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Transactions ensure database operations are reliable using ACID properties.

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

Secure API design requires multiple strategies to protect sensitive data and access.

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

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

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

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

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

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

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

REST API design focuses on creating clean, efficient, and scalable web services.

The CAP Theorem explains the trade-offs in distributed systems during network failures.

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

Read-after-write consistency ensures users see their latest updates immediately.

SQL injection is a security risk where attackers manipulate database queries via input fields.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

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

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

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

Availability is about access; reliability is about correct performance.

Fail Fast means stopping quickly on errors, while Fail Safe ensures safety in failures.

Disaster Recovery Testing verifies that your recovery plan works effectively in real situations.

Downsampling and aggregation reduce data volume while preserving essential trends.

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

Quorum-based replication determines how many database replicas must agree for reads and writes.

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

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

Zero Trust Architecture ensures that no user or device is trusted by default.

Point-in-Time Recovery allows databases to be restored to a specific moment before data loss.

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

Kafka can help ensure messages are processed once, but requires careful design.

Encryption at rest and in transit are essential for protecting data both stored and in motion.

Cache penetration occurs when requests for non-existent data overload the database.

Consistent hashing helps distribute data across servers with minimal movement.

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

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

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

Hashing is one-way for verification, while encryption is two-way for confidentiality.

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

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