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Cassandra uses a masterless architecture to distribute and replicate data efficiently.

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

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

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

Choosing the right labels in time-series data is crucial to avoid inefficiencies.

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

Change Data Capture allows real-time syncing of database changes to other systems.

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

Data warehouses help businesses analyze large amounts of data for better decision-making.

CQRS separates how we change data from how we read data, improving scalability.

MongoDB uses replica sets for data redundancy and sharding for scalability.

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

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

OLTP handles transactions while OLAP is for data analysis and reporting.

Data lakes store large volumes of diverse data for analytics and processing.

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

Cache penetration occurs when requests for non-existent data overload the database.

Data Lakehouse architecture merges flexible data storage with reliable analytics capabilities.

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

Data residency is crucial for compliance with laws about where data can be stored.

Downsampling and aggregation reduce data volume while preserving essential trends.

Distributed caching spreads data across multiple servers for better performance and reliability.

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

Multi-region data consistency ensures that users in different locations see the same data.

Elasticsearch has different node roles that help manage data and scale clusters effectively.

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

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