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

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.

Transactions ensure database operations are reliable using ACID properties.

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

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

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

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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

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

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

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

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

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

Database replication enhances data availability and read performance but has challenges like replication lag.

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

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

Container data can be lost unless stored in persistent volumes.

Database replication helps keep data available and allows systems to handle more read requests.

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

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

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

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

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

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

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

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

CQRS separates how we change data from how we read data, improving scalability.

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

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

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

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

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

Cassandra uses clustering keys to sort rows within a partition for efficient querying.

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

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

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

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

Search engines collect, index, retrieve, and rank documents to provide relevant results.

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

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.

Cross-region database replication keeps databases synchronized across different geographical locations.

Availability is about access; reliability is about correct performance.

Functional requirements define what a system does, while non-functional requirements define how it performs.

Connection pooling reuses database connections but can cause issues if misconfigured.

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

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

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

KMS securely manages encryption keys to protect data from theft.

Composite indexes speed up database queries that filter or sort by multiple columns.