Data lifecycles

The data lifecycle typically includes the following stages:

• Data Creation: This is the initial stage where data comes into existence. It can be generated by users, applications, or through data collection methods.
• Data Storage: Once data is created, it needs to be stored. AWS offers a variety of storage solutions like Amazon S3 (Simple Storage Service) for object storage, Amazon EBS (Elastic Block Store) for block storage, Amazon RDS (Relational Database Service), and Amazon DynamoDB for database storage.
• Data Usage: In this stage, data is processed, accessed, or manipulated. This can involve querying a database, performing analytics, or using data within applications.
• Data Sharing: Data may need to be shared between users, applications, or services. This requires proper permissions and policies to ensure secure access.
• Data Archiving: When data is not frequently accessed but needs to be retained, it is archived. An example is Amazon S3 Glacier, which is designed for secure, long-term storage at lower costs.
• Data Destruction: The final stage is securely destroying or deleting data when it is no longer needed, ensuring compliance with data retention policies and regulations.

Lifecycle Management on AWS

AWS provides tools and services to manage each stage of the data lifecycle.

• AWS S3 Lifecycle Policies: Manage the lifecycle of objects in S3 by automatically transitioning them to lower-cost storage classes like S3 Infrequent Access (IA) or Glacier, or by purging objects past a certain age.
• Amazon EBS Snapshots: Periodically capture the state of your EBS volumes. These snapshots provide point-in-time recovery options and can also be used to instantiate new volumes.
• Amazon RDS Automated Backups: Enable automated backups for databases, which include transaction logs to facilitate point-in-time recovery.
• AWS Data Pipeline: Automate the movement and transformation of data between different AWS services and on-premises data sources.
• AWS Identity and Access Management (IAM): Control access to AWS services and resources, ensuring data sharing is secure and compliant with policies.

Example: Implementing Data Lifecycle Policies in Amazon S3

To illustrate a data lifecycle policy, let’s say your application stores user-uploaded documents in Amazon S3. The access patterns indicate that most documents are only accessed within the first 30 days, but regulatory requirements mandate that you retain documents for at least five years. Here’s an example of an S3 Lifecycle policy that transitions data accordingly:

{
"Rules": [
{
"ID": "Move to IA after 30 days",
"Filter": {
"Prefix": "documents/"
},
"Status": "Enabled",
"Transitions": [
{
"Days": 30,
"StorageClass": "STANDARD_IA"
}
]
},
{
"ID": "Archive to Glacier after 1 year",
"Filter": {
"Prefix": "documents/"
},
"Status": "Enabled",
"Transitions": [
{
"Days": 365,
"StorageClass": "GLACIER"
}
]
},
{
"ID": "Permanent deletion after 5 years",
"Filter": {
"Prefix": "documents/"
},
"Status": "Enabled",
"Expiration": {
"Days": 1825
}
}
]
}

Retention and Compliance

Data lifecycle considerations on AWS also involve understanding data retention and compliance. AWS provides tools to help with this:

• Amazon Glacier Vault Lock: Implement compliance controls using a lockable policy that enforces the retention periods for archives.
• AWS Backup: Centrally manage backups across AWS services, applying retention policies and ensuring compliance with audit requirements.

Conclusion

Understanding the data lifecycle is essential for designing efficient, cost-effective, and compliant systems on AWS. By utilizing the breadth of AWS services, solutions architects can ensure that data is appropriately handled throughout its entire lifecycle, benefiting from the reliability, scalability, and security that AWS platforms offer. Preparing for the AWS Certified Solutions Architect – Associate exam involves not only knowing the available AWS services but understanding how to apply them in real-world scenarios to manage the data lifecycle effectively.