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

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

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

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

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

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

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

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

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

ETL transforms data before loading, while ELT loads raw data and transforms it later.

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

Transactions ensure database operations are reliable using ACID properties.

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

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

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