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

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

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

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Distributed locks ensure only one server performs a task, avoiding duplication.

Read-after-write consistency ensures users see their latest updates immediately.

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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

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

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

Failover ensures systems remain operational by managing leader changes during failures.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

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