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

Elasticsearch aggregations help analyze data by grouping, calculating, and estimating values.

Elasticsearch aggregations help summarize data using metrics for calculations and buckets for grouping.

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

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

Optimizing Elasticsearch involves using filters, caching, and measuring performance.

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

Elasticsearch mapping defines how fields are indexed and searched, with dynamic and explicit options.

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

Replicas keep your system available, while snapshots allow for data recovery.

Elasticsearch uses analyzers and synonyms to improve search accuracy by connecting similar words.

The number of Elasticsearch nodes depends on storage, workload, and performance needs.

Elasticsearch uses shards for data distribution and replicas for redundancy.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

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

Elasticsearch distributes search queries across shards to find and retrieve results quickly.

Elasticsearch has different node roles that help manage data and scale clusters effectively.

A document in Elasticsearch isn't searchable right after it's saved due to the refresh process.

Elasticsearch index templates automatically apply settings to new indices based on patterns.

Elasticsearch ranks search results using BM25 based on relevance scoring.

Elasticsearch documents aren't searchable immediately due to the indexing process.

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

Elasticsearch aliases allow zero-downtime switching between indices for applications.

You can update Elasticsearch indices without downtime using alias switching.

Elasticsearch uses master election and quorum to prevent split brain scenarios.

Filters check conditions without affecting relevance, while queries do.

Elasticsearch ILM automates data management through defined lifecycle phases.

Primary shards partition data, while replica shards duplicate it for redundancy.