Getting started: Deploy your own platform to store, search, and visualize any data

This guide shows you how to set up a general purpose Elastic deployment to store, search, and visualize any data. Start here if you are interested building a custom solution on the Elastic search platform.

To get started, all you need is an internet connection and an email address.

An Elastic Cloud deployment offers you all of the features of the Elastic Stack as a hosted service. To test drive your first deployment, sign up for a free Elastic Cloud trial:

You can add data to Elasticsearch by sending JSON objects (documents) to Elasticsearch over HTTP. Whether you have structured or unstructured text, numerical data, or geospatial data, Elasticsearch efficiently stores and indexes it in a way that supports fast searches.

This tutorial uses the Kibana Dev Tools console to submit REST requests to Elasticsearch, but you can use any HTTP client to send requests to Elasticsearch. Elasticsearch provides clients for Java, Javascript, and many other popular languages.

To add a document to a new index:

Instead of adding documents one at a time, you can use the _bulk endpoint to add multiple documents in one request. This minimizes network roundtrips and is significantly faster than adding documents one at a time.

The optimal batch size depends on a number of factors: the document size and complexity, the indexing and search load, and the resources available to your cluster. A good place to start is with batches of 1,000 to 5,000 documents and a total payload between 5MB and 15MB.

For example, submit the following bulk request to add 10 documents to the bank index.

PUT bank/_bulk
{ "create":{ } }
{ "account_number":1,"balance":39225,"firstname":"Amber","lastname":"Duke","age":32,"gender":"M","address":"880 Holmes Lane","employer":"Pyrami","email":"amberduke@pyrami.com","city":"Brogan","state":"IL" }
{ "create":{ } }
{ "account_number":6,"balance":5686,"firstname":"Hattie","lastname":"Bond","age":36,"gender":"M","address":"671 Bristol Street","employer":"Netagy","email":"hattiebond@netagy.com","city":"Dante","state":"TN" }
{ "create":{ } }
{ "account_number":13,"balance":32838,"firstname":"Nanette","lastname":"Bates","age":28,"gender":"F","address":"789 Madison Street","employer":"Quility","email":"nanettebates@quility.com","city":"Nogal","state":"VA" }
{ "create":{ } }
{ "account_number":18,"balance":4180,"firstname":"Dale","lastname":"Adams","age":33,"gender":"M","address":"467 Hutchinson Court","employer":"Boink","email":"daleadams@boink.com","city":"Orick","state":"MD" }
{ "create":{ } }
{ "account_number":20,"balance":16418,"firstname":"Elinor","lastname":"Ratliff","age":36,"gender":"M","address":"282 Kings Place","employer":"Scentric","email":"elinorratliff@scentric.com","city":"Ribera","state":"WA" }
{ "create":{ } }
{ "account_number":25,"balance":40540,"firstname":"Virginia","lastname":"Ayala","age":39,"gender":"F","address":"171 Putnam Avenue","employer":"Filodyne","email":"virginiaayala@filodyne.com","city":"Nicholson","state":"PA" }
{ "create":{ } }
{ "account_number":32,"balance":48086,"firstname":"Dillard","lastname":"Mcpherson","age":34,"gender":"F","address":"702 Quentin Street","employer":"Quailcom","email":"dillardmcpherson@quailcom.com","city":"Veguita","state":"IN" }
{ "create":{ } }
{ "account_number":37,"balance":18612,"firstname":"Mcgee","lastname":"Mooney","age":39,"gender":"M","address":"826 Fillmore Place","employer":"Reversus","email":"mcgeemooney@reversus.com","city":"Tooleville","state":"OK" }
{ "create":{ } }
{ "account_number":44,"balance":34487,"firstname":"Aurelia","lastname":"Harding","age":37,"gender":"M","address":"502 Baycliff Terrace","employer":"Orbalix","email":"aureliaharding@orbalix.com","city":"Yardville","state":"DE" }
{ "create":{ } }
{ "account_number":49,"balance":29104,"firstname":"Fulton","lastname":"Holt","age":23,"gender":"F","address":"451 Humboldt Street","employer":"Anocha","email":"fultonholt@anocha.com","city":"Sunriver","state":"RI" }

Indexed documents are available for search in near real-time. To search for specific terms within a field, you can use a match query. For example, the following request searches the address field to find customers whose addresses contain mill or lane:

GET /bank/_search
{
  "query": { "match": { "address": "mill lane" } }
}

To construct more complex queries, you can use a bool query to combine multiple query criteria. You can designate criteria as required (must match), desirable (should match), or undesirable (must not match).

For example, the following request searches the bank index for accounts that belong to customers who are 39 years old, but excludes anyone who lives in Pennsylvania (PA):

GET /bank/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "age": "39" } }
      ],
      "must_not": [
        { "match": { "state": "PA" } }
      ]
    }
  }
}

Instead of constructing and submitting REST requests directly to Elasticsearch, you can use Discover to search and filter your data, get information about the structure of the fields, and display your findings.

Kibana requires a data view in order to access the Elasticsearch data that you want to explore. A data view selects the data to use, and allows you to define properties of the fields.

A data view can point to one or more indices, data streams, or index aliases. For example, a data view can point to your log data from yesterday, or all indices that contain your data.

In order to use Discover and many more of the features and tools available in the Elastic Stack your data should be associated with a data view.

You can create visualizations and build dashboards in Kibana to understand your data and share information.

Learn more about Elasticsearch

Learn more about Kibana

Learn about Elastic solutions