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Container data can be lost unless stored in persistent volumes.

Data residency is crucial for compliance with laws about where data can be stored.

Caching helps applications respond faster by serving data from memory instead of hitting the database.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Distributed caching spreads data across multiple servers for better performance and reliability.

Redis is a fast in-memory data store used for caching and low-latency applications.

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

Kafka uses retention to keep historical data and compaction to keep the latest state.

Database per Service ensures each microservice owns its data, reducing dependencies.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Cache penetration occurs when requests for non-existent data overload the database.

Choose Elasticsearch for complex text searches, but use databases for structured queries.

CDC with Kafka allows real-time updates from databases to multiple services.

API pagination helps manage large datasets by breaking them into smaller parts.

Point-in-Time Recovery allows databases to be restored to a specific moment before data loss.

Replicas keep your system available, while snapshots allow for data recovery.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Stateless servers are easier to scale because they don't store user session data locally.

Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Disaster recovery ensures systems can restore services and data after major disruptions.

Batch processing collects data to process later, while stream processing handles data in real-time.

Late-arriving events can be dropped, updated, or routed based on system needs.