. This includes purging outdated data and de-duplicating records to maintain efficiency and relevance. In my experience, this improves performance and reduces storage costs. Implementing Robust Backup and Recovery Plans: Ensuring reliable backup and disaster recovery plans are in place is essential for data integrity. Regular backups and testing of recovery procedures safeguard against data loss. Compliance with Data Governance Policies: Adhering to compliance standards is vital. Regular audits and updates to security protocols ensure that the archived data is protected and compliant with relevant regulations.