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+Build dashboards:
+- System Administration
+- Developers
+- Management (KPIs, etc.)
+
+We can use Elastic Search as an anlytics platform together with Kibana, it is perfectly valid and common use case.
+
+Logstash
+Process logs from applications and send them to Elasticsearch, but it has evolved into a more general purpose tool, meaning that Logstash is a data processing pipeline.
+Logstash receives events from one or more inputs, processes them, and sends them to one or more stashes.
+
+Basic Architecture of Elasticsearch
+-----------------------------------
+Elastichsearch is built in top of Apache Lucene.
+
+When we started Elasticsearch, it starts a node. 
+A node is an instance of Elasticsearch that stores data.
+
+In production is recommended each node runs in a dedicated machine/VM/container.
+
+A cluster is a collection of related nodes that together contain all of our data.
+
+One cluster is tipally fair enough. But, we can have multiples clusters, each one dedicated to specific goal.
+
+For instance, one cluster for powering the search of an e-commerce application and a 2nd one for Application Performance Management (APM)
+
+Every document in Elasticsearch is stored in indexes.
+
+An index groups documents together logically, as well as provide configuration options that are related to scalability and availability.
+
+Elastic requests syntax:
+* <http_verb> /<_api>/<command>
+
+For example:
+* GET /_cluster/health
+
+* <_api> specifies the API that we want to access.
+* Using _cat as <_api> value it means it is a CAT API, which outputs data into a human readable format
+* APIs begins with an underscore by convetion.
+* <command> specifies the command
+
+System indices:
+GET /_cat/indices?v&expand_wildcards=all
+
+* System indices starts with "." (it means will be hidden by default, like in Linux filesystem), for instance: .geoip_databases, .tasks, etc.
+
+Example by using curl:
+curl -u elastic:59a5Ezy5VCv2666acRLuMJ2i -X GET -H "Content-Type:application/json" https://complete-guide-to-elasticsearch-65bc2f.es.us-central1.gcp.cloud.es.io/products/_search -d '{ "query": { "match_all": {} } }'
+
+NOTES: For local deployments, specify either --insecure or --cacert
+
+Sharding and scalability
+-------------------------
+* Sharding is a way to divide indices into smaller pieces
+* Each piece is referred as a " shard"
+* Sharding is done at the index level
+* The main purpose is to horizontally scale the data volume
+
+A bit deeper...
+-> A shard is kind of an independent index
+-> Each shard is an Apache Lucene index
+-> An Ealsticsearch index consists of one or more Lucene indices
+-> A shard has no predefined size; it grows as documents are added to it
+-> A shard may store up to about two billions documents
+
+Configuring the number of shards
+* An index contains a single shard by default
+* Indices in Elasticsearch < 7.0.0 were created with five shards
+
+
+
+