This post is a recap of a community talk given at <a href="/elasticon/conf/2018/sf">Elastic{ON} 2018</a>. Interested in seeing more talks like this? Check out the conference archive or find out when <a href="/elasticon">Elastic{ON} Tour</a> is coming to a city near you.
With a physical presence in over 100 countries and clients in more than 160, CitiGroup has a large, global production infrastructure of business application teams. They face several IT challenges due to their business size and complex infrastructure. Scaling, business resiliency, IT agility, asset utilization, IT tooling, and new technologies have all put pressure on CitiGroup’s IT teams to do things faster(and at a lower cost).
IT tooling between teams has proven to be one of the biggest challenges CitiGroup has had to face. Every team needs to adapt to new business trends like automating software deployments, new deployment environments, and moving from single- to multi-purpose tools. In addition to the immense technical challenges, the CitiGroup IT teams must answer to CitiGroup senior management, who increasingly want to know more about the utilization and business benefits of the tools within their IT infrastructure.
To create oversight, CitiGroup needed a lot of data. With data sitting in various pools across different teams (and often countries), the IT teams were incapable of creating an end-to-end infrastructure monitoring picture that could be used by Citi Management to improve tooling, infrastructure health, and operations. So they set out to create an integrated monitoring system that could track and monitor current and new tooling performance throughout their stack and deliver easy-to-understand reports about said performance to management.
CitiGroup needed an integrated monitoring system that could do all of the following:
<ul>
	<li>Act as a centralized data store</li>
	<li>Configure agents</li>
	<li>Reduce tool licensing support costs</li>
	<li> Unify and centralize data governance</li>
</ul><h2>Developing with the Elastic Stack</h2>CitiGroup turned to the <a href="/products">Elastic Stack</a> because the IT teams saw that advanced search analytics would play a large role in setting up the kind of architecture they needed. For example, the infrastructure needed to ingest metrics, events, and logs (about 90 million documents a day across the entire bank) just to monitor tool performance. Elasticsearch offered an easy way to bring all this data into one place, store it, and make it searchable. Using <a href="/products/elasticsearch">Elasticsearch</a>, CitiGroup developed an active, primary data storage cluster that would hold information for 30 days for search and visualization purposes. Once the data aged beyond 30 days, Elasticsearch was used to export the data using automated snapshots to low-cost, on-prem object storage.<img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt2104312483891439/5c98d15367b8321d593d7f83/citigroup-datastorage.png" data-sys-asset-uid="blt2104312483891439" alt="citigroup-datastorage.png"> CitiGroup also needed a way to design indices for SOC and audit finding, along with various use cases and levels of access. Elasticsearch allowed their IT teams to group data, provide standardized naming schemes, and define role-based access control and authentication for data governance. Elasticsearch also allowed IT teams and management at CitiGroup to easily access data for analysis and go beyond the standard dashboard with the help of in-house APIs that gave business teams the power to call the API, retrieve data, and push it to local systems for further analysis by different departments.<img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt9282bb496ed50358/5c98d14d8e3cc6505f19f64d/citigroup-users.png" data-sys-asset-uid="blt9282bb496ed50358" alt="citigroup-users.png">By setting up container infrastructure monitoring using <a href="https://www.elastic.co/products/stack/alerting">alerting</a> (formerly called Watcher), CitiGroup was also able to develop a system that collects container metrics, events, and logs that touch every department, rather than limiting their monitoring to the activities of only one team. This information is used for a variety of uses cases including audit finding, alert management and ticketing, and more.<img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt330eacff1f0e4a7b/5c98d1478e3cc6505f19f647/citigroup-container.png" data-sys-asset-uid="blt330eacff1f0e4a7b" alt="citigroup-container.png">Along with monitoring needs, the IT teams at CitiGroup needed a scalable and secure way to share findings with different user groups (including management). Using Kibana, they have been able to create a dashboard that provides aggregated views of an application’s health in real time, individual views of metrics and logs, and container resource usage and topology information. Users can also drill down into an application or container to discover issues at their source. Operational and application teams can flip the way they look at the data, getting the top-down or bottom-up view they require for monitoring tooling performance and running reports that give an overview of their tool’s effectiveness in the real world.
Want to see how CitiGroup is leveraging advanced search analytics to support infrastructure monitoring at Citigroup? Watch the full Elastic{ON} talk, <a href="/elasticon/conf/2018/sf/application-infrastructure-monitoring-at-citigroup">Application Infrastructure Monitoring at Citigroup</a>.
<a href="/elasticon/conf/2018/sf/application-infrastructure-monitoring-at-citigroup"><img data-image="rn521584johu" src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltec31d076631a9483/5c57db3f4c9f4feb0b4c584c/Screen_Shot_2018-10-29_at_1.37.52_PM.png" data-sys-asset-uid="bltec31d076631a9483" alt="Screen Shot 2018-10-29 at 1.37.52 PM.png" title="Screen Shot 2018-10-29 at 1.37.52 PM.png" style="margin:0px auto;display:block"></a>

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Using the Elastic Stack to Monitor CitiGroup’s Centralized Database

<a href="https://www.rightmove.co.uk/">Rightmove</a> is the UK’s largest property rental and sales website, with over 1.2 million properties listed, and serving over 55 million visitors a day. Rightmove helps estate and lending agents advertise their stock, connect agents to vendors, and helps users find their next home. Nearly 90% of all estate agents in the UK advertise on the Rightmove website.
In 2014, Rightmove was using a monolithic proprietary search software to power their site. The software couldn’t handle Rightmove’s scaling needs and would only index when the system was offline (a particular pain point since re-indexing was required nightly to keep the site from falling apart). They tried adding more nodes in the hope of making the system run faster, but it did the exact opposite. Worst of all, geo search capabilities were not available, requiring Rightmove to make their own solutions which wasted hours of energy developing and rolling out map updates.
Rightmove established an internal initiative in 2014 — dubbed Project Odin — to investigate new search engine options that solved those debilitating issues. Their quest for better search led them to Solr and Elastic. Rightmove chose <a href="/elasticsearch">Elasticsearch</a> because — in comparison to Solr — it was faster at indexing, easier to work with, had the features they needed right out of the box, and was easier to scale.
<img data-image="1ua7fo1z18d4" src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt0c1564a1668daf37/5c58399665157fdb0b6c6569/rightmove_arch.png" data-sys-asset-uid="blt0c1564a1668daf37" style="display: block; margin: auto;" width="650">Today, Elasticsearch is used across the Rightmove website, with geo capabilities running in the background of all user and map searches. Rightmove uses three types of geo search tools: <ul>
	<li>Geo distance — used to group results within a set distance from a central point, such as public transport stops or offices</li>
	<li>Geo polygon/shape — helps define results within a certain area</li>
	<li>Geo-bounding box — an efficient way of searching within a rectangle that speeds up search results with greater and lesser than comparison</li>
</ul>Elasticsearch geo capabilities, along with making search on the website easier, have allowed Rightmove to introduce advanced mapping features such as a "<a href="https://www.rightmove.co.uk/draw-a-search.html">Draw-a-Search</a>" option (which allows users to create their own search areas), and the “Where Can I Live?” tool which gives users results based on input such as income, desired location, local amenities, and more. Elasticsearch also powers the “<a href="https://www.rightmove.co.uk/house-prices.html">sold price</a>” search and analysis features on the site to help users find how much their house is worth, the value of other properties along their street, or the property values of their desired location.<img data-image="hnio79sdie25" src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltc25dc81104a449e0/5c5839940da9acd10bcc908a/rightmove_map.png" data-sys-asset-uid="bltc25dc81104a449e0" style="display: block; margin: auto;" width="650">Rightmove, in an attempt to create better alerts and search results, also uses the <a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-percolate-query.html">Elasticsearch percolator</a> to index and store queries, which can be used to match documents to queries, rather than the other way around. This allows Rightmove to easily match new property documents to saved queries (such as price, location, etc.) so that users are automatically notified when properties matching their queries are available. Rightmove currently has about 3.5 million saved searches and runs 1 billion queries against percolator to create matches for user-specified alerts.
Behind the scenes, Elasticsearch delivers customer reporting and supports the website and its applications. Rightmove generates 7.3 billion log messages a day, with a retention rate of 2 weeks — about 17 terabytes of data — all of which, thanks to Elasticsearch, a queryable, flexible, consistent, and easy to configure for different purposes. Microservice development gives Rightmove the ability to create continuous deployments rather than two weekly releases, which was the case with their old, propriety monolith search engine.
Want to learn more about how Rightmove uses Elasticsearch? Watch their <a href="/elasticon/conf/2018/sf/improving-customer-experience-with-geo-capabilities-at-the-uks-biggest-property-portal">Improving Customer Experience with Geo Capabilities at the UK’s Biggest Property Portal</a>&nbsp;talk from <a href="/elasticon/conf/2018/sf">Elastic{ON} 2018</a> to learn what type of geo searches are used, the pros and cons of polygon vs. shape, a glimpse into the Rightmove website architecture and code, and to see demos of the website features in action.
<a href="https://www.elastic.co/elasticon/conf/2018/sf/improving-customer-experience-with-geo-capabilities-at-the-uks-biggest-property-portal"><img data-image="or4qo4d8xzdz" src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt1a59548a00648e7d/5c583989a209c1ca0be9df10/rightmove_preview.png" data-sys-asset-uid="blt1a59548a00648e7d" style="display: block; margin: auto;" width="650"></a>

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Elasticsearch and Rightmove: Mapping Out Your New Home With Search

OTTO Motors designs, manufactures, and operates self-driving vehicles used for industry. Learn how they use the Elastic Stack to run and monitor their fleet.

This post is a recap of a user talk given at <a href="/elasticon/conf/2018/sf">Elastic{ON} 2018</a>. Interested in seeing more talks like this? Check out the conference archive&nbsp;or&nbsp;find out when the <a href="/elasticon">Elastic{ON} Tour</a> is coming to a city near you.
<a href="https://ottomotors.com/">OTTO Motors</a> — a part of <a href="https://www.clearpathrobotics.com/">Clearpath Robotics</a> — designs, manufactures, and operates self-driving industrial vehicles. Their goal is to use robotics to make human-driven operations obsolete. Their mobile robot fleet operate in over 40 countries, allowing companies to automate work, including some that would otherwise be dangerous for humans.
Operating so many robots requires a management system that can identify problems quickly, improve development speed, industrialize solutions at cost, and easily connect to IoT technology. OTTO Motors also relies on intelligent path planning, which requires high volumes of data points to be processed live, so that robots know how to get from A to B at both an individual unit and fleet level.
OTTO Motors needed a full stack solution that could be tested without huge expense or the need to build new technologies, and that would plug easily into existing software already in use within the company. While searching for a solution that fit all this criteria, the OTTO Motors team made a survey of the wide, IoT solution landscape. “We wanted something quick,” says Greg Jacobs, former Manager of Infrastructure at OTTO Motors. “Turns out Elastic was the best thing to start with.”
Ultimately, the team at OTTO Motors chose the <a href="/products">The Elastic Stack</a> for three key reasons:
<ol>
	<li>Elastic’s products have HTTP/JSON APIs readily available for use. With the Elastic Stack, it was easy to get data into the service because it played nicely with other existing internal products and services already in use. In addition, Elastic provided easy-to-use client libraries and rich data collection options.</li>
	<li>A single computer could be used to install and run the Elastic Stack, and out-of-the-box was an independent system, nothing else needed to be built to be off to the races. From the install, it was easy to get started, explore, start plugging things in with the Elastic Stack, and scale as OTTO Motors’ needs grew.</li>
	<li>KIbana is web-based, easy to use, and well-integrated for use by a variety of end users/use cases, making it useful across the entire company. </li>
</ol>Again, Greg Jacobs: “[The Elastic Stack] was the seed to plant. But it isn’t going to be something that grows into (just) a walled (off) garden. It will work with all the other stuff.”
To that end, OTTO Motors moved beyond simply using the Elastic Stack for IoT applications, and deployed it to help with software development QA, IT security, and building dashboards for exploratory reports. For example, automated systems for testing robots at OTTO Motors makes virtual docker containers to create Lidar data that the robots read as real obstacles. Elastic also allows OTTO Motors to perform high-speed simulations that the robots can complete instantaneously. This allows OTTO Motors to exercise their fleet management software at hundreds, if not thousands of times faster than normal speed, all while using <a href="/products/beats/metricbeat">Metricbeat</a> and other tools provided by Elastic to test for issues within the system.<img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltee4a974b8d3accea/5f9b2254432f517518d40b71/blog-otto-motors.png" data-sys-asset-uid="bltee4a974b8d3accea" alt="blog-otto-motors.png">Running a large fleet of robots produces a massive amount of data which is fed to Elastic. The amount of data collected by OTTO Motor robots is overwhelming: over 5k data points every 10 seconds for every robot in a fleet. This includes CPU time, WiFi signal, motor temperature, navigation details, and more. And they use Elastic to aggregate all that data into 30 minute reporting windows. OTTO Motors also uses Elastic’s monitoring feature to create custom dashboards to discover hidden trends, see what indices are being hit hardest, and to learn what’s popular with users.
The Otto Motors story is about full stack needs and full stack solutions. Want to learn more about how Otto Motors uses the Elastic Stack to make their robotics work better across the globe? Watch <a href="/elasticon/conf/2018/sf/the-quick-starter-stack-for-robotics-iot-and-big-data">The Quick Starter Stack for Robotics, IoT, and Big Data</a> talk from <a href="/elasticon/conf/2018/sf">Elastic{ON} 2018</a>. In it you’ll learn more about how the Elastic Stack makes maintaining and growing a fleet of intelligent, self-driving robots a reality.
<a href="/elasticon/conf/2018/sf/the-quick-starter-stack-for-robotics-iot-and-big-data"><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt649d393fa3c4fcfe/5c57df9352256ff20b4afaf7/Screen_Shot_2018-08-22_at_1.28.13_PM.png" data-sys-asset-uid="blt649d393fa3c4fcfe" alt="OttoMotors" style="display: block; margin: auto;"></a>

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OTTO Motors: Using the Elastic Stack to Expand the IoT Landscape

NetApp is one of the fastest growing all-flash array vendors, recognized as a leader in general-purpose disk arrays, solid-state arrays, and integrated systems. OnCommand Insight (OCI) was created by NetApp to be used as a monitoring tool for data centers with on-prem, private, and public cloud setups. OCI has three common use cases: intelligent operations, business insights, and ecosystem optimization (including forecast performance, resource optimization, etc).
Recently, NetApp expanded its focus to help customers get more value out of their data, especially when moving between on-prem and cloud data stores. They did this by harnessing the power of the hybrid cloud, building a next generation data center, and modernizing storage through data management.
Today, OCI is an intelligent operation management system capable of diving deep into data, learning and forecasting trends, and optimizing resources. However, the tool did not always have this level of flexibility. <a href="https://www.elastic.co/elasticon/conf/2018/sf/the-path-to-intelligent-operation-with-netapp-oncommand-insight">At Elastic{ON} 2018, Karen Dagen and Francisco Rosa from the NetApp team</a> revealed how they embedded <a href="https://www.elastic.co/elasticsearch">Elasticsearch</a> into OCI to create a more robust app for their customers.
<h2>All Roads Lead to Elasticsearch</h2>It all started with the need to scale.
In 2014, NetApp was upgraded to version 7.0, bringing new scale and functionality to the system. Until this point, NetApp had an architectural flow that moved from OCI to MySQL, where everything was stored. Lucene was used to discover objects stored in the database via attributes. In addition, all time series events were stored in Cassandra, which, though fast and efficient, was not capable of helping customers visualize more than one time series event at a time.
To solve this problem, NetApp engineers replaced Lucene and Cassandra with Elasticsearch as an embedded solution delivered within OCI. Using Elasticsearch, a large index was created for virtual machines that included both attributes and counters. This allowed OCI to display multiple timeline visualizations, filter results, and present hypervisor averages — all features previously unavailable.&nbsp;
<h2>Lessons Learned Along the Way</h2>NetApp engineers learned a few lessons when creating the OnCommand Insight dashboard as it is today.
First, embedding worked for NetApp. Being able to install what was needed as the app scaled was incredibly helpful. Elasticsearch can do a lot, but for what it can’t do out the box, it is easily expandable using plugins. While plugin documentation is sometimes scarce, being able to look at the open source Elasticsearch code makes it possible to create the plugins needed for success. In NetApp’s case, having the functionality of a plugin outweighed whatever extra work was necessary to keep up with Elasticsearch as it updated.
Second, machine learning proved its value for NetApp by revealing that dormant resources within the system, which are normally inactive, can create a small anomaly when activated — small blips on a timeline that would be alarming without an alert.
<h2>Building Solutions Customer Love</h2>Since integrating Elasticsearch into OCI, NetApp has received great feedback from their customers. From the scale of the solution to the richness and quality of the data from the sources and API, embedding Elasticsearch in the OnCommand Insight app greatly expanded the possibilities of the product. NetApp has been working to incorporate more components of the Elastic Stack into their offering (including Logstash and Beats) and will continue adapting, and learning lessons, as the years go by and the data continues to scale.
<a href="https://www.elastic.co/elasticon/conf/2018/sf/the-path-to-intelligent-operation-with-netapp-oncommand-insight">Watch the full session from Elastic{ON} 2018 </a>to find out how NetApp has expanded its focus to help customers get more value out of their data with an embedded Elasticsearch solution.

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NetApp: Finding Intelligent Operation with Embedded Elasticsearch

This post is part of the <a href="https://www.elastic.co/elasticon/conf/2018/sf">Elastic{ON} 2018</a>&nbsp;blog series where we recap specific demos and related deep-dive sessions from the conference. From machine learning forecasting to APM to security analytics with Mr. Robot — check out the list at the bottom of this post.
Detecting security breaches and performing post-detection adversary activity tracking can be frustrating and time-consuming work. But it doesn't have to be. In this demo Asawari Samant, Director of Product Marketing at Elastic, shows how the Elastic Stack can be used to detect security breaches and investigate the who, what, where, and how behind it. 
<div class="video embed-container" style="height: 319.725px;">
	<img style="width: 100%; margin: auto; display: block;" class="vidyard-player-embed" src="https://play.vidyard.com/eizyu8dVBp1GzuULkJnrRK.jpg" data-uuid="eizyu8dVBp1GzuULkJnrRK" data-v="4" data-type="inline">
</div>So, how can the Elastic Stack identify and locate the source of security breaches? It all starts with data collection. In this fast-paced demo, Asawari demonstrates how lightweight data shippers called <a href="https://www.elastic.co/beats">Beats</a> (specifically Winlogbeat for Windows event logs and Packetbeat for network data) can be used to simplify log collection. 
Kibana provides data visualization and exploration layers for sleuthing out potential malicious activity, letting users drill in and out as needed to follow the trail of a potential threat. Employing the anomaly explorer view, Asawari shows how users can&nbsp;zoom into anomalies to see where they occurred, when they occurred, and the severity of the issue. Using machine learning influencer analysis, she then demonstrates how users can determine which hosts and domains contributed to the detected activity. 
Asawari then outlines how the time series visualization features in Kibana can create&nbsp;dynamic views of flagged events such as successful logins from anonymous users. From there, users can also see whether honey files (sensitive or critical files within a system) have been accessed, scrutinize network traffic for chatter between hosts, determine which users were in control of that activity, and locate the potential offender. 
For analysts working to address a breach post detection, speed at scale is key. Working together, Elasticsearch, Kibana, Beats, and Logstash changes your relationship to data, providing fast results to queries so you can follow issues at the speed of thought. 
Want to see the demo in action? Watch the video above. If your inner detective is inspired by all that the Elastic Stack can accomplish, take a deep dive into security analytics with Jingsi Xia and Rajesh Hari in Ferreting out Financial Fraud at Discover Financial or <a href="https://www.elastic.co/elasticon/conf/2018/sf/a-security-analytics-platform-for-today">A Security Analytics Platform for Today</a> with Samir Bennacer and Kevin Kenney.
See what else we covered <a href="https://www.elastic.co/elasticon/conf/2018/sf/agenda">during the conference</a> in these recaps:&nbsp;
<ul>
	<li><a href="https://www.elastic.co/blog/geo-roadmap-for-elasticsearch-and-kibana-layers-geojson-vega">Geo Roadmap for Elasticsearch and Kibana: Layers, GeoJSON, Vega</a></li>
	<li><a href="https://www.elastic.co/blog/elasticon-kibana-canvas-story-elasticoffee">But First, Coffee - An Elastic{ON} Canvas Story</a><a href="/blog/elasticon-kibana-canvas-story-elasticoffee"></a></li>
	<li><a href="https://www.elastic.co/blog/how-mr-robot-technical-consultant-ryan-kazanciyan-used-kibana">How Mr. Robot's Technical Consultant Used Kibana in the Show</a></li>
	<li><a href="/blog/make-operational-data-simple-with-the-elastic-stack">Operational Data Analysis with the Elastic Stack</a></li>
	<li><a href="https://www.elastic.co/blog/monitor-kubernetes-with-beats-autodiscover-feature-and-elasticsearch">Monitor Kubernetes with Beats Autodiscover Feature</a></li>
	<li><a href="https://www.elastic.co/blog/elasticon-kibana-canvas-story-ama">Here to Help - An Elastic{ON} Canvas Story</a></li>
	<li><a href="https://www.elastic.co/blog/data-rollups-in-elasticsearch-you-know-for-saving-space">Data Rollups in Elasticsearch</a></li>
	<li><a href="https://www.elastic.co/blog/swiftype-app-search-using-its-intuitive-interface-to-implement-elasticsearch">App Search with Elasticsearch</a></li>
	<li><a href="/blog/preview-of-sql-in-canvas-with-rashid-creator-of-kibana">A Preview of SQL in Canvas with Rashid, Creator of Kibana</a></li>
	<li><a href="/blog/recap-elasticsearch-machine-learning-forecasting-on-time-series-data">Machine Learning Forecasting on&nbsp;Elasticsearch Data</a></li>
	<li><a href="/blog/getting-started-with-apm-for-the-elastic-stack-a-recap">APM for the Elastic Stack - A Recap</a></li>
	<li><a href="/blog/whats-coming-with-machine-learning-logging-and-more-on-hosted-elasticsearch">Machine Learning, Logging, and More on Hosted Elasticsearch</a></li>
</ul>

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Using Kibana and Beats for Security Analytics

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