Meet our first Elastic Certified Engineer — Hiroshi Yoshioka from Acroquest.

<html><head></head><body>We recently launched our first Elastic professional certification &#x2014; <a href="/training/certification">Elastic Certified Engineer.</a> It&#x2019;s a rigorous, performance-based exam that challenges people on a full range of Elasticsearch skills across operations, development, and more. We were excited to launch the exam, but even more excited when we had our first Elastic Certified Engineers &#x2014; <a href="https://twitter.com/Hirosh_Yoshioka">Hiroshi Yoshioka</a> from <a href="http://www.acroquest.co.jp/">Acroquest Technology</a> (and his colleague Shin Higuchi &#x2014; the fourth person certified). So to celebrate this accomplishment, we recently caught up with Hiro to talk with him about the certification, his experience preparing for the exam, and how being an Elastic Certified Engineer means more than just a badge on LinkedIn or sticker for your laptop.
Note: The following has been adapted from the original Japanese interview.
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<figcaption>Hiroshi Yoshioka (left) and Shin Higuchi</figcaption>Congratulations, Hiro! How did you feel when you heard you were the first Elastic Certified Engineer in the world?
Hiro: I was so excited and I felt so proud! Becoming an Elastic Certified Engineer means a lot to me. It shows that Elastic sees me as an Elasticsearch expert &#x2014; with the elite skills to provide appropriate solutions across a wide variety of use cases.
Am I really the first one in the world to get the certification? That&#x2019;s really exciting and hard to believe!
Tell us a little about Acroquest and your primary role at the company.
Hiro: I&#x2019;m a Senior Consultant, Data Analytics engineer at Acroquest. Much of my daily role is architecting the Elastic Stack deployment, building Kibana dashboards, designing the machine learning jobs, and setting up alerts for my clients.
Acroquest is a solution integrator that delivers big data visualization and analytics consulting services covering electric power, mobile technology, automobile, and other industries. We have been addressing the needs of these industries and implementing solutions for them by utilizing the scalability and the real-time analytics capability of the Elastic Stack as our platform.
Why did you decide to get officially certified in Elasticsearch?
Hiro: It&#x2019;s an important factor giving a feeling of security to customers considering using Elasticsearch. My mission is to deliver the best Elastic solution to customers who expect big data analysis/visualization. There are many engineers who are using Elastic, but not all of them can solve customer issues with the best combination of Elastic functionality. Being an Elastic Certified Engineer means I&#x2019;m an Elastic expert who has enough skill to be able to give the best solutions.
Now that I&#x2019;m certified, I look forward to our customers knowing they chose the best solution provider. And hopefully they will decide to use Elastic much more!
How did you prepare for the certification exam?
Hiro: When I started the process I thought I was already an Elasticsearch expert. I had learned from Elastic webinars and by using the documentation as a reference. First, I learned visualizations with Kibana, then data processing and inputs with Logstash. After that, I expanded my vision to Elasticsearch, eventually moving onto functions such as search, aggregation, Watcher, and machine learning.
But once I took the Elasticsearch Engineer I and II training courses, I realized that I had much more to learn. The training courses gave me many hints for understanding certain fundamentals I was lacking. We were taught these courses by Jun Ohtani, one of Elastic&#x2019;s Community Engineers, who has extensive experience in the search industry. We were fortunate to learn practical and relevant skills from him in order to help prepare for the exam. After the training, I spent more time reading the official documentation to review Elasticsearch functions such as index and cluster management, search, and aggregation.
What was the exam like?
Hiro: The exam was not an examination of knowledge, but of how well you can implement Elasticsearch as a solution. There is a use case, and then you are asked if you can solve the problem by combining the different functions of Elasticsearch. It is a very practical exam, and its contents can be utilized to solve real user needs.
Additionally, the monitoring via web camera while under examination was more severe than expected! The test proctor paid attention to slight movement of eyes and mouth. I felt it was evaluated quite strictly!
Would you recommend other engineers who work with Elastic take the exam?
Hiro: Certainly! Fearlessly leveraging the power of Elasticsearch is a mandatory skill in the modern IT industry. Engineers who have such fundamental skills will be welcomed and valuable in many industries. I am the first certified engineer in Acroquest, but my colleague, Shin, also recently achieved his Elastic Certified Engineer credential. Some other engineers are following in our footsteps and taking the exam, too. I hope we can encourage other Elasticsearch engineers to take the certification exam, whether they are from Japan or other countries around the world.
Speaking of fearless, you signed up for the exam only two days after it became available!
Hiro: I had been thinking of attempting this certification since I heard the announcement at Elastic{ON} 2018 &#x2014; so those were four long months of waiting! When I was notified of the opening of the examination, I enrolled immediately. It&#x2019;s a rare experience in my life that I&#x2019;m excited to take an exam.
Yes, you were asking us when to start quite often. To wrap up, what advice would you give to someone that wants to become an Elastic Certified Engineer?
Hiro: You should know that the Elastic Certified Engineer exam isn&apos;t just a vendor certification, but real problem solving simulation. While preparing, study the many different use cases that Elasticsearch covers to get a deeper understanding. It will help both the exam and your daily business.
I believe this certification leads everyone to acquire further skills. I hope many people are up to challenge.
<hr>We thank Hiro and Shin for inspiring us and other Elasticsearch experts with their journey and excitement for the certification program. The number of <a href="/use-cases">use cases</a> our customers address is constantly growing and we continue to enjoy helping support your success. To see the specifics and start your own journey to becoming an Elastic Certified Engineer, visit our <a href="/training/certification">Certification page</a>.
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Celebrating the first Elastic Certified Engineer

<html><head></head><body><h2>INTRO</h2>The Elastic Stack is fantastic for analysing time based data, most of which we currently see coming from operational requirements around things like system load, memory or disk use, network flows, application error rates and so on.
However, the awesome thing about the stack is that it isn&apos;t tied to time based data consisting of logs or metrics. Here we&apos;ll take a look at earthquake data from the Northern California Earthquake Data Center, via the <a href="http://www.ncedc.org/anss/catalog-search.html">ANSS Composite Catalog Search</a>, this data set includes naturally occurring quakes, as well as man-made ones from nuclear blasts and quarry explosions.
You may not be shaking in your boots with excitement yet, but we&apos;ll show a few neat tricks around leveraging custom maps, <a href="https://github.com/elastic/timelion">Timelion</a> visualisations and other tips to increase usability for your users and to give things a bit of extra shine.
<h2>FIRST STEPS</h2>To get this dataset into Elasticsearch we built a very simple Logstash configuration with the CSV, mutate and date filters. We&apos;ve also created mappings for the fields in the data and converted that into a template. If you want to play along at home, then head over to <a href="https://github.com/elastic/examples/tree/master/Exploring%20Public%20Datasets/earthquakes">this Github repo</a> and either clone it or download the files, then follow the instructions to get the data loaded. The dashboards are all provided, but we&apos;ll dig into the bits that make this an interesting use case throughout this post, so don&apos;t go anywhere!
<h2>VISUALISING WITH YOUR EYEBALLS</h2><img src="https://api.contentstack.io/v2/assets/579a9f665401211f166d2e36/download?uid=blta243470bf998ac55" alt="Dashboard" title="">
When you open the main Earthquake dashboard (as seen above), you can see we&apos;ve broken down the data into different groupings, which are also separate dashboards themselves. These are based on Hot Areas around the world, Catalogues, or archives, and finally, by a few specific Quarries and Nuclear Blasts.
This is a basic <a href="https://en.wikipedia.org/wiki/Markdown">markdown</a> visualisation that&apos;s a handy way for introducing your data to your users. Rather than letting them click around and figuring out what the data is all about, you can help frame their discovery process by creating dashboards that link to specific subsets of your data. It&apos;s also a great way to introduce concepts such as filters and the ability to build multiple dashboards with different visualisations for different audiences, but all based on the one dataset.
One tip we also really like in markdown boxes like this, is having a simple &quot;return to home&quot; or &quot;reset time frame&quot; link that can help if someone gets lost or makes changes they didn&apos;t want. This helps with time based data, as you can have a link to the dashboard with the last 5/10/100 minutes/months/years without them having to jump into the timepicker. We&apos;ve called our reset link Go back to the world map. and it&apos;s at the bottom of markdown box.
<img src="https://api.contentstack.io/v2/assets/579a9fa55401211f166d2e3d/download?uid=bltdc84b4e4673eeb06" alt="Navigation - Go back to the world map" title="">
Moving on, and next to that we have a heat map of all the events, which has been populated thanks to the inclusion of latitude and longitude in the dataset, that was then converted to a single <code>location</code> field and then mapped to a <code>geo_point</code> using the Elasticsearch template. Note that this is a standard Kibana heatmap, we&apos;ll touch on some custom maps goodness later.
Then we have a few high level metrics, a histogram of all the magnitudes for the given time period, a breakdown of quake type (natural or man made) and then <a href="http://www.ncedc.org/anss/anss-catalog-source-codes.html">the source code</a> (ie the network operator code for the detectors) of each of these.
Finally we have a bar chart showing a date histogram of all the events for each month, with a count of each magnitude, and below that a Timelion plot showing the average depth of the quakes, broken down per-week as dots, with a moving average of those depths over the past 3 months as a line graph.
Let&apos;s look at all of this in more detail.
<h2>SECOND STEPS</h2>Now we will open up the Earthquake - Japan dashboard, you can do that by clicking Japan under the Hot Areas links, or finding the dashboard using the Load Saved Dashboard button on the top right of the toolbar.
<img src="https://api.contentstack.io/v2/assets/579aa0365401211f166d2e5c/download?uid=blt66f3688d2a2ea528" alt="Hot Areas - Japan" title="">
The first thing we notice is that a saved filter has been applied, the little green box labelled &quot;Japan Territorial Water&quot;. Interestingly. this is actually a <code>geo_polygon</code> query that isn&apos;t currently exposed natively in Kibana, luckily for us, Kibana does allow us to specify custom queries to pass to Elasticsearch and then have applied in our dashboard. If you mouse over the box and click the Edit button, the one that looks like a pencil and is the furthest icon on the right of the filter, you can see how this has been set up as a <code>geo_polygon</code> filter, with the points being an area that contains Japan&apos;s territorial waters.
As we are only looking at data points within the general Japan region, as defined by our polygon, the other visualisations also change the values that they report. This is one of the great features of Kibana as it automatically puts all of the data we are looking at in the same point of reference. If you compare the average depth of quakes in Japan to all events worldwide from the previous dashboard, it is nearly 300% more than the average and the quakes that strike the region are also usually a magnitude 4.
People sometimes think Kibana visualizations are static, but most of them are capable of running queries and aggregations dynamically on the dashboard. Let&apos;s zoom to around 2010 to the end of 2011.
<img src="https://api.contentstack.io/v2/assets/579aa057d37c77e715de3659/download?uid=bltae84a03c4e6b0883" alt="Change the time window from the chart" title="">
What is also obvious here is a spike in quakes from December 2010 through to July 2011, as we know this is when the T&#x14D;hoku earthquake and tsunami occurred. Moving onto the Timelion visualisation, the average depth of the quakes around this time are also very consistent and relatively shallow, which was why the event was so devastating.
<img src="https://api.contentstack.io/v2/assets/579eab5755948bb119f0f27b/download?uid=bltc0efe41048aa082e" alt="Charts of Timelion" title="">
The last few visualisations, which are also from Timelion, show a number of interesting statistics that are taken dynamically from the World Bank API. This is another neat feature that Timelion has and you can read more on it here. By mousing over the any of the Timelion graphs we can see a horizontal line showing the current time period we are over, this line is carried over to all other Timelion visualisations on the dashboard, which lets us quickly draw correlations between the data.
After the financial crisis happened in 2008, the GDP grown of Japan was in the recovery but the trial was broken and the direct investment finally became minus in 2011. Opposite to the situation, Japanese Yen was strong at that time.
<h2>DOWN AND DIRTY WITH MAPS</h2>Those with sharp eyes may have noticed that the graphs in the Earthquake - Japan dashboard are different to the main Earthquake dashboard. Here we have leveraged the ability for Kibana to import WMS compliant maps from other sources and taken something from <a href="http://www.gebco.net">GEBCO</a>.
We have used this map server as when we display the heatmap on top of the supplied bathymetric chart, you can very easily find out the connection between the earthquakes and oceanic trenches.
The dashboard can be used not only for showing statistics and exploring the data, but also recording the event happened in the past. In the &quot;Catalogues&quot; section of the dashboard, we have put some historical tragedies caused by earthquakes. Click on Loma Prieta, 1989 under the &quot;Catalogues&quot;. According to <a href="https://en.wikipedia.org/wiki/1989_Loma_Prieta_earthquake">Wikipedia</a>, &quot;The 1989 Loma Prieta earthquake occurred in Northern California on October 17 at 5:04 p.m. local time. The shock was centered in The Forest of Nisene Marks State Park approximately 10 mi (16 km) northeast of Santa Cruz on a section of the San Andreas Fault System and was named for the nearby Loma Prieta Peak in the Santa Cruz Mountains.&quot; Let&apos;s locate where it happens. We know the maximal magnitude of the earthquake is about 7 so type <code>mag:[7 TO *]</code> into the query area of Kibana. Now a blue dot appears on the center of the map which is the epicenter of the biggest earthquake.
<img src="https://api.contentstack.io/v2/assets/579aa0a14276ce2c214223c8/download?uid=blte478eaaf0bb7a021" alt="Loma Prieta" title="">
We sometimes find something unexpected while exploring public datasets. When we were looking at man made quakes, typically quarries and blasts, in Nevada states, we found some hot spots on the north west of Las Vegas where could be considered as nuclear bomb testing sites. Those were quite often from the 60&apos;s to the 80&apos;s. 
	We also found two blue dots in Henderson city which shouldn&apos;t be nuclear explosions. According to the timeline, those blasts happened on May 4th 1988. As We searched &quot;henderson explosion may 4 1988&quot; on Google, it seems those are the evidences of <a href="https://en.wikipedia.org/wiki/PEPCON_disaster">the PEPCON disaster</a>. Two of those explosions are huge as equal to magnitude 3 earthquakes.
<img src="https://api.contentstack.io/v2/assets/579aa0b95401211f166d2e82/download?uid=blt0ffd684a66ecc806" alt="Nevada State" title="">
<h2>OUTRO</h2>Hopefully we have given you a few good tips for getting the most from Kibana. Whether it&apos;s as simple as adding a markdown box with some intro text and links to other dashboards to help guide your users, integrating Timelion visualisations showing correlation of data from external sources against the data in Elasticsearch, or getting really advanced and importing your own maps.
As a reminder, you can find the dataset, Logstash configs, Elasticsearch mappings and Kibana dashboards at this&#xA0;<a href="https://github.com/elastic/examples/tree/master/Exploring%20Public%20Datasets/earthquakes">Examples GitHub Repo</a> - so please check it out, see how the methods we have talked about in the blog post were applied, and leverage it to make something awesome of your own.
The really cool part about all of what we have looked at here; If you&apos;re someone like the United States Geological Survey (USGS), you can ingest tweets in real time to detect when earthquakes have hit regions that may not have adequate detection systems, and then build insightful dashboards to visual that data! To see more of how that works for them, watch their fantastic talk from Elastic{ON} 2015 <a href="https://www.elastic.co/elasticon/2015/sf/tectonic-shifts-tweets-and-tactical-data-analysis">here</a>.
If you have further questions or comments about any of this, please head over to our <a href="https://discuss.elastic.co">Discuss forums</a>, or hit us up on Twitter via @elastic
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Earthquake data with the Elastic Stack

<a href="https://www.elastic.co/blog/get-hosted-elasticsearch-on-your-aws-bill-with-elastic-cloud">March 30, 2017 Update</a>: Elastic Cloud (<a href="https://aws.amazon.com/marketplace/pp/B01N6YCISK?ref=_ptnr_elastic_fb_blog_esawsmp">hosted Elasticsearch) on AWS</a> can now be added directly&nbsp;your AWS bill through the <a href="https://aws.amazon.com/marketplace/pp/B01N6YCISK?ref=_ptnr_elastic_fb_blog_esawsmp">marketplace</a>. If you are looking for hosted &amp; managed Elasticsearch, you can <a href="https://www.elastic.co/cloud/as-a-service/signup">try Elastic Cloud</a> for 14-days at no cost. For deploying and managing yourself on AWS EC2, this is the right article for you:<h2>Part I - Provisioning EC2 Instances</h2>Want to learn more about the differences between the Amazon Elasticsearch Service and our official Elasticsearch Service? Visit our <a href="/aws-elasticsearch-service#full-comparison">AWS Elasticsearch</a> comparison page.We often talk to customers running Elasticsearch clusters on Amazon Web Services (AWS). AWS is a convenient way to provision and scale machine resources in response to changing business requirements. Elasticsearch takes advantage of EC2's on-demand machine architecture enabling the addition and removal of EC2 instances and corresponding Elasticsearch nodes as capacity and performance requirements change.In this article we will show you how to deploy <a href="/blog/elasticsearch-2-3-3-released">Elasticsearch 2.3.3</a> on Amazon EC2. In this example we will configure a three node Elasticsearch cluster.<h3>Step 1: Choose an Amazon Machine Image (AMI)</h3>Elasticsearch runs on various operating systems such as CentOS, Redhat, Ubuntu, and Amazon Linux. We suggest using the latest Amazon&nbsp;Linux AMI — "Amazon Linux AMI 2016.03.0 (HVM), SSD Volume Type".<img src="https://api.contentstack.io/v2/assets/576b089c1f82392051463860/download?uid=blt9963de9b246441e3" data-sys-asset-uid="blt9963de9b246441e3" alt="Choose an Amazon Machine Image (AMI).png" data-image="tpzk8so0uj9z"/><h3>Step 2: Choose an Instance Type</h3>A reasonable starting instance type is m3.2xlarge which provides 8 vCPUs, 30 GiB of memory, 2 x 80 GB SSD drives and comes with High Network Performance. Solid State Drives are preferred as indexing is IO intensive and High Network Performance is essential for cluster performance and reliability.M3.2xlarge is a baseline recommendation. To determine whether it is an appropriate choice, you should <a href="/blog/announcing-rally-benchmarking-for-elasticsearch">benchmark your solution</a> to determine whether it meets performance and scaling requirements.Click the "Next: Configure Instance Details" button.<img src="https://api.contentstack.io/v2/assets/576b088e1f82392051463854/download?uid=blt15d7ca9b0dac952f" data-sys-asset-uid="blt15d7ca9b0dac952f" alt="Choose an Instance Type.png" data-image="30nvr4jqsbaw"/><h3>Step 3: Configure Instance Details</h3>Each Elasticsearch node will run on its own dedicated EC2 instance, so set the number of instances to 3.Note that any AWS accounts that have been created after December 4, 2013 only support EC2-VPC, so the "Network" option for picking "Launch into EC2-Classic" won't be available for those users and should not be enabled anyway.Selecting "Enable termination protection" is a good idea as it prevents accidental deletion of nodes and their data.Leave the default values for remaining fields and click the "Next: Add Storage" button.<img src="https://api.contentstack.io/v2/assets/576b09691f8239205146387f/download?uid=blt670618f63168180d" data-sys-asset-uid="blt670618f63168180d" alt="Configure Instance Details.png" data-image="k6avd8x47tes"/><h3>Step 4: Add Storage</h3>Let's leave the storage Size at 8 GiB. If you happen to know your index storage requirements at this time, you can adjust the storage now. Leave the Volume Type set to General Purpose SSD.Click the "Next: Tag Instance" button.<img src="https://api.contentstack.io/v2/assets/576b089685453e6f4e3b4cb4/download?uid=blt661a2c609d1404bd" data-sys-asset-uid="blt661a2c609d1404bd" alt="Add Storage.png" data-image="cgztngs2si4j"/><h3>Step 5: Tag Instance</h3>In this field, provide a key and value pair, for example "name" and "esonaws", to make it easy to recall the ec2 instances.Click the "Next: Configure Security Group" button.<img src="https://api.contentstack.io/v2/assets/576b083b173b7f5d5060324c/download?uid=blt09612d14527f408b" data-sys-asset-uid="blt09612d14527f408b" alt="Step 5 Tag Instance.png" data-image="8wv78nupahrq"/><h3>Step 6: Configure Security Group</h3>This configuration panel allows you to configure a set of firewall rules for accessing your instance. By default, Elasticsearch exposes TCP port 9200 for REST API access and TCP port 9300 for internal cluster communication. Consider adding rules to allow connecting to TCP port 9200 from desired subnets, typically private subnets, and TCP port 9300 from the subnets where Elasticsearch nodes live. If you plan to change the default port settings in elasticsearch.yml, configure rules for those ports rather than TCP ports 9200 and 9300.Also, add a rule to allow SSH connections on port 22, so you can connect to the instance in the later steps.Click The "Review and Launch" button.<img src="https://api.contentstack.io/v2/assets/576b085685453e6f4e3b4ca3/download?uid=bltb5d9899fe69a7840" data-sys-asset-uid="bltb5d9899fe69a7840" alt="Configure Security Group.png" data-image="ndo0u0se0rzm"/><h3>Step 7: Review Instance Launch</h3>Note any warnings and review the Instance Launch settings and click the "Launch" button when ready.<img src="https://api.contentstack.io/v2/assets/576b08481f8239205146383c/download?uid=bltc258259f2721b723" data-sys-asset-uid="bltc258259f2721b723" alt="Review Instance Launch.png" data-image="rkcdayco7yh9"/>At this point you will be prompted to provide a key pair or create a new key pair.<img src="https://api.contentstack.io/v2/assets/576b084285453e6f4e3b4c90/download?uid=blt795f4d51585c445f" data-sys-asset-uid="blt795f4d51585c445f" alt="Select Key Pair.png" data-image="5je0e1g63tsa"/>This is necessary to enable SSH access to the EC2 instance. If you need help setting up a key pair the <a href="http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html" target="_blank">Amazon EC2 Key Pairs</a> article provides an overview and instructions for creating new new key pairs.Start up the EC2 instances and take note of the assigned private IP addresses which we will use in a following step.<h2>Part II - Installing Elasticsearch</h2><h3>RPM</h3>Log into each EC2 instance via SSH.<pre class="prettyprint">$ ssh -v -i /pathto/[certfilename].pem ec2-user@[ec2hostname] </pre>Then install the Elasticsearch RPM package on each EC2 instance as instructed below.<pre class="prettyprint">$ sudo rpm -i https://download.elastic.co/elasticsearch/release/org/elasticsearch/distribution/rpm/elasticsearch/2.3.3/elasticsearch-2.3.3.rpm </pre>Other versions of Elasticsearch are available <a href="/downloads/elasticsearch">here</a>. Refer to <a href="https://www.elastic.co/guide/en/elasticsearch/reference/5.2/install-elasticsearch.html">the guide</a> if you prefer installing with yum.Register Elasticsearch as a system service.<pre class="prettyprint">$ sudo chkconfig --add elasticsearch </pre><h3>Install Plugins</h3>You need to install&nbsp;AWS cloud plugin on each EC2 instance in the cluster.<pre class="prettyprint">$ cd /usr/share/elasticsearch/ $ sudo bin/plugin install cloud-aws </pre>If there are any additional plugins you need, such as Marvel, for monitoring, or ICU, for additional language support, now is a good time to install them.<h3>Configure Elasticsearch</h3>The maximum JVM heap size should be based upon the machine's memory. Open "/etc/sysconfig/elasticsearch" on each EC2 instance with your favorite editor and set the "ES_HEAP_SIZE" and "MAX_LOCKED_MEMORY" parameters. The following configuration will fit a m3.2xlarge instance. The "ES_HEAP_SIZE" is recommended to be half of the memory but not more than 32GB.<pre class="prettyprint">ES_HEAP_SIZE=15g MAX_LOCKED_MEMORY=unlimited </pre>Open "/etc/elasticsearch/elasticsearch.yml" on every machine and edit the following settings.<pre class="prettyprint">cluster.name:esonaws bootstrap.mlockall: true discovery.zen.ping.unicast.hosts: [_ip_address_,…] network.host: [_ip_address_] </pre>"discovery.zen.ping.unicast.hosts" is a list of EC2 instance private IP addresses. All the master-eligible nodes must be listed. In a small cluster all nodes can be configured as both master nodes and data nodes. "network.host" is the EC2 instance private&nbsp;IP address&nbsp;of&nbsp;this host that is shared with the&nbsp;other nodes in the cluster.The IP address is not required for a single node cluster. "_site_" and "_local_" represent the private address and the local loopback address "127.0.0.1" and allow access to those from remote.<h3>Starting Up and Verification</h3>If you are setting up multiple Elasticsearch nodes, they must all be the same version, same plugins and equivalent configurations. Start up Elasticsearch on each EC2 instance.<pre class="prettyprint">$ sudo service elasticsearch start </pre>Once sta;d, let's verify the Elasticsearch cluster by using curl to request the cluster state.<pre class="prettyprint">$ curl localhost:9200/_cluster/health?pretty </pre>If successful, the "status" will be "green" (or "yellow" for a single node). The "number_of_nodes" should be the same number of nodes started. Depending on your index settings,&nbsp;you will need a minimum of two nodes for the cluster "status" to turn "green". A minimum of 3 nodes is recommended to avoid leader election conflicts.&nbsp;If "status" isn't "green" you can also check the Security Group configuration or check logs under "/var/log/elasticsearch" for errors.<pre class="prettyprint">{ “cluster_name” : “esonaws”, “status” : “green”, “timed_out” : false, “number_of_nodes” : 3, “number_of_data_nodes” : 3, “active_primary_shards” : 8, “active_shards” : 16, “relocating_shards” : 0, “initializing_shards” : 0, “unassigned_shards” : 0, “delayed_unassigned_shards” : 0, “number_of_pending_tasks” : 0, “number_of_in_flight_fetch” : 0, “task_max_waiting_in_queue_millis” : 0, “active_shards_percent_as_number” : 100.0 } </pre>Your Elasticsearch cluster is ready!<h2>Summary</h2>Deploying an Elasticsearch cluster on Amazon EC2 is relatively easy, but it does require a number of configuration steps, familiarity with SSH, key pair management, and also assumes that you will be managing the machines.If you prefer the ease-of-use of a managed service, <a href="/cloud">Elastic Cloud</a>, Elastic's official hosted Elasticsearch and Kibana offering on AWS, is a great choice. You can spin up a cluster in just a few clicks. <a href="/cloud">Elastic Cloud</a> also comes with Security, Kibana, supported Plugins, on-demand cluster scaling, automatic version backup and more.<img src="https://api.contentstack.io/v2/assets/576b08501f82392051463842/download?uid=blt16e32e38151bfbcb" data-sys-asset-uid="blt16e32e38151bfbcb" alt="elastic cloud is easy vi.gif" data-image="a8r3ea2idlwa"/>The <a href="/cloud/signup">Elastic Cloud trial</a> is free and doesn't require a credit card. Here's a link to a&nbsp;short video that describes <a href="https://www.youtube.com/watch?v=D743DdFmon0" target="_blank">Elastic Cloud</a> in a little more detail. You can also <a href="https://www.elastic.co/webinars/getting-started-with-elastic-cloud-with-gcp-and-aws" target="_self">get started with Elastic Cloud on GCP and AWS</a>.

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Solutions Architect

Kosho Owa

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