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

MongoDB uses sharding to distribute data across multiple servers for better scalability.

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.

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

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

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

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

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

Retention policies help manage how long data is kept in databases.

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

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

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

Elasticsearch aggregations help summarize data using metrics for calculations and buckets for grouping.

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

Time-based partitioning organizes time-series data into smaller, efficient segments for better querying.

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

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

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

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