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Dynamo-style databases are designed for high availability and fault tolerance in distributed systems.

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

Kafka Streams uses local state for quick data access and changelogs for recovery.

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

Choosing the right distributed lock depends on your system's needs and existing tools.

Event Sourcing stores every action as an event to recreate the current state.

Cassandra uses partition keys to determine how and where to store data across nodes.

Kafka uses state stores to remember previous events in stateful stream processing.

Time-series databases are designed to store and query time-stamped metrics efficiently.

Cassandra uses clustering keys to sort rows within a partition for efficient querying.

KMS securely manages encryption keys to protect data from theft.

CDC with Kafka allows real-time updates from databases to multiple services.

Consistent hashing allows efficient addition and removal of nodes with minimal key movement.

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

Consistent hashing helps distribute data across servers with minimal movement.

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

Container data can be lost unless stored in persistent volumes.

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

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

Hashing is one-way for verification, while encryption is two-way for confidentiality.

ZooKeeper uses ephemeral and sequential nodes for managing distributed services effectively.