—> Elasticsearch , is a java binary 
—> once the server is installed you can analyzed and persisted by Elasticsearch oky 
—> elasticsearch with the data can help execute search queries as well as aggregations on that data 


—> our client wil  be kibana a rich UI web application from elastic
—> to provide the developer all it need to discover , analyse , manage , and maintain our cluster and data 
—> kibana give us rich visualizations and dashboards , ml models and more 
Priming Elasticsearch with data A search engine can’t work on thin air! It needs data as its input so it can produce results as output when queried. We need to dump our data into Elasticsearch, which is the first step in priming the engine. But before we start storing data in Elasticsearch, let’s get to know the sample application we work with in this chapter. For our examples, we need a basic understanding of the problem domain and the data model. Let’s assume we are building an online bookstore; obviously, we are not architecting the whole application—we are only interested in the data model part for our discussion. We go over the details of this fictitious bookstore in the next section as a prerequisite for our objective of working with Elasticsearch$


Q-  the beussines problem our application is a bookstore that pay technical books 
We use elastique search to store and the analyse and search the desired data oky 

The data model for our bookstore application is simple. We have a book as our entity, with a few properties such as title, author, and so on as described in table 2.1. We do not need to complicate things by creating elaborate entities; instead, we’ll focus on the goal of getting hands-on experience with Elasticsearch.



→ the data model here is “Book” entity 

With “title” → Title of a Book → “Effective Java” 
With “author” → Author of the book → “Joshua Bolch” 
With “release_date” → Date of release → “01-06-2001
With “amazon_rating” → average rating on Amazon → 4.7
With “best_seller” → flag that qualifies the book as a best seller → true 
With “prices” → Inner object with individual prices in three currencies → “prices” : {
“Usd” : 9.95,





—> Elasticsearch is a document data store , and it expects documents to be presented in json format 




—> to bring data into Elasticsearch we can : 
Either creating an adpetetr to pipe the data from a relational database 
Extracting the data from a file system 
Streaming events from a real time source 
—> we need to invoke “Elasticsearch” RESTul apis from the client application to loead the data into Elasticsearch oky  




→ PUT books/_doc/1  —-> the index is books and , the document ID is 1
{
 “title”:”Effective Java” , 
“Author” : “Joshua Bloch” , 
“Release_date”:”2001-06-01”,
“Amazon_rating”:4.7 , 
“Best_seller”:true,
“Prices”: {
	“usd”: 9.95 , 
	“Gbp”: 7.95,
	“





—> as you see here Kibana request : abstract a loot of details from the actual cURl request 
For example 

PUT books/_doc/1 
{
	“title”:”Effective Java” 
} 

→ the server details and header info are appended to the request Kibana 



Q- my first tutor to use the most efficient update lighweite docker-compose file to install the fullstack of elk stack oky give this file and make tutor to create it locally