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Transactions ensure database operations are reliable using ACID properties.

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

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

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

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

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

Functional requirements define what a system does, while non-functional requirements define how it performs.

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

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

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

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

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.

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

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