Data deletion and archiving are important aspects of data modeling that affect the performance, integrity, and compliance of your data systems. In this article, you will learn how to handle these processes in a systematic and efficient way, using some best practices and tools.
Before you delete or archive any data, you need to understand the data lifecycle and retention policies of your organization and your data sources. The data lifecycle describes the stages that data goes through from creation to disposal, such as collection, processing, analysis, storage, and usage. The data retention policy defines how long and where data should be kept, based on legal, regulatory, business, or technical requirements. You should document these aspects in your data model and follow them consistently.
This is the base of the any data deletion and archiving activities: based in functionality, resource usage, usability and legal reasons, each stage will define their spaces to store, duration in each of these space, mechanism to garantee the available of data, way to consuming and recovery time.
Handling data deletion and archiving in data modeling involves: Data Retention Policy: Define policies for how long data should be kept and criteria for deletion. Soft Deletion: Instead of deleting records, mark them as inactive or deleted using a flag. This allows for recovery if needed. Archiving: Move old or infrequently accessed data to separate storage, like data warehouses or archive tables, ensuring it's still accessible for historical analysis. Regular Cleanup: Implement scheduled jobs to archive and then delete data based on retention policies. Audit Trails: Maintain logs of all deletions and archiving actions for compliance and tracking purposes. Security: Ensure proper permissions and encryption to protect archived data.
Data has a lifecycle similar to a living organism, from creation to retirement. Key phases include creation and usage, where data is actively generated and utilized; aging, when its relevance declines; archiving, for long-term retention; and deletion, the permanent removal driven by legal or business needs. Understanding this lifecycle aids in informed decisions about data management.
Data deletion is the process of removing data from your data systems permanently or temporarily, depending on your needs. There are different methods of data deletion, such as hard delete, soft delete, and anonymization. Hard delete means that data is erased from the physical storage and cannot be recovered. Soft delete means that data is marked as deleted in the logical layer but remains in the physical layer until overwritten or purged. Anonymization means that data is modified to remove or mask any identifying information, such as names, addresses, or IDs. You should choose the appropriate method based on the type, sensitivity, and value of your data.
Depending on the need and data management policies, a data deletion method can be chosen. A soft delete is more preferred as the records won't get physically removed from the system. Periodical purging of soft delete would be recommended. The transactional systems should provide needed attributes supporting soft delete. Soft deletes support historical records maintenance.
Data deletion varies based on needs and data sensitivity. Hard deletion permanently erases data, making it unrecoverable. Soft deletion marks data as deleted but keeps it recoverable. Cryptographic erasure encrypts data and destroys the key, making it unreadable.
Data archiving is the process of moving data from your active data systems to a separate storage location for long-term preservation and future access. Data archiving can help you optimize the performance, cost, and security of your data systems by reducing the volume and complexity of your data. There are different strategies of data archiving, such as partitioning, compression, encryption, and deduplication. Partitioning means that data is divided into smaller segments based on criteria such as date, category, or status. Compression means that data is reduced in size by eliminating redundancy or using algorithms. Encryption means that data is protected by converting it into unreadable code. Deduplication means that data is eliminated if it is duplicated or redundant. You should choose the appropriate strategy based on the frequency, format, and purpose of your data.
Data archiving strategies differ based on data volume, retention needs, and access frequency. Options include on-premise archiving, where data is stored in the organization's data center; cloud archiving, which offers scalable and cost-effective solutions with remote servers; and hybrid archiving, combining both methods to optimize cost, performance, and security.
Data deletion and archiving can be done manually or automatically, depending on your data model and system capabilities. Manual deletion and archiving require executing commands or scripts to perform the desired actions on your data, while automatic deletion and archiving require configuring policies or rules that trigger the actions based on predefined conditions or schedules. Examples of tools that can help with data deletion and archiving include SQL Server (DELETE and TRUNCATE statements to delete data from tables, views, or indexes; BACKUP and RESTORE commands to archive data to files, tapes, or other devices; SQL Server Agent to schedule and automate these tasks), MongoDB (remove() and drop() methods to delete data from collections or databases; mongodump and mongorestore utilities to archive data to files or other databases; TTL (Time to Live) indexes to automatically delete data after a specified period of time), and AWS S3 (DELETE and PUT Object operations to delete and archive data from buckets or objects; S3 Lifecycle Management feature to automate the transition and expiration of your data based on rules and policies).
Various tools aid in data deletion and archiving, including Database Management Systems (DBMS) with built-in features, Data Lifecycle Management (DLM) software that automates tasks based on policies, and cloud-based archiving services offered by providers.
To handle data deletion and archiving efficiently, it is important to define clear and consistent criteria based on your data lifecycle and retention policy. This should be documented and communicated to stakeholders and users. Before applying the data deletion and archiving tools and processes to production data, it is essential to test and validate them. Additionally, monitoring and auditing activities should be done regularly, as well as reporting any issues or anomalies. Finally, data deletion and archiving policies must be reviewed and updated periodically to accommodate changing needs and regulations.
Implementing best practices for data management involves establishing clear policies, automating processes, maintaining audit trails, and securing archived data. This ensures efficient, compliant, and secure data deletion and archiving.
The GDPR impacts data deletion and archiving by granting individuals the right to erasure, requiring organizations to delete personal data upon request. It mandates that data retention be limited to necessary purposes and allows archiving to meet legal retention requirements.