chore: sync content to repo (#9415)

Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com>
2026-09-24 15:00:31 +08:00 · 2025-11-27 13:26:23 +01:00
parent 6742b17b47
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 # API Keys

-API keys in Elasticsearch provide a mechanism for authentication and authorization, allowing users or applications to securely access Elasticsearch APIs. They are a more granular alternative to using usernames and passwords, enabling you to restrict access to specific resources and actions. API keys can be configured with specific roles and privileges, limiting what a user or application can do within the Elasticsearch cluster.
+API keys in Elasticsearch provide a mechanism for authentication and authorization, allowing users or applications to securely access Elasticsearch APIs. They are a more granular alternative to using usernames and passwords, enabling you to restrict access to specific resources and actions. API keys can be configured with specific roles and privileges, limiting what a user or application can do within the Elasticsearch cluster.
+
+Visit the following resources to learn more:
+
+- [@official@Elasticsearch API keys](https://www.elastic.co/docs/deploy-manage/api-keys/elasticsearch-api-keys)
+- [@official@Create an API key](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-security-create-api-key)
+- [@article@Creating API Keys in Elasticsearch: An Advanced Guide](https://opster.com/guides/elasticsearch/security/api-keys-in-elasticsearch/)
@@ -1,3 +1,13 @@
 # Authentication

-Authentication is the process of verifying the identity of a user or system attempting to access a resource. It ensures that only authorized individuals or applications can gain entry by requiring them to prove who they are, typically through credentials like usernames and passwords, API keys, or certificates. This process confirms that the user or system is indeed who they claim to be before granting access.
+Authentication is the process of verifying the identity of a user or system attempting to access a resource. It ensures that only authorized individuals or applications can gain entry by requiring them to prove who they are, typically through credentials like usernames and passwords, API keys, or certificates. This process confirms that the user or system is indeed who they claim to be before granting access.
+
+Visit the following resources to learn more:
+
+- [@official@User authentication](https://www.elastic.co/docs/deploy-manage/users-roles/cluster-or-deployment-auth/user-authentication)
+- [@official@Authentication](https://www.elastic.co/docs/api/doc/elasticsearch/authentication)
+- [@official@Minimal security setup](https://www.elastic.co/docs/deploy-manage/security/set-up-minimal-security)
+- [@article@Elasticsearch Basic Authentication for Cluster (EN)](https://medium.com/@kaangorur/elasticsearch-basic-authentication-for-cluster-en-3728ba7acf8a)
+- [@article@Implementing Elasticsearch API Authentication for Enhanced Security](https://opster.com/guides/elasticsearch/security/elasticsearch-api-authentication/)
+- [@video@File-Based Realm User Authentication | Elasticsearch Self-Managed | Support Troubleshooting](https://www.youtube.com/watch?v=sueO7sz1buw)
+- [@video@Token Based Authentication Using API Keys to access Elasticsearch](https://www.youtube.com/watch?v=5vBa7AwfslE)
@@ -1,3 +1,11 @@
 # Autoscaling

-Autoscaling is the ability of a system to automatically adjust its resources (like compute, memory, or storage) based on the current demand. This means that the system can scale up (add more resources) when demand increases and scale down (remove resources) when demand decreases, all without manual intervention. This ensures optimal performance and cost efficiency by only using the resources that are actually needed.
+Autoscaling is the ability of a system to automatically adjust its resources (like compute, memory, or storage) based on the current demand. This means that the system can scale up (add more resources) when demand increases and scale down (remove resources) when demand decreases, all without manual intervention. This ensures optimal performance and cost efficiency by only using the resources that are actually needed.
+
+Visit the following resources to learn more:
+
+- [@official@Autoscaling](https://www.elastic.co/docs/deploy-manage/autoscaling)
+- [@official@Autoscaling example](https://www.elastic.co/guide/en/cloud-enterprise/3.7/ece-autoscaling-example.html)
+- [@article@Unlocking Elastic Scalability: A Comprehensive Guide to Enable Autoscaling in Elasticsearch](https://medium.com/@prosenjeet.saha88/unlocking-elastic-scalability-a-comprehensive-guide-to-enable-autoscaling-in-elasticsearch-ff6ab1000b65)
+- [@video@Autoscale your Elastic Cloud deployment](https://www.youtube.com/watch?v=kS-_uJMxotU&t=14s)
+- [@video@Autoscaling - Daily Elastic Byte S04E11](https://www.youtube.com/watch?v=g3_YddGpMrs&t=10s)
@@ -1,3 +1,14 @@
 # Avg, Sum, Min, and Max Aggregations

-These aggregations are fundamental tools for calculating statistical summaries of numerical data. They compute the average (Avg), total (Sum), smallest value (Min), and largest value (Max) respectively, across a set of documents that match a query. These aggregations operate on numeric fields within your Elasticsearch indices, providing insights into the distribution and range of your data.
+These aggregations are fundamental tools for calculating statistical summaries of numerical data. They compute the average (Avg), total (Sum), smallest value (Min), and largest value (Max) respectively, across a set of documents that match a query. These aggregations operate on numeric fields within your Elasticsearch indices, providing insights into the distribution and range of your data.
+
+Visit the following resources to learn more:
+
+- [@official@Aggregations](https://www.elastic.co/docs/explore-analyze/query-filter/aggregations)
+- [@official@Avg aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-metrics-avg-aggregation)
+- [@official@Sum aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-metrics-sum-aggregation)
+- [@official@Max aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-metrics-max-aggregation)
+- [@official@Min aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-metrics-min-aggregation)
+- [@article@A Basic Guide To Elasticsearch Aggregations](https://logz.io/blog/elasticsearch-aggregations/)
+- [@article@ElasticSearch Aggregation & Queries](https://medium.com/@souravchoudhary0306/elasticsearch-aggregation-queries-557131ef5ea4)
+- [@video@Learn about elastic search aggregation in 15 minutes](https://www.youtube.com/watch?v=ZziIEDfA8ZE)
@@ -1,3 +1,11 @@
 # BM25 Algorithm

-BM25 (Best Matching 25) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It's a bag-of-words retrieval function that scores documents based on the query terms appearing in each document, taking into account term frequency and document length. The algorithm adjusts for document length, preventing longer documents from being unfairly favored, and also considers how frequently a term appears in the entire collection of documents.
+BM25 (Best Matching 25) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It's a bag-of-words retrieval function that scores documents based on the query terms appearing in each document, taking into account term frequency and document length. The algorithm adjusts for document length, preventing longer documents from being unfairly favored, and also considers how frequently a term appears in the entire collection of documents.
+
+Visit the following resources to learn more:
+
+- [@official@Practical BM25 - Part 1: How Shards Affect Relevance Scoring in Elasticsearch](https://www.elastic.co/blog/practical-bm25-part-1-how-shards-affect-relevance-scoring-in-elasticsearch)
+- [@official@Practical BM25 — Part 2: The BM25 Algorithm and its variables](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables)
+- [@official@Practical BM25 - Part 3: Considerations for Picking b and k1 in Elasticsearch](https://www.elastic.co/blog/practical-bm25-part-3-considerations-for-picking-b-and-k1-in-elasticsearch)
+- [@official@Improved Text Scoring with BM25](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25)
+- [@article@Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25)
@@ -1,3 +1,10 @@
 # Boosting Queries

-Boosting queries in Elasticsearch allows you to influence the relevance score of documents based on specific criteria. It works by increasing or decreasing the score of documents that match certain query clauses, effectively prioritizing some results over others. This helps to fine-tune search results to better align with user intent and improve the overall precision of your search.
+Boosting queries in Elasticsearch allows you to influence the relevance score of documents based on specific criteria. It works by increasing or decreasing the score of documents that match certain query clauses, effectively prioritizing some results over others. This helps to fine-tune search results to better align with user intent and improve the overall precision of your search.
+
+Visit the following resources to learn more:
+
+- [@official@Boosting query](https://www.elastic.co/docs/reference/query-languages/query-dsl/query-dsl-boosting-query)
+- [@official@Relevance Tuning Guide, Weights and Boosts](https://www.elastic.co/guide/en/app-search/current/relevance-tuning-guide.html)
+- [@article@Elasticsearch Boosting Query](https://opster.com/guides/elasticsearch/search-apis/boosting-query/)
+- [@article@Elasticsearch Boosting Query - Syntax, Example, and Tips](https://pulse.support/kb/elasticsearch-boosting-query)
@@ -1,3 +1,10 @@
 # Cardinality Aggregation

-Cardinality aggregation is used to estimate the number of unique values in a field. It's particularly useful when you need to count distinct items but don't need the actual unique values themselves. This aggregation provides an approximate count, balancing accuracy with performance, especially when dealing with large datasets.
+Cardinality aggregation is used to estimate the number of unique values in a field. It's particularly useful when you need to count distinct items but don't need the actual unique values themselves. This aggregation provides an approximate count, balancing accuracy with performance, especially when dealing with large datasets.
+
+Visit the following resources to learn more:
+
+- [@official@Cardinality aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-metrics-cardinality-aggregation)
+- [@article@Elasticsearch Cardinality – Low + High Cardinality Fields](https://opster.com/guides/elasticsearch/data-architecture/elasticsearch-cardinality/)
+- [@article@Elasticsearch Cardinality Aggregation - Syntax, Example, and Tips](https://pulse.support/kb/elasticsearch-cardinality-aggregation)
+- [@video@Beginner’s Crash Course to Elastic Stack - Part 4: Aggregations](https://www.youtube.com/watch?v=iGKOdep1Iss&t=1184s)
@@ -1,3 +1,10 @@
 # CAT API

-The CAT API in Elasticsearch provides a simple, human-readable way to access cluster-level information using a command-line interface or a RESTful API. It returns data in a tabular format, making it easy to understand and interpret the status, health, and performance metrics of your Elasticsearch cluster. This API is primarily used for monitoring and troubleshooting purposes.
+The CAT API in Elasticsearch provides a simple, human-readable way to access cluster-level information using a command-line interface or a RESTful API. It returns data in a tabular format, making it easy to understand and interpret the status, health, and performance metrics of your Elasticsearch cluster. This API is primarily used for monitoring and troubleshooting purposes.
+
+Visit the following resources to learn more:
+
+- [@official@Compact and aligned text (CAT)](https://www.elastic.co/docs/api/doc/elasticsearch/group/endpoint-cat)
+- [@official@Get the cluster health status](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-cat-health)
+- [@article@Mastering the Elasticsearch Cat API for Efficient Cluster Management](https://opster.com/guides/elasticsearch/search-apis/elasticsearch-cat-api/)
+- [@article@Elasticsearch - Cat APIs](https://www.tutorialspoint.com/elasticsearch/elasticsearch_cat_apis.htm)
@@ -1,3 +1,9 @@
 # Cluster Monitoring

-Cluster monitoring involves continuously observing the health, performance, and resource utilization of an Elasticsearch cluster. This process helps identify potential issues, bottlenecks, and anomalies that could impact the cluster's stability and responsiveness. Effective monitoring allows administrators to proactively address problems, optimize resource allocation, and ensure the cluster operates efficiently.
+Cluster monitoring involves continuously observing the health, performance, and resource utilization of an Elasticsearch cluster. This process helps identify potential issues, bottlenecks, and anomalies that could impact the cluster's stability and responsiveness. Effective monitoring allows administrators to proactively address problems, optimize resource allocation, and ensure the cluster operates efficiently.
+
+Visit the following resources to learn more:
+
+- [@official@Monitoring](https://www.elastic.co/docs/deploy-manage/monitor)
+- [@official@Track what's happening in your Elastic Stack](https://www.elastic.co/elasticsearch/monitoring)
+- [@official@Stack monitoring](https://www.elastic.co/docs/deploy-manage/monitor/stack-monitoring)
@@ -1,3 +1,11 @@
 # Cross-Cluster Replication

-Cross-cluster replication (CCR) allows you to replicate indices and their data from one Elasticsearch cluster to another. This enables scenarios like disaster recovery, where a secondary cluster can take over if the primary fails, and data locality, where data is replicated closer to users in different geographic regions for faster access. CCR ensures data consistency across clusters, providing a reliable and efficient way to maintain data availability and resilience.
+Cross-cluster replication (CCR) allows you to replicate indices and their data from one Elasticsearch cluster to another. This enables scenarios like disaster recovery, where a secondary cluster can take over if the primary fails, and data locality, where data is replicated closer to users in different geographic regions for faster access. CCR ensures data consistency across clusters, providing a reliable and efficient way to maintain data availability and resilience.
+
+Visit the following resources to learn more:
+
+- [@official@Cross-cluster replication](https://www.elastic.co/docs/deploy-manage/tools/cross-cluster-replication)
+- [@official@Set up cross-cluster replication](https://www.elastic.co/docs/deploy-manage/tools/cross-cluster-replication/set-up-cross-cluster-replication)
+- [@official@Replicate Elasticsearch Data with Cross-Cluster Replication (CCR)](https://www.elastic.co/virtual-events/replicate-elasticsearch-data-cross-cluster-replication-ccr)
+- [@official@Follow the Leader: An Introduction to Cross-Cluster Replication in Elasticsearch](https://www.elastic.co/blog/follow-the-leader-an-introduction-to-cross-cluster-replication-in-elasticsearch)
+- [@video@Elasticsearch Cross-Cluster Replication (CCR)](https://www.youtube.com/watch?v=2Uwh-H_qazE)
@@ -1,3 +1,11 @@
 # Custom Analyzers

-Custom analyzers in Elasticsearch provide a way to define how text is processed both when indexing documents and when searching. They allow you to combine character filters, tokenizers, and token filters in a specific order to tailor the analysis process to your specific needs, such as handling language-specific nuances or removing unwanted characters. This customization ensures that your search results are more relevant and accurate.
+Custom analyzers in Elasticsearch provide a way to define how text is processed both when indexing documents and when searching. They allow you to combine character filters, tokenizers, and token filters in a specific order to tailor the analysis process to your specific needs, such as handling language-specific nuances or removing unwanted characters. This customization ensures that your search results are more relevant and accurate.
+
+Visit the following resources to learn more:
+
+- [@official@Create a custom analyzer](https://www.elastic.co/docs/manage-data/data-store/text-analysis/create-custom-analyzer)
+- [@official@Specify an analyzer](https://www.elastic.co/docs/manage-data/data-store/text-analysis/specify-an-analyzer)
+- [@article@Mastering Elasticsearch Custom Analyzers for Enhanced Search Capabilities](https://opster.com/guides/elasticsearch/data-architecture/elasticsearch-custom-analyzers/)
+- [@article@Custom analyzer building in Elasticsearch](https://medium.com/elasticsearch/custom-analyzer-building-in-elasticsearch-4e86f7c9c3be)
+- [@video@Elasticsearch Custom Analyzers - V1](https://www.youtube.com/watch?v=0ZuRExiHn1A)
@@ -1,3 +1,12 @@
 # Data Tiers

-Data tiers in Elasticsearch refer to the strategy of categorizing and storing data based on its access frequency and importance. This approach involves segregating data into different storage types (like hot, warm, cold, and frozen) to optimize performance, cost, and resource utilization. By aligning data storage with its usage patterns, organizations can efficiently manage large volumes of data while maintaining acceptable query speeds and minimizing infrastructure expenses.
+Data tiers in Elasticsearch refer to the strategy of categorizing and storing data based on its access frequency and importance. This approach involves segregating data into different storage types (like hot, warm, cold, and frozen) to optimize performance, cost, and resource utilization. By aligning data storage with its usage patterns, organizations can efficiently manage large volumes of data while maintaining acceptable query speeds and minimizing infrastructure expenses.
+
+Visit the following resources to learn more:
+
+- [@official@Data tiers](https://www.elastic.co/docs/manage-data/lifecycle/data-tiers)
+- [@official@Elastic data tiering strategy: Optimizing for a resilient and efficient implementation](https://www.elastic.co/blog/elastic-data-tiering-strategy)
+- [@official@What’s the difference? Elastic and Splunk data tiers](https://www.elastic.co/blog/elastic-splunk-data-tiers-differences)
+- [@article@Elasticsearch Multi-Tier Architecture – How to Set Up a Hot/Warm/Cold/Frozen Elasticsearch Architecture](https://opster.com/guides/elasticsearch/capacity-planning/elasticsearch-hot-warm-cold-frozen-architecture/)
+- [@article@Managing Elasticsearch Storage Tiers: Hot, Warm, Cold, and Frozen](https://www.hyperflex.co/solution-and-best-practices/managing-elasticsearch-storage-tiers-hot-warm-cold-and-frozen)
+- [@video@Setting Up Data Tiers (Snippet)](https://www.youtube.com/watch?v=f9MS5Kw3H8U)
@@ -1,3 +1,10 @@
 # Doc Values

-Doc values are a data structure in Elasticsearch that stores field values in a column-oriented fashion, optimized for aggregations, sorting, and scripting. Instead of storing the data alongside the inverted index, doc values are stored separately on disk, making them efficient for retrieving values for a large number of documents. This allows Elasticsearch to perform operations like sorting and aggregations much faster than if it had to retrieve the data from the inverted index.
+Doc values are a data structure in Elasticsearch that stores field values in a column-oriented fashion, optimized for aggregations, sorting, and scripting. Instead of storing the data alongside the inverted index, doc values are stored separately on disk, making them efficient for retrieving values for a large number of documents. This allows Elasticsearch to perform operations like sorting and aggregations much faster than if it had to retrieve the data from the inverted index.
+
+Visit the following resources to learn more:
+
+- [@official@doc_values](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/doc-values)
+- [@article@Elasticsearch doc-values-only Fields](https://opster.com/guides/elasticsearch/data-architecture/elasticsearch-doc-values-only-fields/)
+- [@article@Elasticsearch _source, doc_values and store Performance](https://sease.io/2021/02/field-retrieval-performance-in-elasticsearch.html)
+- [@video@Field Data vs Doc Values | Understanding Elasticsearch Performance Issues](https://www.youtube.com/watch?v=l99lIuvQULk)
@@ -1,3 +1,9 @@
 # Fielddata

-Fielddata is an on-disk data structure used by Elasticsearch to enable aggregations, sorting, and scripting on text fields. Because text fields are analyzed (broken down into individual terms), Elasticsearch needs a way to quickly access all the terms for a specific document when performing these operations. Fielddata loads all the terms for a field into memory, allowing for fast access during these operations.
+Fielddata is an on-disk data structure used by Elasticsearch to enable aggregations, sorting, and scripting on text fields. Because text fields are analyzed (broken down into individual terms), Elasticsearch needs a way to quickly access all the terms for a specific document when performing these operations. Fielddata loads all the terms for a field into memory, allowing for fast access during these operations.
+
+Visit the following resources to learn more:
+
+- [@article@What is Elasticsearch Fielddata?](https://pulse.support/kb/what-is-elasticsearch-fielddata)
+- [@article@Elasticsearch Fielddata](https://opster.com/guides/elasticsearch/glossary/elasticsearch-fielddata/)
+- [@video@Field Data vs Doc Values | Understanding Elasticsearch Performance Issues](https://www.youtube.com/watch?v=l99lIuvQULk)
@@ -1,3 +1,9 @@
 # Filter Aggregations

-Filter aggregations narrow down the documents that are used to calculate metrics within an aggregation. They work by applying a filter to the documents before the aggregation is performed, effectively creating a subset of the data for analysis. This allows you to focus on specific segments of your data and gain insights into particular subsets of your documents.
+Filter aggregations narrow down the documents that are used to calculate metrics within an aggregation. They work by applying a filter to the documents before the aggregation is performed, effectively creating a subset of the data for analysis. This allows you to focus on specific segments of your data and gain insights into particular subsets of your documents.
+
+Visit the following resources to learn more:
+
+- [@official@Filter aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-bucket-filter-aggregation)
+- [@official@Multi-bucket filters aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-bucket-filters-aggregation)
+- [@article@Elasticsearch Filter Aggregation: Advanced Usage and Optimization Techniques](https://opster.com/guides/elasticsearch/search-apis/elasticsearch-filter-aggregation/)
@@ -1,3 +1,10 @@
 # Function Score Query

-The Function Score Query allows you to modify the score of documents retrieved by a query. It provides a way to apply a function to each document that matches the base query, influencing its final relevance score. This function can be based on factors like document fields, pre-defined weights, or even custom scripts, enabling fine-grained control over search results ranking.
+The Function Score Query allows you to modify the score of documents retrieved by a query. It provides a way to apply a function to each document that matches the base query, influencing its final relevance score. This function can be based on factors like document fields, pre-defined weights, or even custom scripts, enabling fine-grained control over search results ranking.
+
+Visit the following resources to learn more:
+
+- [@official@Function score query](https://www.elastic.co/docs/reference/query-languages/query-dsl/query-dsl-function-score-query)
+- [@official@A Gentle Intro to Function Scoring](https://www.elastic.co/blog/found-function-scoring)
+- [@article@Elasticsearch Function Score: Boosting Relevance with Custom Scoring](https://opster.com/guides/elasticsearch/search-apis/elasticsearch-function-score/)
+- [@article@Elasticsearch Function Score Query - Syntax, Example, and Tips](https://pulse.support/kb/elasticsearch-function-score-query)
@@ -1,3 +1,10 @@
 # Histogram Aggregation

-A histogram aggregation calculates the distribution of numeric values across a set of intervals, or "buckets." It groups data into these buckets based on their values, providing a count of how many data points fall within each bucket's range. This allows you to visualize the frequency of values within specific ranges, revealing patterns and trends in your data.
+A histogram aggregation calculates the distribution of numeric values across a set of intervals, or "buckets." It groups data into these buckets based on their values, providing a count of how many data points fall within each bucket's range. This allows you to visualize the frequency of values within specific ranges, revealing patterns and trends in your data.
+
+Visit the following resources to learn more:
+
+- [@official@Histogram aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-bucket-histogram-aggregation)
+- [@article@Mastering Elasticsearch Histogram Aggregations](https://opster.com/guides/elasticsearch/how-tos/elasticsearch-histogram-aggregations/)
+- [@article@Elasticsearch Date Histogram Aggregation - Syntax, Example, and Tips](https://pulse.support/kb/elasticsearch-date-histogram-aggregation)
+- [@video@Elasticsearch Bucket Aggregations Part 1, Date Histogram Aggregation - S1E16: Mini Beginner's Course](https://www.youtube.com/watch?v=iDaAW3__hb8)
@@ -1,3 +1,11 @@
 # Hybrid Search

-Hybrid search combines multiple search techniques to improve the relevance and accuracy of search results. It leverages the strengths of different approaches, such as keyword-based search and semantic search, to provide a more comprehensive and nuanced understanding of the user's query and the available data. By blending these methods, hybrid search aims to overcome the limitations of any single approach and deliver more relevant and meaningful results.
+Hybrid search combines multiple search techniques to improve the relevance and accuracy of search results. It leverages the strengths of different approaches, such as keyword-based search and semantic search, to provide a more comprehensive and nuanced understanding of the user's query and the available data. By blending these methods, hybrid search aims to overcome the limitations of any single approach and deliver more relevant and meaningful results.
+
+Visit the following resources to learn more:
+
+- [@official@What is hybrid search?](https://www.elastic.co/what-is/hybrid-search)
+- [@official@Elasticsearch hybrid search](https://www.elastic.co/search-labs/blog/hybrid-search-elasticsearch)
+- [@official@Hybrid Search: Combined Full-Text and kNN Results](https://www.elastic.co/search-labs/tutorials/search-tutorial/vector-search/hybrid-search)
+- [@video@What is hybrid search in Elasticsearch?](https://www.youtube.com/watch?v=IPGIU2QmZjw)
+- [@video@How to build an advanced semantic search engine with hybrid search | Elasticsearch Coding Sessions](https://www.youtube.com/watch?v=inaBjdvdFgA)
@@ -1,3 +1,11 @@
 # Index Lifecycle Management (ILM)

-Index Lifecycle Management (ILM) automates the process of managing Elasticsearch indices over time. It defines policies to control how indices are stored, moved, and deleted based on factors like age, size, or performance. This helps optimize resource utilization, reduce storage costs, and ensure data is available when needed.
+Index Lifecycle Management (ILM) automates the process of managing Elasticsearch indices over time. It defines policies to control how indices are stored, moved, and deleted based on factors like age, size, or performance. This helps optimize resource utilization, reduce storage costs, and ensure data is available when needed.
+
+Visit the following resources to learn more:
+
+- [@official@Index lifecycle management](https://www.elastic.co/docs/manage-data/lifecycle/index-lifecycle-management)
+- [@official@Index lifecycle management settings in Elasticsearch](https://www.elastic.co/docs/reference/elasticsearch/configuration-reference/index-lifecycle-management-settings)
+- [@official@Monitoring Elasticsearch index lifecycle management with the history index](https://www.elastic.co/blog/elasticsearch-index-lifecycle-management-history-index)
+- [@article@An Introduction to Index Life Cycle Management in Elasticsearch](https://medium.com/knowledgelens/an-introduction-to-index-life-cycle-management-in-elasticsearch-da6b0ff579c3)
+- [@video@Setting Up Elasticsearch ILM - Index Lifecycle Management](https://www.youtube.com/watch?v=TPO6WzRp6Vo)
@@ -1,3 +1,8 @@
 # Latest Transformation

-The "latest" transformation in Elasticsearch is used to identify and extract the most recent document within a group of documents that share a common field value. It allows you to find the most up-to-date information for each unique entity based on a specified sorting criteria, such as a timestamp or version number. This is particularly useful when dealing with time-series data or scenarios where you need to retrieve the latest state of an object.
+The "latest" transformation in Elasticsearch is used to identify and extract the most recent document within a group of documents that share a common field value. It allows you to find the most up-to-date information for each unique entity based on a specified sorting criteria, such as a timestamp or version number. This is particularly useful when dealing with time-series data or scenarios where you need to retrieve the latest state of an object.
+
+Visit the following resources to learn more:
+
+- [@official@Latest transforms](https://www.elastic.co/docs/explore-analyze/transforms/transform-overview#latest-transform-overview)
+- [@official@Transform and enrich data](https://www.elastic.co/docs/manage-data/ingest/transform-enrich)
@@ -1,3 +1,10 @@
 # Nested Aggregations

-Nested aggregations allow you to perform aggregations on nested objects within your documents. These nested objects are stored as separate documents internally by Elasticsearch, and nested aggregations provide a way to access and analyze the data within these nested structures as if they were part of the parent document. This is particularly useful when you have complex data structures where related information is embedded within a single document.
+Nested aggregations allow you to perform aggregations on nested objects within your documents. These nested objects are stored as separate documents internally by Elasticsearch, and nested aggregations provide a way to access and analyze the data within these nested structures as if they were part of the parent document. This is particularly useful when you have complex data structures where related information is embedded within a single document.
+
+Visit the following resources to learn more:
+
+- [@official@Nested aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-bucket-nested-aggregation)
+- [@article@Elasticsearch Nested Aggregation](https://opster.com/guides/elasticsearch/data-architecture/elasticsearch-nested-aggregation/)
+- [@article@How to Optimize Nested Aggregations in Elasticsearch](https://opster.com/guides/elasticsearch/search-apis/optimizing-nested-aggregations-elasticsearch/)
+- [@video@Nested Aggregations](https://www.youtube.com/watch?v=G1ExN9cBVCw)
@@ -1,3 +1,10 @@
 # Pipeline Aggregations

-Pipeline aggregations in Elasticsearch take the results of other aggregations as their input, allowing you to perform calculations and derive new insights based on the aggregated data. Instead of operating on the documents themselves, they process the output of other aggregations, enabling you to create complex analytical pipelines within your search queries. This allows for calculations like moving averages, derivatives, and cumulative sums to be performed directly within Elasticsearch.
+Pipeline aggregations in Elasticsearch take the results of other aggregations as their input, allowing you to perform calculations and derive new insights based on the aggregated data. Instead of operating on the documents themselves, they process the output of other aggregations, enabling you to create complex analytical pipelines within your search queries. This allows for calculations like moving averages, derivatives, and cumulative sums to be performed directly within Elasticsearch.
+
+Visit the following resources to learn more:
+
+- [@official@Pipeline](https://www.elastic.co/docs/reference/aggregations/pipeline)
+- [@article@Comprehensive Guide to Elasticsearch Pipeline Aggregations: Part I](https://medium.com/qbox-search-as-a-service/comprehensive-guide-to-elasticsearch-pipeline-aggregations-part-i-be77aff65630)
+- [@article@Comprehensive Guide to Elasticsearch Pipeline Aggregations: Part II](https://medium.com/qbox-search-as-a-service/comprehensive-guide-to-elasticsearch-pipeline-aggregations-part-ii-f7d3dd34e4bb)
+- [@video@Pipeline Aggregations in Elasticsearch [ElasticSearch 7 for Beginners 5.3]](https://www.youtube.com/watch?v=nLSdwtqWqtk)
@@ -1,3 +1,9 @@
 # Pivot Transformation

-The pivot transformation in Elasticsearch is a way to reshape your data by aggregating values from one or more fields into columns. It essentially rotates the data, turning unique values in a field into separate fields in the output. This allows you to analyze and visualize data in a different format, making it easier to identify trends and patterns that might be hidden in the original structure.
+The pivot transformation in Elasticsearch is a way to reshape your data by aggregating values from one or more fields into columns. It essentially rotates the data, turning unique values in a field into separate fields in the output. This allows you to analyze and visualize data in a different format, making it easier to identify trends and patterns that might be hidden in the original structure.
+
+Visit the following resources to learn more:
+
+- [@official@Transforming data](https://www.elastic.co/docs/explore-analyze/transforms)
+- [@official@Pivot transforms](https://www.elastic.co/docs/explore-analyze/transforms/transform-overview#pivot-transform-overview)
+- [@official@Transforms examples](https://www.elastic.co/docs/explore-analyze/transforms/transform-examples)
@@ -1,3 +1,12 @@
 # Range/Date Range Aggregations

-Range and Date Range aggregations are used to categorize documents into buckets based on numeric or date values falling within specified ranges. These aggregations allow you to define custom intervals for grouping data, providing flexibility in analyzing distributions and trends across your dataset. You can define specific start and end points for each range, enabling you to create meaningful segments for your analysis.
+Range and Date Range aggregations are used to categorize documents into buckets based on numeric or date values falling within specified ranges. These aggregations allow you to define custom intervals for grouping data, providing flexibility in analyzing distributions and trends across your dataset. You can define specific start and end points for each range, enabling you to create meaningful segments for your analysis.
+
+Visit the following resources to learn more:
+
+- [@official@Range aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-bucket-range-aggregation)
+- [@official@Date range aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-bucket-daterange-aggregation)
+- [@article@Elasticsearch Range Aggregation - Syntax, Example, and Tips](https://pulse.support/kb/elasticsearch-range-aggregation)
+- [@article@Elasticsearch Date Range Aggregation - Syntax, Example, and Tips](https://pulse.support/kb/elasticsearch-date-range-aggregation)
+- [@video@Elasticsearch Bucket, Histogram, Range & Terms Aggregations - S1E17 Mini Beginner's Crash Course](https://www.youtube.com/watch?v=R114ib2D9mU)
+- [@video@Bucket Aggregations in Elasticsearch | ElasticSearch 7 for Beginners #5.2](https://www.youtube.com/watch?v=8QmBZLOl9Y8&t=277s)
@@ -0,0 +1,10 @@
+# Reindex API
+
+The Reindex API in Elasticsearch allows you to copy documents from one index to another. This is useful for a variety of tasks, including changing the mapping of an index, upgrading to a new Elasticsearch version, or splitting a large index into smaller ones. It essentially reads documents from a source index and writes them into a destination index, optionally applying transformations along the way.
+
+Visit the following resources to learn more:
+
+- [@official@Reindex documents](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-reindex)
+- [@article@Reindex indices examples](https://www.elastic.co/docs/reference/elasticsearch/rest-apis/reindex-indices)
+- [@article@Elasticsearch Reindexing: When to Reindex, Best Practices and Alternatives](https://medium.com/@jmills2010/elasticsearch-reindexing-when-to-reindex-best-practices-and-alternatives-7ebfa11667a0)
+- [@article@Elasticsearch Reindex API: A Guide to Data Management](https://last9.io/blog/elasticsearch-reindex-api/)
@@ -1,3 +1,11 @@
 # Roles & Users

-Roles and users are fundamental components of security in Elasticsearch. Roles define a set of privileges, specifying what actions a user can perform on which resources (like indices or clusters). Users are then assigned one or more roles, granting them the combined permissions of those roles. This system allows administrators to control access to data and cluster operations, ensuring that only authorized individuals can perform specific tasks within the Elasticsearch environment.
+Roles and users are fundamental components of security in Elasticsearch. Roles define a set of privileges, specifying what actions a user can perform on which resources (like indices or clusters). Users are then assigned one or more roles, granting them the combined permissions of those roles. This system allows administrators to control access to data and cluster operations, ensuring that only authorized individuals can perform specific tasks within the Elasticsearch environment.
+
+Visit the following resources to learn more:
+
+- [@official@User roles](https://www.elastic.co/docs/deploy-manage/users-roles/cluster-or-deployment-auth/user-roles)
+- [@official@Users and roles](https://www.elastic.co/docs/deploy-manage/users-roles)
+- [@official@User roles and privileges](https://www.elastic.co/docs/deploy-manage/users-roles/cloud-organization/user-roles)
+- [@official@Manage users and roles](https://www.elastic.co/docs/deploy-manage/users-roles/cloud-enterprise-orchestrator/manage-users-roles)
+- [@video@Managing Kibana Users, Roles & Permissions - Daily Elastic Byte S02E12](https://www.youtube.com/watch?v=mLRnNk1ZpTQ)
@@ -1,3 +1,12 @@
 # Rollover Policies

-Rollover policies in Elasticsearch automate the management of indices over time. They define conditions, such as index size, document count, or age, that trigger the creation of a new index and the transition of write operations to it. This process helps maintain manageable index sizes, optimize search performance, and simplify data retention strategies.
+Rollover policies in Elasticsearch automate the management of indices over time. They define conditions, such as index size, document count, or age, that trigger the creation of a new index and the transition of write operations to it. This process helps maintain manageable index sizes, optimize search performance, and simplify data retention strategies.
+
+Visit the following resources to learn more:
+
+- [@official@About rollover](https://www.elastic.co/docs/manage-data/lifecycle/index-lifecycle-management/rollover)
+- [@official@Rollover](https://www.elastic.co/docs/reference/elasticsearch/index-lifecycle-actions/ilm-rollover)
+- [@official@Roll over to a new index](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-rollover)
+- [@official@Configuring rollover](https://www.elastic.co/docs/manage-data/lifecycle/index-lifecycle-management/ilm-tutorials#configuring-rollover)
+- [@article@Elasticsearch Index Life cycle and Rollover Policy](https://www.elastic.co/docs/reference/elasticsearch/index-lifecycle-actions/ilm-rollover)
+- [@video@Optimizing Index Operations in Elasticsearch: Shrink & Rollover - Daily Elastic Byte S01E05](https://www.youtube.com/watch?v=9U9OBWfxC-M)
@@ -1,3 +1,12 @@
 # Search Analyzer

-A search analyzer in Elasticsearch is responsible for processing the query text provided by a user before it's used to search the index. It transforms the query text into a format that matches the indexed data, ensuring relevant results are retrieved. This process typically involves character filtering, tokenization, and token filtering, similar to the analysis process performed on documents during indexing, but tailored for search queries.
+A search analyzer in Elasticsearch is responsible for processing the query text provided by a user before it's used to search the index. It transforms the query text into a format that matches the indexed data, ensuring relevant results are retrieved. This process typically involves character filtering, tokenization, and token filtering, similar to the analysis process performed on documents during indexing, but tailored for search queries.
+
+Visit the following resources to learn more:
+
+- [@official@Anatomy of an analyzer](https://www.elastic.co/docs/manage-data/data-store/text-analysis/anatomy-of-an-analyzer)
+- [@official@Index and search analysis](https://www.elastic.co/docs/manage-data/data-store/text-analysis/index-search-analysis)
+- [@official@Specify an analyzer](https://www.elastic.co/docs/manage-data/data-store/text-analysis/specify-an-analyzer)
+- [@article@https://pulse.support/kb/what-is-elasticsearch-analyzer](https://pulse.supphttps//pulse.support/kb/what-is-elasticsearch-analyzerort/kb/what-is-elasticsearch-analyzer)
+- [@video@Elastic Search Analyzer explained in a easy way](https://www.youtube.com/watch?v=9VhTnWuely4)
+- [@video@Mapping and Analysers [ElasticSearch 7 for Beginners #3.2]](https://www.youtube.com/watch?v=_OjUoZ5NbYY)
@@ -1,3 +1,8 @@
 # Segment Merging

-Segment merging is the process of combining multiple smaller segments in an Elasticsearch index into larger segments. This optimization reduces the number of segments the search engine needs to consult during a query, leading to faster search performance and more efficient resource utilization. The process involves reading the data from the smaller segments, merging them, and writing the merged data into a new, larger segment.
+Segment merging is the process of combining multiple smaller segments in an Elasticsearch index into larger segments. This optimization reduces the number of segments the search engine needs to consult during a query, leading to faster search performance and more efficient resource utilization. The process involves reading the data from the smaller segments, merging them, and writing the merged data into a new, larger segment.
+
+Visit the following resources to learn more:
+
+- [@official@Merge settings](https://www.elastic.co/docs/reference/elasticsearch/index-settings/merge)
+- [@article@Mastering ElasticSearch Write Performance: Refresh, Merge & Flush Explained](https://medium.com/@mokshteng/mastering-elasticsearch-write-performance-refresh-merge-flush-explained-290631930e4a)
@@ -1,3 +1,13 @@
 # Semantic Search

-Semantic search aims to improve search accuracy by understanding the intent and contextual meaning of search queries. Instead of relying solely on keyword matching, it analyzes the relationships between words and concepts to deliver more relevant results. This involves using techniques like natural language processing (NLP) and machine learning to interpret the meaning behind the query and match it with documents that have similar meaning, even if they don't contain the exact keywords.
+Semantic search aims to improve search accuracy by understanding the intent and contextual meaning of search queries. Instead of relying solely on keyword matching, it analyzes the relationships between words and concepts to deliver more relevant results. This involves using techniques like natural language processing (NLP) and machine learning to interpret the meaning behind the query and match it with documents that have similar meaning, even if they don't contain the exact keywords.
+
+Visit the following resources to learn more:
+
+- [@official@Semantic search](https://www.elastic.co/docs/solutions/search/semantic-search)
+- [@official@What is semantic search?](https://www.elastic.co/what-is/semantic-search)
+- [@article@Vector-Based Semantic Search using Elasticsearch](https://medium.com/version-1/vector-based-semantic-search-using-elasticsearch-48d7167b38f5)
+- [@article@Semantic Searches with Elasticsearch](https://heidloff.net/article/semantic-search-vector-eslasticsearch/)
+- [@video@Semantic Search Made Easy & Complex by Sander Philipse, Elastic](https://www.youtube.com/watch?v=tOCwVkoPtI8)
+- [@video@Semantic Search Explained: Search with intent [Quick Question Ep. 3]](https://www.youtube.com/watch?v=eZNV_jkbdW0&t=15s)
+- [@video@What Is Vector Search? Difference Between Vector & Semantic Search Explained [Quick Question Ep. 5]](https://www.youtube.com/watch?v=BKbScJ2P2P0&t=45s)
@@ -1,3 +1,11 @@
 # SLM

-Snapshot Lifecycle Management (SLM) provides a way to automate the creation, retention, and deletion of Elasticsearch snapshots. It allows you to define policies that specify when snapshots should be taken, how long they should be kept, and how they should be named, ensuring consistent and reliable backups of your Elasticsearch data.
+Snapshot Lifecycle Management (SLM) provides a way to automate the creation, retention, and deletion of Elasticsearch snapshots. It allows you to define policies that specify when snapshots should be taken, how long they should be kept, and how they should be named, ensuring consistent and reliable backups of your Elasticsearch data.
+
+Visit the following resources to learn more:
+
+- [@official@Create, monitor and delete snapshots](https://www.elastic.co/docs/deploy-manage/tools/snapshot-and-restore/create-snapshots)
+- [@official@Start snapshot lifecycle management](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-slm-start)
+- [@article@Elasticsearch SLM – Elasticsearch Snapshot Lifecycle Management](https://opster.com/guides/elasticsearch/operations/elasticsearch-slm-elasticsearch-snapshot-lifecycle-management/)
+- [@video@Index Lifecycle Management (ILM) & Snapshot Lifecycle Management (SLM) - Daily Elastic Byte S01E15](https://www.youtube.com/watch?v=JhxMpUY5upg)
+- [@video@32 Cluster Management: Automate snapshots with Snapshot Lifecycle Management](https://www.youtube.com/watch?v=-ZNTL1uzFP8)
@@ -1,3 +1,12 @@
 # Snapshots and Restores

-Snapshots are backups of your Elasticsearch cluster's data and state, stored in a repository. Restoring from a snapshot allows you to recover data in case of failure, corruption, or accidental deletion. This mechanism provides a way to revert your cluster to a previous point in time, ensuring data safety and disaster recovery capabilities.
+Snapshots are backups of your Elasticsearch cluster's data and state, stored in a repository. Restoring from a snapshot allows you to recover data in case of failure, corruption, or accidental deletion. This mechanism provides a way to revert your cluster to a previous point in time, ensuring data safety and disaster recovery capabilities.
+
+Visit the following resources to learn more:
+
+- [@official@Snapshot and restore docs](https://www.elastic.co/docs/deploy-manage/tools/snapshot-and-restore)
+- [@official@Restore from snapshot](https://www.elastic.co/docs/troubleshoot/elasticsearch/restore-from-snapshot)
+- [@official@Snapshot and Restore](https://www.elastic.co/blog/found-elasticsearch-snapshot-and-restore)
+- [@article@Elasticsearch Snapshot and Restore Feature](https://medium.com/orion-innovation-techclub/elasticsearch-snapshot-and-restore-feature-f7d52a9fd40)
+- [@video@Elasticsearch Snapshot & Restore: Managing Snapshots within Kibana - Daily Elastic Byte S02E14](https://www.youtube.com/watch?v=hc6V-1aR33E)
+- [@video@Backup Elasticsearch Data - Snapshot and Restore -Let's Deploy a Host Intrusion Detection System #15](https://www.youtube.com/watch?v=gIZNez_gmMQ)
@@ -1,3 +1,11 @@
 # Standard Analyzer

-The Standard Analyzer is a default text analyzer in Elasticsearch that breaks text into individual words based on whitespace and punctuation. It also converts all terms to lowercase and removes common English stop words like "the," "a," and "is." This analyzer is a good general-purpose choice for many text indexing and searching tasks.
+The Standard Analyzer is a default text analyzer in Elasticsearch that breaks text into individual words based on whitespace and punctuation. It also converts all terms to lowercase and removes common English stop words like "the," "a," and "is." This analyzer is a good general-purpose choice for many text indexing and searching tasks.
+
+Visit the following resources to learn more:
+
+- [@official@Standard analyzer](https://www.elastic.co/docs/reference/text-analysis/analysis-standard-analyzer)
+- [@official@Configure text analysis](https://www.elastic.co/docs/manage-data/data-store/text-analysis/configure-text-analysis)
+- [@official@Configuring built-in analyzers](https://www.elastic.co/docs/manage-data/data-store/text-analysis/configuring-built-in-analyzers)
+- [@article@Elasticsearch Text Analyzers – Tokenizers, Standard Analyzers, Stopwords and More](https://opster.com/guides/elasticsearch/data-architecture/elasticsearch-text-analyzers/)
+- [@article@Elasticsearch in Action: Standard Text Analyzer](https://mkonda007.medium.com/elasticsearch-in-action-standard-text-analyzer-87d4164e412e)
@@ -1,3 +1,10 @@
 # Stats and Extended Stats Aggregations

-Stats and Extended Stats aggregations are used to calculate various statistical measures from a set of numeric values. The Stats aggregation provides basic statistics like count, min, max, average, and sum. The Extended Stats aggregation builds upon this by adding standard deviation, sum of squares, variance, and other related metrics, offering a more comprehensive statistical overview of the data.
+Stats and Extended Stats aggregations are used to calculate various statistical measures from a set of numeric values. The Stats aggregation provides basic statistics like count, min, max, average, and sum. The Extended Stats aggregation builds upon this by adding standard deviation, sum of squares, variance, and other related metrics, offering a more comprehensive statistical overview of the data.
+
+Visit the following resources to learn more:
+
+- [@official@Stats aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-metrics-stats-aggregation)
+- [@official@Extended stats aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-metrics-extendedstats-aggregation)
+- [@article@Elasticsearch Stats Aggregation - Syntax, Example, and Tips](https://pulse.support/kb/elasticsearch-stats-aggregation)
+- [@article@Elasticsearch Extended Stats Aggregation - Syntax, Example, and Tips](https://pulse.support/kb/elasticsearch-extended-stats-aggregation)
@@ -1,3 +1,12 @@
-# Synonyms Graph
+# c

-Synonyms Graph is a feature in Elasticsearch that allows you to expand your search queries by including words or phrases that have similar meanings. Instead of just searching for the exact terms entered by a user, Elasticsearch can also search for related terms defined as synonyms, improving the recall of search results. The "graph" aspect refers to how these synonyms are represented internally, allowing for more complex relationships between terms, including multi-word synonyms and different synonym types.
+Synonyms Graph is a feature in Elasticsearch that allows you to expand your search queries by including words or phrases that have similar meanings. Instead of just searching for the exact terms entered by a user, Elasticsearch can also search for related terms defined as synonyms, improving the recall of search results. The "graph" aspect refers to how these synonyms are represented internally, allowing for more complex relationships between terms, including multi-word synonyms and different synonym types.
+
+Visit the following resources to learn more:
+
+- [@official@Search with synonyms](https://www.elastic.co/docs/solutions/search/full-text/search-with-synonyms)
+- [@official@Update your synonyms in Elasticsearch: Introducing the synonyms Synonyms Guide](https://www.elastic.co/guide/en/app-search/current/synonyms-guide.html)
+- [@official@Multi-Token Synonyms and Graph Queries in Elasticsearch](https://www.elastic.co/blog/multitoken-synonyms-and-graph-queries-in-elasticsearch)
+- [@official@Update your synonyms in Elasticsearch: Introducing the synonyms API](https://www.elastic.co/search-labs/blog/update-synonyms-elasticsearch-introducing-synonyms-api)
+- [@video@How to use the Elasticsearch Synonym API to improve search accuracy](https://www.youtube.com/watch?v=lJaiVZbCpbY)
+- [@video@ElasticSearch in Python #25 - Synonyms API](https://www.youtube.com/watch?v=kOm8r7v0yu4)
@@ -1,3 +1,13 @@
 # Terms Aggregation

-The Terms aggregation is a multi-bucket aggregation that groups documents based on the terms found in a specific field. It analyzes the field's values and creates buckets for each unique term, counting the number of documents that contain that term. This allows you to identify the most frequent terms within your data and gain insights into the distribution of values in a field.
+The Terms aggregation is a multi-bucket aggregation that groups documents based on the terms found in a specific field. It analyzes the field's values and creates buckets for each unique term, counting the number of documents that contain that term. This allows you to identify the most frequent terms within your data and gain insights into the distribution of values in a field.
+
+Visit the following resources to learn more:
+
+- [@official@Terms aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-bucket-terms-aggregation)
+- [@official@Multi Terms aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-bucket-multi-terms-aggregation)
+- [@official@Unveiling unique patterns: A guide to significant terms aggregation in Elasticsearch](https://www.elastic.co/search-labs/blog/significant-terms-aggregation-elasticsearch)
+- [@official@Bucket](https://www.elastic.co/docs/reference/aggregations/bucket)
+- [@article@Elasticsearch Terms Aggregation - Syntax, Example, and Tips](https://pulse.support/kb/elasticsearch-terms-aggregation)
+- [@article@Terms Aggregation on High-Cardinality Fields in Elasticsearch](https://opster.com/guides/elasticsearch/search-apis/terms-aggregation-on-high-cardinality-fields-in-elasticsearch/)
+- [@video@Elasticsearch Bucket, Histogram, Range & Terms Aggregations - S1E17 Mini Beginner's Crash Course](https://www.youtube.com/watch?v=R114ib2D9mU)
@@ -1,3 +1,9 @@
 # The Analyze API

-The Analyze API in Elasticsearch allows you to break down a text string into its individual terms, which are the basic building blocks for searching and indexing. It simulates the analysis process that Elasticsearch performs when indexing or searching documents, letting you see how a specific analyzer would process a given piece of text. This is useful for testing and debugging your analysis configuration.
+The Analyze API in Elasticsearch allows you to break down a text string into its individual terms, which are the basic building blocks for searching and indexing. It simulates the analysis process that Elasticsearch performs when indexing or searching documents, letting you see how a specific analyzer would process a given piece of text. This is useful for testing and debugging your analysis configuration.
+
+Visit the following resources to learn more:
+
+- [@official@Get tokens from text analysis](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-analyze)
+- [@official@Test an analyzer](https://www.elastic.co/docs/manage-data/data-store/text-analysis/test-an-analyzer)
+- [@article@Leveraging the Elasticsearch Analyze API for Advanced Text Analysis](https://www.dhiwise.com/post/leveraging-the-elasticsearch-analyze-api-for-text-analysis)
@@ -1,3 +1,11 @@
 # The Inverted Index

-The inverted index is a data structure that stores a mapping from content, such as words or numbers, to their locations in a document or a set of documents. Instead of listing documents and then the words they contain, an inverted index lists words and then the documents in which those words appear. This allows for very fast full-text searches.
+The inverted index is a data structure that stores a mapping from content, such as words or numbers, to their locations in a document or a set of documents. Instead of listing documents and then the words they contain, an inverted index lists words and then the documents in which those words appear. This allows for very fast full-text searches.
+
+Visit the following resources to learn more:
+
+- [@official@Elasticsearch from the Bottom Up, Part 1](https://www.elastic.co/blog/found-elasticsearch-from-the-bottom-up)
+- [@article@What is the inverted index in elastic search?](https://medium.com/@sujathamudadla1213/what-is-the-inverted-index-in-elastic-search-f04df6f0c806)
+- [@article@Elasticsearch Inverted Index: The Key to Fast Data Retrieval](https://www.datasunrise.com/knowledge-center/elasticsearch-inverted-index/)
+- [@article@Indexing: Inverted Index](https://www.baeldung.com/cs/indexing-inverted-index)
+- [@video@Inverted Index - The Data Structure Behind Search Engines](https://www.youtube.com/watch?v=iHHqnyThrqE)
@@ -1,3 +1,9 @@
 # Transform API

-The Transform API in Elasticsearch provides a way to summarize and transform data from one or more Elasticsearch indices into a new index. It essentially automates the process of creating aggregated views of your data, allowing you to perform tasks like data reduction, feature engineering, and creating summary indices for faster analysis and visualization. This API enables you to create new indices that contain pre-computed aggregations and transformations of your source data.
+The Transform API in Elasticsearch provides a way to summarize and transform data from one or more Elasticsearch indices into a new index. It essentially automates the process of creating aggregated views of your data, allowing you to perform tasks like data reduction, feature engineering, and creating summary indices for faster analysis and visualization. This API enables you to create new indices that contain pre-computed aggregations and transformations of your source data.
+
+Visit the following resources to learn more:
+
+- [@official@Create a transform](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-transform-put-transform)
+- [@official@Get transforms](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-transform-get-transform)
+- [@article@Elasticsearch Transform APIs](https://opster.com/guides/elasticsearch/data-architecture/transform-apis-in-elasticsearch/)
@@ -1,3 +1,10 @@
 # Understanding Similarity

-Similarity in information retrieval refers to the algorithm used to calculate the relevance score between a search query and a document. It determines how closely a document matches the search terms, influencing the order in which search results are presented. Different similarity algorithms consider factors like term frequency, inverse document frequency, and field length to produce a score reflecting the degree of relevance.
+Similarity in information retrieval refers to the algorithm used to calculate the relevance score between a search query and a document. It determines how closely a document matches the search terms, influencing the order in which search results are presented. Different similarity algorithms consider factors like term frequency, inverse document frequency, and field length to produce a score reflecting the degree of relevance.
+
+Visit the following resources to learn more:
+
+- [@official@similarity](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/similarity)
+- [@official@Similarity settings](https://www.elastic.co/docs/reference/elasticsearch/index-settings/similarity)
+- [@official@Similarity in Elasticsearch](https://www.elastic.co/blog/found-similarity-in-elasticsearch)
+- [@official@Vector similarity techniques and scoring](https://www.elastic.co/search-labs/blog/vector-similarity-techniques-and-scoring)
@@ -1,3 +1,11 @@
 # Value Count Aggregation

-Value Count is a type of metric aggregation that calculates the total number of values present in a specific field. It essentially counts how many documents have a value for the chosen field, including duplicates if they exist. This aggregation is useful for determining the overall occurrence or frequency of a particular field within your dataset.
+Value Count is a type of metric aggregation that calculates the total number of values present in a specific field. It essentially counts how many documents have a value for the chosen field, including duplicates if they exist. This aggregation is useful for determining the overall occurrence or frequency of a particular field within your dataset.
+
+Visit the following resources to learn more:
+
+- [@official@Aggregations](https://www.elastic.co/docs/explore-analyze/query-filter/aggregations)
+- [@official@Count search results](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-count)
+- [@official@Value count aggregation](https://www.elastic.co/docs/reference/aggregations/search-aggregations-metrics-valuecount-aggregation)
+- [@article@Elasticsearch Value Count Aggregation - Syntax, Example, and Tips](https://pulse.support/kb/elasticsearch-value-count-aggregation)
+- [@video@Elasticsearch Aggregations & go-elasticsearch - Elastic Meetup](https://www.youtube.com/watch?v=y5MUNPJzMsI)
@@ -1,3 +1,14 @@
 # Vector Search

-Vector search is a method of searching for data based on its meaning or context, rather than exact keyword matches. It involves representing data as high-dimensional vectors, where each vector captures the semantic properties of the data. Search queries are also converted into vectors, and the system finds data points with vectors that are "close" to the query vector, indicating semantic similarity.
+Vector search is a method of searching for data based on its meaning or context, rather than exact keyword matches. It involves representing data as high-dimensional vectors, where each vector captures the semantic properties of the data. Search queries are also converted into vectors, and the system finds data points with vectors that are "close" to the query vector, indicating semantic similarity.
+
+Visit the following resources to learn more:
+
+- [@official@Vector search in Elasticsearch](https://www.elastic.co/docs/solutions/search/vector)
+- [@official@What is vector search?](https://www.elastic.co/what-is/vector-search)
+- [@official@How to set up vector search in Elasticsearch](https://www.elastic.co/search-labs/blog/vector-search-set-up-elasticsearch)
+- [@official@A quick introduction to vector search](https://search-labs-redesign.vercel.app/search-labs/blog/introduction-to-vector-search)
+- [@article@Elasticsearch Was Great, But Vector Databases Are the Future](https://thenewstack.io/elasticsearch-was-great-but-vector-databases-are-the-future/)
+- [@video@What Is Vector Search? Difference Between Vector & Semantic Search Explained [Quick Question Ep. 5]](https://www.youtube.com/watch?v=BKbScJ2P2P0)
+- [@video@Elastic Snackable Series: Elasticsearch Vector Search](https://www.youtube.com/watch?v=GYtLxyvWE0w)
+- [@video@ElasticON EMEA: The Search for Relevance with Vector Search](https://www.youtube.com/watch?v=MUve9LiEAeI)