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

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

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

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.

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

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

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.

Transactions ensure database operations are reliable using ACID properties.

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

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

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

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

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

Stream Processing Architecture helps turn incoming events into real-time insights efficiently.

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

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

Choosing the right boundaries for microservices is crucial for effective architecture.

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.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

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

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