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

Transactions ensure database operations are reliable using ACID properties.

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

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

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.

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

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

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

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

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

HLD provides an overview of system architecture, while LLD details implementation specifics.

REST API design focuses on creating clean, efficient, and scalable web services.

Database indexing speeds up data retrieval by allowing quick lookups.

FAANG engineers use high-level design principles to create scalable systems.

Search engines collect, index, retrieve, and rank documents to provide relevant results.

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

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

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

Cache stampede occurs when many requests hit the database due to a cache miss.

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

Composite indexes speed up database queries that filter or sort by multiple columns.

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

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

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

Microservices and modular monoliths differ mainly in deployment and communication methods.

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

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

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

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