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

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

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

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

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

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

Write-Ahead Log ensures data is safely recorded before changes are made.

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

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.

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

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

Database replication enhances data availability and read performance but has challenges like replication lag.

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

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

Disaster recovery ensures systems can restore services and data after major disruptions.

Container data can be lost unless stored in persistent volumes.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

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