Learn how one of Canada’s largest telecommunications companies uses Elastic to drive improved security analysis.

This post is a recap of a community talk given at a recent <a href="/elasticon">Elastic{ON}</a>&nbsp;Tour event. Interested in seeing more talks like this? Check out the <a href="/elasticon/tour/2019">Elastic{ON} Tour</a>&nbsp;page to see when a stop is coming to a city near you.
Bell Canada, one of Canada’s largest telecommunications companies, offers mobile phone, television, internet, and landline services to big corporations, small and medium-sized businesses, and individuals across the country. Bell Canada’s security operations center (SOC) covers every Bell office and business unit coast to coast and they rely on logs to detect cybersecurity threats.
Sylvain Proulx, Bell Canada’s Senior Security Manager, says the business units — like Bell TV, Bell Internet, Bell Media, or Bell Mobility — that deliver services to their customers all use different technologies and applications, so the logs they collect are diverse and uncommon. Logs come from routers, firewalls, web logs, OS logs, application logs, and many other devices, some of which get ‘chatty’ and generate a lot of data.
The SOC had performed log and event correlation and incident response and reporting using only an ArcSight Security Information and Event Management (SIEM) solution. But over time, as the volume of logs increased, normalizing many new types of logs from a variety of devices bogged down the system. Their SIEM solution also provided only rule-based detection with no machine learning, so it generated a high ratio of false-positive incidents, which threatened to alert-fatigue their analysts.
Proulx said they’d hit their SIEM’s limit. They found no single vendor solution that would let them ingest more data faster, build threat detection models, and normalize many new types of logs while also retaining ownership of their data. So, the SOC got to work augmenting their ArcSight SIEM with tools like the Elastic Stack to handle high log volume and traffic spikes automatically and generate meaningful security data that wouldn’t overwhelm analysts.
Bell Canada gets data from bare metal servers, virtual machines, and, increasingly, from container infrastructure&nbsp;with&nbsp;Docker and Kubernetes. They needed a log shipper that was simple, lightweight, and straightforward to automate, so they turned to <a href="/products/beats">Beats</a>. They use <a href="/products/beats/filebeat">Filebeat</a> and <a href="/products/beats/winlogbeat">Winlogbeat</a> to ship logs because they’re easy to configure, test, and deploy. Plus, they can version control their configurations and there is no loss of data in case of a network outage.
After the data is queued in Kafka, the SOC must parse and normalize their logs in all their various formats in order to perform security analysis. Running <a href="/products/logstash">Logstash</a> instances on OpenShift has helped them scale quickly and automatically in case of traffic spikes without dropping logs, and it consumes less resources than multiple virtual machines. An additional advantage they’ve found to having Logstash in a container is that they can easily run it through RSpec for testing before moving to production.
Once the logs are normalized, the SOC stores them in <a href="/products/elasticsearch">Elasticsearch</a>. Bell Canada’s previous solution was unable to handle increasing log volumes and scale without losing logs. The SOC now does this with Elasticsearch, which allows them to scale quickly and horizontally, making&nbsp;their job a lot easier.
The day events are logged, the SOC searches the data with multiple queries and processes, which puts a heavy load on the cluster, so they’ve implemented a hot-warm architecture with automated deployment of new nodes. The beefier nodes are ingesting and being searched constantly, but when the logs lose their value, they’re shipped to warm nodes for aggregation and lighter analysis. “If you lose a node in Elasticsearch, you can still keep working. Not a problem. You can fix it later,” says Mathew Vandystadt, Bell Canada’s Security Specialist Software Engineer.
Securing data is a top priority for the SOC. Role-based access control (RBAC) is a must, but RBAC can be painful to manage. With the <a href="/products/stack/security">security features of the Elastic Stack</a>, the SOC has control over who has access to the data, they can add a layer of encryption over data transportation, and they can easily perform RBAC management — and it also ties in easily with their existing LDAP, meaning they don’t need to spend extra time redefining group roles and can focus on their security mandate.
Once they have their data where they want it, the SOC analysts can use it to find security incidents — and good visualization is key. <a href="/products/kibana">Kibana</a>’s straightforward interface means their busy specialists don’t spend a lot of extra time learning new query languages.
<blockquote>“[Kibana] works great in our use cases. It’s simple. We don’t have to do a lot of training and we get a nice visualization.” Mathew Vandystadt, Security Specialist Software Engineer | Bell Canada
</blockquote>Bell Canada found a flexible alerting solution for rule-based detection, but their analysts also needed smart detection that works with different algorithms, so they developed in-house machine learning with open source, ML-centric libraries. Their containers in OpenShift let them easily spin Python containers tied to Kafka or Elasticsearch so all data is accessible. Then they use their ArcSight SIEM for event aggregation and correlation to get a higher ratio of true positives to false positives, sparing analysts from alert bombardment.
They’ve built this whole pipeline using different software from different vendors, made possible because Elastic allows for simple integration with open security protocols. In the future, the SOC plans to merge a Cyber Threat Intelligence platform with their new security architecture. “Having that infrastructure right there allows us to do more than we were a year and a half ago with only the ArcSight solution,” says Proulx.
Ready to learn more about the team’s tools and pipeline? <a href="/elasticon/tour/2018/toronto/security-events-logging-bell-canada">Watch this October 2018 Elastic{ON}Tour presentation</a> and discover how they accomplish their security mission, including how Bell Canada handles long-term log retention and fast forensic data retrieval without using public clouds.
<a href="/elasticon/tour/2018/toronto/security-events-logging-bell-canada"><img src="https://images.contentstack.io/v3/assets/bltf7afce26b89a5b33/bltc586b844830b7d7f/5c66ddb4f6d704c65fb28add/download" data-sys-asset-uid="bltc586b844830b7d7f" alt="Bell Canada is using Elastic to drive improved security analysis in their SOC." "=""></a>

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Security Events Logging at Bell Canada

Learn how AmericanAncestors.org worked with the Elastic support team to boost performance and deliver a highly scalable service with minimal overhead.

<html><head></head><body>This post is a recap of a community talk given at a recent <a href="/elasticon">Elastic{ON} Tour</a> event. Interested in seeing more talks like this? Check out the conference archive or find out when the <a href="/elasticon">Elastic{ON} Tour</a> is coming to a city near you. 
<a href="https://www.americanancestors.org">AmericanAncestors.org</a>, the website of the nonprofit New England Historical Genealogical Society, is one of the leading online family history research sites in the United States. They maintain one terabyte of data in <a href="/cloud/elasticsearch-service">Elastic&#x2019;s Elasticsearch Service</a> that their more than 250,000 members can search in real time, including over 1.4 billion names and an electronic collection of over 40 million pages of centuries-old, handwritten documents.
The average AmericanAncestors.org member is sixty-five and retired. They have names and locations to research and pages of elegant, faded quill script to decipher. They don&#x2019;t want to fiddle with a slow, rigid search feature. They want intelligent search that delivers results fast.
Before switching to Elasticsearch, AmericanAncestors.org had used SQL for their search capability, but that solution didn&#x2019;t have the flexibility their users needed. When Don LeClair, Associate Director of Database Search and Systems, started at AmericanAncestors.org in 2016, they had been running Elasticsearch 1.3 on five physical servers set up by external contractors. A year later, with those contractors long gone, their clusters were no longer optimized, search was slow, and the environment was unstable. LeClair was learning Elastic on the job and, unfortunately, becoming proficient at full cluster restarts.
LeClair and AmericanAncestors.org needed to increase availability and boost performance, but they also wanted to add expertise and reduce system administration time and costs, so they purchased a Platinum subscription to the Elasticsearch Service. Additionally, with their new subscription, they were able to implement <a href="/products/stack/security">Elasticsearch security</a> definitions so queries would come through with appropriate authentication. Another major advantage they found in choosing the Elasticsearch Service was they could create a separate cluster to monitor production and visualize the data in Kibana.
<blockquote>&#x201C;For the first time we had visibility and facts about how the system was performing rather than opinions.&#x201D;&#xA0; Don LeClair,&#xA0;Associate Director of Database Search and Systems | AmericanAncestors.org
</blockquote>Once the clusters were optimized, LeClair was able to turn his attention to new challenges. The genealogy research industry was trending toward search that offered suggestions. At the time, AmericanAncestors.org&#x2019;s approach to search was that results must fit search criteria, but that led to many frustrating member searches that delivered no results.
So they changed to a best fit results approach with prioritized rankings. This change improved the user experience, but also increased the server workload. And that workload increased even more dramatically after <a href="https://www.csoonline.com/article/3247868/vulnerabilities/spectre-and-meltdown-explained-what-they-are-how-they-work-whats-at-risk.html">Spectre and Meltdown</a> vulnerability fixes were applied to their systems, leading to a performance decline and even an outage. With a new optimization challenge to handle, AmericanAncestors.org decided to work closely with Elastic&#x2019;s support team to drive improvements. 
Through this work, LeClair discovered that Elastic support team&#x2019;s ethos went deeper than simply working to help mitigate break/fix issues. The Elastic support team helped identify problem queries, performed code reviews on breaking changes between versions, and altered their sharding strategy to increase speed. Support also identified that over 35% of records in their index were logically deleted documents. With this revelation, AmericanAncestry.org changed tools and processes to avoid excessive re-indexing. They also implemented and maintain a full-data test cluster to simplify upgrade testing and enable them to stay current. The test cluster helps during development so AmericanAncestors.org can roll into production with minimal outage and inconvenience.
In the end, with the help of the Elastic team, they built a highly scalable service with minimal overhead. They reduced disk usage by 60% and CPU by 50%, making search 2-3 times faster. This speed enabled AmericanAncestors.org to offer a major promotion with free signup and search for three days. The system handled a 400% traffic increase with excellent response time. &#x201C;It was &#x2026; perfect,&#x201D; said LeClair. &#x201C;In fact, it was so smooth on the second day I stopped watching it on the monitor.&#x201D; Their current solution was dramatically simpler than their previous physical servers, and, even with premium support and a full-data test cluster, it cost 25% less than their old solution.
Learn about how AmericanAncestors.org is leveraging Elasticsearch Service to drastically improve their service and the lessons they learned along the way implementing by watching <a href="/elasticon/tour/2018/boston/elastic-at-nehgs">Scalability &amp; Support Using the Elasticsearch Service: Elastic @ NEHGS</a> from Elastic{ON} Tour Boston.
 
<a href="/elasticon/tour/2018/boston/elastic-at-nehgs"><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt0a1b033b9e9e32b9/5c5837924c9f4feb0b4c5a73/american-ancestors-boston-tour-architecture-thumbnail.png" data-sys-asset-uid="blt0a1b033b9e9e32b9" alt="american-ancestors-boston-tour-architecture-thumbnail.png" "=""></a>
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A Fast, Satisfying Search Experience with AmericanAncestors.org

Terradue uses satellite data and the Elastic Stack to keep an eye on threats from geohazards such as earthquakes, volcanoes, and floods.

<html><head></head><body>Landslides, earthquakes, ground settlement, and floods. Earth is an unquiet planet, and sometimes that&#x2019;s trouble for the life that inhabits it, including humans. But it&#x2019;s hard to predict natural disasters. The best way to protect life and infrastructure from geohazards is to prepare in advance. Post-disaster studies show that mitigation efforts pay off, but scientists need the best data they can get to analyze risk. One place they get this good data is from <a href="https://www.terradue.com/">Terradue</a>.Terradue is a European Space Agency (ESA) spin-off company that specializes in massive processing of Earth science data using Elastic. Emmanuel Mathot, a technical leader at Terradue, <a href="https://www.elastic.co/blog/elastic-earth-science-for-global-monitoring">wrote a blog post last May</a> about their Elasticsearch-powered query engine that helps monitor geohazards from space. Since last year, Terradue has done even more exciting work that Mathot discussed in his March 8, 2017 <a href="https://www.elastic.co/elasticon/conf/2017/sf/advancing-earth-science-with-elasticsearch-at-terradue">presentation at Elastic{ON}</a>.Earth observation satellites orbit the planet 24/7 taking fantastically precise radar images of its surface. &#x201C;This [satellite] instrument is able to deliver data we use to track movements down to a few millimeters,&#x201D; said Emmanuel Mathot. It can even or measure how fast individual buildings settle into the ground, like <a href="https://www.forbes.com/sites/lauriewinkless/2016/12/05/the-sinking-tower-of-san-francisco/#6b08923469de">San Francisco&#x2019;s famously sinking Millennium Tower</a>.As the satellites orbit, they take pictures of the planet&#x2019;s entire surface once every six days. All the images and metadata about satellite position, velocity, and orbital track get indexed into Terradue&#x2019;s Elasticsearch query engine, Mathot&#xA0;explained. This engine provides the best data to monitor ground movement along a fault or near a volcano, or to map flood plains, among other uses. It does this by selecting the most appropriate images to compare to each other by minimizing some parameters within the metadata.One good illustration of this involves earthquakes, specifically <a href="https://www.nytimes.com/2016/08/25/world/europe/italy-earthquake.html?_r=0">the 6.2 magnitude earthquake</a> that struck central Italy in August of 2016. To create the most accurate picture of earthquake-related ground movement, researchers compared the satellite&#x2019;s first post-event image of the disaster area with the best image taken in the months before the earthquake. There are typically around 50 of these pre-event images to pick from and which one is &#x201C;best&#x201D; depends on many factors.To do so, Terradue uses Elasticsearch to compare the satellite&#x2019;s position when it took the post-event picture to its position when it took each the pre-event image. It also analyzes the time span between when the images were taken, the satellite&#x2019;s orbital track and velocity, and other metadata. The query engine compares all of the possible images and selects the best one, and it can do it in about ten seconds.&#x201C;Without this information in Elasticsearch, in our catalog,&#x201D; said Mathot, &#x201C;It&#x2019;s not possible. The user has to [make] assumptions.&#x201D; With these accurate pictures, scientists can locate the fault lines that previously went unnoticed. With the earthquake in Italy, the pictures confirmed Italian scientists&#x2019; field observations that the ground had moved a whopping 70 centimeters. The query engine images offer more knowledge than just ground displacement &#x2014; it can even delineate water movement if you ask the data the right questions. Nigerian scientists have partnered with ESA and Terradue to map flooding along the Niger River in Africa. &#x201C;The impact of the floods on the people and the culture is really critical, and understanding and forecasting the intensity and extent is key to reduce the impact of the flooding,&#x201D; said Mathot. To find patterns, they need an accurate picture of what the river looks like during the rainy and dry seasons over several years.The trouble is, liquid water doesn&#x2019;t show up in images the way the ground and buildings do. But scientists figured out that when the satellite images a patch of ground, rougher surfaces bounce back higher intensity microwave backscatter profiles, while smooth surfaces &#x2014; like water &#x2014; bounce back low intensity backscatter data, all of which is indexed into Elasticsearch. Using this knowledge they query Elasticsearch to find the best reference images of an area prior to flooding and also track the greatest extent of the flooding. As with the earthquake in Italy, the engine can pick the best image in seconds.Terradue compiles these very accurate Earth observation images into one collaborative, <a href="https://tep.eo.esa.int/about-tep">virtual workspace</a> that also contains over 20 years&#x2019; worth of Earth observation data. This workspace is part of a major European Space Agency initiative to provide scientists with a single interface to use this data and interact with an online community of researchers. Terradue uses Elastic software to power three of its seven platforms. This ESA initiative allows scientists across the globe to search and process this wealth of Earth observation data to its full research potential. To learn more about the details of how Terradue is using the Elastic Stack <a href="https://www.elastic.co/elasticon/conf/2017/sf/advancing-earth-science-with-elasticsearch-at-terradue">watch their full presentation</a>.<a href="https://www.elastic.co/elasticon/conf/2017/sf/advancing-earth-science-with-elasticsearch-at-terradue"><img src="https://api.contentstack.io/v2/uploads/58ff5f3364a01ef95e1d0ee2/download?uid=blt4c8cb727f5fbde94" data-sys-asset-uid="blt4c8cb727f5fbde94" alt="terradue.jpg"></a> </body></html>

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Eyes in the Sky: Geohazard Monitoring with Terradue and Elasticsearch

When Ebola struck West Africa, eHealth Africa used the Elastic Stack to collect and search outbreak data with speed and flexibility to outpace the lethal virus.

<html><head></head><body>This is the first in a series of blogs highlighting the <a href="https://www.elastic.co/causeawards">Elastic Cause Award</a> honorees: their projects,&#xA0;teams, and the causes that motivated them. This week we&#x2019;re featuring Adam Thompson, the Executive Director of <a href="https://www.ehealthafrica.org/">eHealth Africa</a>.eHealth Africa&#xA0;designs and implements data-driven solutions and technologies to improve health systems for and with local communities across West Africa. They currently employ nearly 800 staff in Nigeria, Sierra Leone, Liberia, Guinea, the United States, and Germany. Their technology works in low connectivity settings, and smartly uses data to drive decision-making by local governments and partner agencies to get optimum results.The following excerpts are from Thompson during <a href="https://www.elastic.co/elasticon/conf/2017/sf/closing-keynote">the closing keynote at Elastic{ON} 2017</a> and have been edited for length and clarity. Editorial commentary (in bold) is sprinkled in to add context.&#xA0; When Ebola struck West Africa late in 2014, eHealth Africa already had infrastructure in Nigeria and they quickly helped stop the outbreak. Their success there prompted the health ministries in Guinea, Liberia, and Sierra Leone to request their help. &quot;We hit the ground behind. Behind the virus. It was taking one, sometimes two weeks between a suspected case being identified and getting a laboratory confirmation to start contact traces. At the speed Ebola works, we were getting further and further behind all the time.We needed to identify suspected cases, get them quarantined get lab results, and get contact tracing to figure out who the they had contact with over the last several days. That needed to happen in 24 hours. In order to do that we really needed visibility. We only saw the people showing up to the hospitals. We needed to broaden that picture.&quot;They decided to expand the health call lines that existed in the country &#x2014; and they needed expanding.&quot;What we were able to do was expand these health call lines that had mostly been dormant in the countries. They were reactivated early on in the response, but were really kind of limited to one or two people on phones, writing down stuff mostly on paper. And we still weren&#x2019;t getting enough information. And we weren&#x2019;t getting that true picture of the outbreak.So we built a call center application. We have our own software team and we do a lot of work with open source tools. But we looked around and we needed a tool that was going to allow us to record data at a central location with all of those calls from around the region and we needed to distribute that data to the district centers. The centers that were spread around the country where they would get the data, respond to the alerts, and then add in the follow up alerts and the rest of the data. We needed to build a solution.&quot;They started with one NoSQL database solution&#xA0;because of its powerful synchronization features for partially offline architecture, which was important for remote district offices in areas of low connectivity. But it had primitive indexing and reporting. They needed more capability.&quot;We stumbled upon Elasticsearch after a bunch of late night Google queries and realized it was something that we could try to dramatically improve the way that we were searching our data because the database was growing so quickly. We constantly had to refactor and rewrite the application because we had no idea the volume this response was going to take on.&quot;In Sierra Leone, they were reaching 8,000-10,000 calls a day, and in Guinea, 10,000-20,000. People reporting symptoms or deaths. People asking questions.&quot;Elasticsearch allowed us to immediately index all the different facets of the call center. And as we were adding new data elements in, new parts of the response, we could quickly update the indexes and get new reports based on the new elements. Kibana let us set up these visualizations and snapshot in time reports so that every 24 hours our response teams met in the morning and evening, they&#x2019;d get reports on all aspects of the response in real time so they could see a picture &#x2014; the whole picture &#x2014; of the country for the first time in terms of what was going on.&quot;They quickly saw that the outbreak was much bigger than the number of people showing up to treatment centers indicated. Elasticsearch allowed district office staff to filter and search local cases and respond rapidly.&quot;Every single person that called this call center, 24 hours later they got a call back. &apos;Hey, you reported a death, you reported a sickness. Did anyone come? Did anyone show up? If they did show up, did they do these things?&apos;We created a feedback loop for the actual response [teams] themselves so they could quickly iterate through and improve the situation. We went from a two-week response window down to a 24-hour response window in two to three weeks just by constantly reiterating and running through the data and making sure the people making these decisions had access to the real time information.&quot;And the result of tireless work of all the responding agencies informed by real time-data?&quot;Responding to public health emergencies is all about time. And every minute, every second, every day that goes by where you don&#x2019;t have visibility on the response is time you lose and it&#x2019;s lives lost. So, the call center application, the data team, all the people in the responding agencies are really hooked into this application and used this data to drive decision making and drive Ebola out of these three countries.&quot;The call center played a crucial role in curbing the Ebola outbreak in 2014 and currently operates as a universal health emergency number in Sierra Leone. eHealth Africa is currently in the process of determining how the solution they built with Elasticsearch can best be modified to support health systems strengthening in the years to come.<a href="https://www.elastic.co/elasticon/conf/2017/sf/closing-keynote">Watch Thompson&apos;s full presentation</a> from Elastic{ON} 2017 and l<a href="https://www.elastic.co/causeawards">earn more about the Elastic Cause Awards</a>. Banner image credit:&#xA0;<a href="https://phil.cdc.gov/phil/details_linked.asp?pid=10815">CDC / Frederick A. Murphy</a>. </body></html>

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Fighting Ebola with Elasticsearch: Cause Award Honoree eHealth Africa

How Merck is using Elasticsearch to tame a rich, but wild jungle of genetic data and getting it to perform tricks — like predicting disease causes.

<html><head></head><body>The Human Genome Project was supposed to cure all the diseases. At least, that&apos;s what the general public thought back in 2001 when the results of the project were first shared. But scientists suspected all along that uncovering the root of all human disease would be a bit more complicated &#x2014; and they were right. The data could be collected, but making use of it was going to take years of grueling and tedious work.
Daniel Myung and Bhasker Bokuri are two scientific computing experts at <a href="https://www.elastic.co/elasticon/conf/2017/sf/integrating-human-genetic-data-to-help-drive-drug-discovery-elastic-at-merck">Merck Pharmaceuticals who presented at Elastic{ON}</a> on March 9, 2017 about how their innovative approach to managing genomic data with the Elastic Stack opens new vantages for geneticists. Using the Elastic Stack, scientists can see their data in new ways that uncover safer, more effective treatments for diseases like cancer, Alzheimer&#x2019;s, and diabetes.
&#x201C;The pharmaceutical industry still has a pretty low batting average,&#x201D; said Myung. &#x201C;There&#x2019;s all this work and all this effort. It&#x2019;s pretty expensive and there&#x2019;s still this low hit rate of [a product idea] actually being safe and effective.&#x201D; They want to use genetic data to better predict in the discovery phase how a drug will affect the body long before expensive trials. The goal is to speed the production of effective treatments and make drugs cheaper for all.
<blockquote>They want to use genetic data to better predict in the discovery phase how a drug will affect the body long before expensive trials.</blockquote>As sequencing costs have fallen from tens of millions of dollars to around $1,000 to sequence a whole genome, thousands of studies have been pouring in from academia, public sources, and consortium efforts among multiple pharmaceutical companies. The consortium efforts are the source of most of the de-identified, aggregate data in Merck&apos;s&#xA0;database. The problem Myung and Bokuri face is that the data is unwieldy. There is so much of it, it&#x2019;s in multiple formats from diverse sources, and the methods that have produced this 13 years-and-counting&#x2019;s worth of data change every few years. Previous methods of handling this data, as Myung and Bokuri described, were tedious, manual, and required a significant amount of expert integration. It could take two days to add a single field.
Myung and Bokuri have used Elastic products to make a pipeline that ingests and harmonizes the diverse data into a rapidly searchable database and creates a universal coordinate system for genetic variants. The size and the weird shape of their data fit well into Elastic.
<blockquote>The size and the weird shape of their data fit well into Elastic.</blockquote>They approach it like a weather map, but instead of using location, temperature, humidity, pressure, and wind speed data to make weather predictions, they use genetic variant locations, epigenetic effects, phenotype expression, GWAS, and eQTL data to help researchers predict the efficacy of a drug. It&#x2019;s not perfect, just like weather forecasts aren&#x2019;t perfect, but it presents search results in hours that used to take weeks. And it helps researchers find better places to aim their questions and hopefully raise that pharmaceutical batting average.
Interestingly, this method works in retrospect. Myung displayed&#xA0;a graph from the genetic database of the gene targeted by the successful cholesterol drug Lipitor &#x2014; a known pharmaceutical home run.The graph displayed some interesting effects. If geneticists had run a search on this gene using the new Elastic database, they would have seen these effects and recommended this gene as a target for new medications.
The database is promising, but it&#x2019;s not done yet. &#x201C;This is a start,&#x201D; said Myung. &#x201C;We&#x2019;ve put something in a database and you can search things now.&#x201D; Myung and Bokuri are still looking for ways to support the geneticists who make those data-driven decisions. In the future they hope to apply machine learning or statistics to the data.
If you&#x2019;re interested in their&#xA0;approach to working with complex and important data, dive in and <a href="https://www.elastic.co/elasticon/conf/2017/sf/integrating-human-genetic-data-to-help-drive-drug-discovery-elastic-at-merck">check out their Elastic{ON} presentation and slide deck</a>.
 
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AUTHOR

の記事 Emily Mosher

Security Events Logging at Bell Canada

A Fast, Satisfying Search Experience with AmericanAncestors.org

Eyes in the Sky: Geohazard Monitoring with Terradue and Elasticsearch

Fighting Ebola with Elasticsearch: Cause Award Honoree eHealth Africa

Stretching the Elastic Stack to Fit the (Very Large and Complicated) Human Genome

Elastic Cloudの無料トライアルに登録

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Elasticについて

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Elastic (ELK) Stack

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AUTHOR

の記事 Emily Mosher

Security Events Logging at Bell Canada

A Fast, Satisfying Search Experience with AmericanAncestors.org

Eyes in the Sky: Geohazard Monitoring with Terradue and Elasticsearch

Fighting Ebola with Elasticsearch: Cause Award Honoree eHealth Africa

Stretching the Elastic Stack to Fit the (Very Large and Complicated) Human Genome

Elastic Cloudの無料トライアルに登録

Elasticについて

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