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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

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

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

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

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

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

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

Elasticsearch offers two pagination methods: from & size for shallow pages and search_after for deep pages.

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

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

Tumbling windows are non-overlapping time segments, while hopping windows can overlap.