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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 indexing speeds up data retrieval by allowing quick lookups.

Write-Ahead Log ensures data is safely recorded before changes are made.

Database isolation levels determine how transactions interact and affect data consistency.

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.

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

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.

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

Elasticsearch aggregations help analyze data by grouping, calculating, and estimating values.

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.