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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.

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

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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.

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.

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.

API pagination helps manage large datasets by breaking them into smaller parts.

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

KMS securely manages encryption keys to protect data from theft.

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

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

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.

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

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

Container data can be lost unless stored in persistent volumes.

Backup strategies include Full, Incremental, and Differential, each with unique benefits.

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

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

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

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

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

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

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

Kafka uses brokers for data handling and KRaft controllers for metadata management.

Elasticsearch ILM automates data management through defined lifecycle phases.