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

Container data can be lost unless stored in persistent volumes.

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

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

Password hashing with unique salts ensures security by creating different hashes for identical passwords.

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

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

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

Redis is a fast in-memory data store used for caching and low-latency applications.

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

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

KMS securely manages encryption keys to protect data from theft.

Session-based authentication uses cookies to remember users after login.

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

OAuth 2.0 lets apps access user data securely without passwords.

Cassandra uses partition keys to determine how and where to store data across nodes.

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

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

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

Stateless servers are easier to scale because they don't store user session data locally.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

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

Distributed caching spreads data across multiple servers for better performance and reliability.

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

Symmetric encryption uses one key, while asymmetric uses a pair of keys for security.

Caching helps applications respond faster by serving data from memory instead of hitting the database.

Replicas keep your system available, while snapshots allow for data recovery.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

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

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

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

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

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

Choosing the right distributed lock depends on your system's needs and existing tools.

Event Sourcing stores every action as an event to recreate the current state.

JWTs allow secure user authentication without needing session storage.

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

Structured logging helps in quickly identifying issues by organizing log data consistently.

Transactions ensure database operations are reliable using ACID properties.

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

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

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

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

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

Consistent hashing allows efficient addition and removal of nodes with minimal key movement.

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

API keys help identify clients and control their access to APIs securely.

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

HTTPS is a secure version of HTTP that encrypts data for safety.

Database indexing speeds up data retrieval by allowing quick lookups.

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