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

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

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

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

Cache eviction policies determine which data to remove when caches are full.

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

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

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

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

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

Late-arriving events can be dropped, updated, or routed based on system needs.

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

KMS securely manages encryption keys to protect data from theft.

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

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

Elasticsearch ILM automates data management through defined lifecycle phases.

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

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

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

RPO and RTO are key metrics for planning disaster recovery strategies.

Active-passive architecture ensures a standby system takes over if the main system fails.

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

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

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

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

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

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

Choosing between single and multi-region architecture affects system resilience and cost.

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

Kafka commit strategies determine how offsets are managed during message processing.

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

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

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

Cache stampede occurs when many requests hit the database due to a cache miss.

Cache invalidation is about knowing when to refresh cached data.

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

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

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

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

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.

Fallbacks allow systems to handle failures safely and maintain user experience.

Kafka consumer reprocessing involves safely replaying events to correct system states.

Kafka uses state stores to remember previous events in stateful stream processing.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

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

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

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

Kafka uses retries and Dead Letter Queues to manage message processing failures.

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

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

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

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

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