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

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

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

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

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

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

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

Database indexing speeds up data retrieval by allowing quick lookups.

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

B-Trees help databases find data quickly without scanning every row.

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

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

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

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

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

Choose Elasticsearch for complex text searches, but use databases for structured queries.

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

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

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

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

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

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.

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

Transactions ensure database operations are reliable using ACID properties.

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

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

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

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

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

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

Partition pruning helps databases skip unnecessary data scans, speeding up queries.

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

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

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

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

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

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

Batch processing collects data to process later, while stream processing handles data in real-time.

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

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

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

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

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