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Elasticsearch rescoring improves search result rankings using a two-stage process.

Elasticsearch's function_score combines text relevance with business signals for better rankings.

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

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

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.

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

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