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

Database indexing speeds up data retrieval by allowing quick lookups.

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.

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

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

Structured logging helps in quickly identifying issues by organizing log data consistently.

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

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

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

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

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

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

Transactions ensure database operations are reliable using ACID properties.

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

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

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

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

Elasticsearch mapping defines how fields are indexed and searched, with dynamic and explicit options.