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

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.

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

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

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

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

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

Transactions ensure database operations are reliable using ACID properties.

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

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

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

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

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

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

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

Elasticsearch ILM automates data management through defined lifecycle phases.