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

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

Container data can be lost unless stored in persistent volumes.

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

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

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

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

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

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

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

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

Database replication enhances data availability and read performance but has challenges like replication lag.

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

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

Database replication helps keep data available and allows systems to handle more read requests.

Stream Processing Architecture helps turn incoming events into real-time insights efficiently.

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

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

Choosing the right boundaries for microservices is crucial for effective architecture.

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

Consistency models determine what data reads in distributed systems can see.

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

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

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