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

Secure API design requires multiple strategies to protect sensitive data and access.

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

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

Retention policies help manage how long data is kept in databases.

Sharding splits databases into smaller parts to improve performance and scalability.

Session-based authentication uses cookies to remember users after login.

Choosing between a shared database and a database per service affects system design significantly.

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

The choice between SQL and NoSQL databases depends on your application's specific needs.

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

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

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

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

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

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

Time-series databases are designed to store and query time-stamped metrics efficiently.

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

Choosing the right distributed lock depends on your system's needs and existing tools.

Event Sourcing stores every action as an event to recreate the current state.

JWTs allow secure user authentication without needing session storage.

Transactions ensure database operations are reliable using ACID properties.

Time-based partitioning organizes time-series data into smaller, efficient segments for better querying.

Data warehouses help businesses analyze large amounts of data for better decision-making.

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

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

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

API keys help identify clients and control their access to APIs securely.