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

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

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

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

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

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

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

Transactions ensure database operations are reliable using ACID properties.

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

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

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

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

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

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

FAANG engineers use high-level design principles to create scalable systems.

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

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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

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

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

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

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

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

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

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

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

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