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

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

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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.

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

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.

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

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

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

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

B-Trees help databases find data quickly without scanning every row.

OLTP handles transactions while OLAP is for data analysis and reporting.

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.

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

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

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

Transactions ensure database operations are reliable using ACID properties.

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

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

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.

Choosing between single and multi-region architecture affects system resilience and cost.