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Transactions ensure database operations are reliable using ACID properties.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

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

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

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

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

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

DNS resolution translates a URL into an IP address for web access.

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

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

Authentication verifies who you are, while authorization determines what you can do.

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

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

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

An inverted index helps search engines quickly find documents containing specific terms.

Hinted Handoff allows a database to manage temporary replica failures by storing missed write operations.

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

Failover ensures systems remain operational by managing leader changes during failures.

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

Distributed locks ensure only one server performs a task, avoiding duplication.

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

Availability is about access; reliability is about correct performance.

Elasticsearch uses shards for data distribution and replicas for redundancy.