Data Classification and Handling: Labeling, Encryption Tiers, Retention Policies, and DLP Patterns

Data Classification and Handling#

Data classification assigns sensitivity levels to data and maps those levels to specific handling requirements — who can access it, how it is encrypted, where it can be stored, how long it is retained, and how it is disposed of. Without classification, every piece of data gets the same (usually insufficient) protection, or security is applied inconsistently based on individual judgment.

Defining Classification Tiers#

Most organizations need four tiers. Fewer leads to overly broad categories. More leads to confusion about which tier applies.

Time-Series Database Selection and Operations

Time-Series Database Selection and Operations#

Time-series databases optimize for a specific access pattern: high-volume writes of timestamped data points, queries that aggregate over time ranges, and automatic expiration of old data. Choosing the right one depends on your data model, query patterns, retention requirements, and operational constraints.

When You Need a Time-Series Database#

A dedicated time-series database is justified when you have high write throughput (thousands to millions of data points per second), queries that are predominantly time-range aggregations, and data that has a defined retention period. Common use cases: infrastructure metrics, application performance monitoring, IoT sensor data, financial tick data, and log analytics.