I Am Community and So Can You

The Elastic community is growing every day, and in 2017, we zoomed well past 120K members across GitHub, Meetups, forums and more. If you've ever wanted to do any of the following, this is the session for you:

  • Contribute to Elasticsearch, Logstash, Kibana, or Beats
  • Use the Elastic Stack to track community metrics
  • Speak at a conference or user group about the Elastic Stack
  • Level up your community involvement in any other way

Members of the Elastic Developer Relations team will walk through current metrics around the Elastic community, resources we provide to support your endeavors in open source, and our future plans and goals.

You'll leave this talk with a stronger understanding of the Elastic community, a ton of tips on how to increase your involvement in open source, and a clear view on how these activities can help your skills, projects, career, and friendships in tech.

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Lyft's Wild Ride from Amazon ES to Self-Managed Elasticsearch

From October 2016 to May 2017, Lyft was Amazon's biggest hosted Elasticsearch customer and most frequent support headache. After quickly scaling to the maximum supported size — and then beyond — Lyft found themselves keeping one big support ticket open to address daily (and sometimes hourly) incidents. It was clearly time for a change. After two intense weeks of migration, Lyft became self-hosted, and both companies breathed a sigh of relief.

How did Lyft get there from here? What are the pros and cons of cloud versus self-hosted Elasticsearch deployments? And what has Lyft learned from almost a year of operating their own cluster? Michael Goldsby of Lyft's observability team talks you through the joys and sorrows of scaling a logging cluster from 300,000 to 3 million events per minute.

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Replacing Systems of Record with Engagement at Royal Bank of Canada

Today, almost every single major decision that drives revenue, controls costs, or reduces risk can be powered with data and analytics. Search analytics are being used to help companies react faster and smarter through insight into everything from marketing to consumer trends and beyond.

See how the Royal Bank of Canada leverages a powerful event-driven microservices architecture in order to evolve from a systems-of-record way of working to systems-of-engagement. Learn how Canada's leading financial institution utilized Elastic Cloud Enterprise to accomplish mainframe offload through data analytics and drive tremendous cost savings.

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Elasticsearch SQL

If you (or others you know) are versed in SQL statements and newer to the Elasticsearch query syntax but want to benefit from the power Elasticsearch, this is the talk for you. After making its debut at last year's conference, Elasticsearch SQL is a project designed to bridge these two worlds. From converting SQL statements into Elasticsearch queries to a console experience for exploring data using "SELECT x FROM y WHERE z > 1000" type queries, there's a lot on the horizon.

Join Costin to get the full rundown on where we are one year later.

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A Security Analytics Platform for Today

Thinking about building an end-to-end security analytics platform with the Elastic Stack? This talk explores how to do it with a homegrown solution that’s fast and scalable so you can increase team impact by having more data faster and gaining back time for threat hunting versus responding to alerts.

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The State of the Elasticsearch Java Client

Java has always had a special place in our hearts, but we've never had a great Java client for Elasticsearch. The Transport client depends on the rest of Elasticsearch and is tied deeply into Elasticsearch's internal binary protocol. The Low-Level REST client is useful but it is too low level to replace the Transport client. The High-Level REST client is incomplete and still depends on the rest of Elasticsearch. It is time to finish the High-Level REST client and remove its dependency on Elasticsearch.

Learn how we're ushering in a new era with the Elasticsearch Java client and what to expect next.

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Stories from the Trenches at GoDaddy: How Big Data Insights Equal Big Money

GoDaddy is ingesting and analyzing more than 200,000 data points per second to improve customer experience and operations. They're using the Elastic Stack along with X-Pack and technologies like Kafka, Spark, and Hadoop to perform real-time anomaly detection, log analysis, and auto-remediation daily on over 10 TB of new data across their products and systems.

Take a journey through their transformation from a farm of data silos to a centralized platform that ingests, analyzes, and visualizes data throughout the enterprise. Learn how they collect and use data from products like Hosting and Domains to analyze infrastructure events across their fleet of 85,000 servers and network devices. Plus, see how they leverage data in real time to answer key business questions.

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"Here, Hold My Beer." Customer Stories to Learn from As Told by Support

Elasticsearch gives you so many different options and configurations to support a variety of use cases. However, just because you can tweak so many settings within Elasticsearch, doesn't mean you should.

Members of the Elastic support team discuss the top three common customer mistakes and misconfigurations along with best practices and recommendations so you can avoid those issues yourself.

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Stretching the Cloud: Flexibility in Cloud Deployments

Elastic Cloud has historically been optimized for performance. We focused on the best instances and SSDs we could get on AWS and GCP, but recognized that those optimizations might not fit all use cases.

The team will discuss, in technical detail, the biggest changes to Elastic Cloud since its launch. New capabilities will allow you to match your provisioned hardware to your use cases, making it easier to efficiently run Elasticsearch for multiple workloads such as hot/warm architectures, dedicated master nodes, and machine learning. This is foundational to enabling additional Elastic Stack features like APM, Logstash, and more.

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Concept to Flight: Engineering Software for Systems Engineers at JPL

JPL systems engineers are responsible for the system design across the lifecycle of a flight project, and capturing the complexity of this decision-making process is a difficult task. See the software approach that allows system engineers to document, query and perform analysis on highly structured data.

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Managing the Elastic Stack in Production

There are many things to consider and many tools and techniques at your disposal when you begin running and managing the Elastic Stack in production. This talk will highlight new management features of Kibana and cover the most important elements for running and managing the Elastic Stack in production.

We'll walk the new administrator through key questions, like how to plan for long-term retention and how to know when it's time to add more nodes. You'll also learn how to use monitoring to proactively detect issues with your cluster and identify the root causes. Plus, see how you can use alerting to notify you if there are issues with specific data sources or if your users change their usage patterns.

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The Math Behind Elastic Machine Learning

Machine learning functionality is being added to our products in the form of shrink-wrapped applications. We began by focusing on time series anomaly detection, which required the ability to predict what the time series might do next. It wasn't such a large step to go from this to forecasting what the time series might do over the next day, week, or month. This capability was added in 6.2.

To automate this process, an ML system needs expressive modeling, which can adapt to different data characteristics, relearn its parameters (or select a new model as things change), seamlessly deal with abnormal periods to minimize their impact on the modeling, and much more. Dig into the details of some of the modeling techniques we’ve used for these features and some of the key ways we have addressed these requirements.

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The Path to Intelligent Operation with NetApp OnCommand Insight

OnCommand Insight is a sophisticated management and monitoring product for hybrid IT infrastructures on public clouds, private clouds, and on-premises environments. The platform collects telemetry from applications and systems, network, and storage infrastructure, and the analytics are built on Elasticsearch and use X-Pack machine learning features to alert on anomalous system behavior.

Learn about the technical architecture of the product, including the tradeoffs and design decisions that led to replacing Cassandra with Elasticsearch, optimizing Elasticsearch for an embedded use-case, and the move from alerting based on static thresholds to using X-Pack machine learning features. Plus, hear about lessons learned and insights from running Elasticsearch embedded in our product at hundreds of customers.

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Logging and Metrics in Elastic Cloud: Drinking Our Own Champagne

In Elastic Cloud, we've migrated from a polyglot logging solution to one based entirely on the Elastic Stack.

Hear members of the Cloud SRE team talk about making the switch and cover architectural and implementation concerns, care, and feeding, as well as lessons learned.

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Being Elastic: The Who, The Where, The How

A distributed system...built by a distributed team...in a company committed to distributed work. Join this session to learn more about Elastic the company. Who we are. Where we are. How we work.

What is it that makes Elastic – well – elastic? What are some characteristics of an Elastician? What are the actual things that we care about and the aspirational statements we make to chart our course into the future?

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Helping Grubhub Diners Search for the Perfect Meal

Ever felt hungry but couldn't decide what to eat? The search team at Grubhub helps diners find the perfect meal. In this talk, see how Elasticsearch helps Grubhub deliver.

Discover what drove Grubhub to convert to Elasticsearch and see the ways the company tailored their stack to improve delivery — both for their engineers and diners. Hear about the importance of ephemeral clusters in deployment, how and why to build integrations with Eureka, how to use Impression Engine to improve search results, and more of Grubhub's favorite recipes for testing, improving performance, and optimizing relevance.

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Machine Learning in the Elastic Stack

Machine learning for the Elastic Stack empowers you with simple tools to understand the behavior of your Elasticsearch data. What started with simple single- and multi-metrics anomaly detection jobs has grown into a powerful tool that automates notifications for anomalies and simplifies tasks like pre-configuring NGINX log analysis at scale. And there's more to show, including new features such as time series forecasting, which will allow you to predict system capacity and pre-empt issues, and automatic log data categorization.

Learn how to apply machine learning to your Elasticsearch data and see new features in action firsthand.

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Improving Customer Experience with Geo Capabilities at the UK’s Biggest Property Portal

With nearly 55 million requests per day to their website, Rightmove is the UK's most visited property portal that helps connect potential renters and buyers with their next domestic investment. And they're using the Elastic Stack to deliver a quality search experience for users.

Hear how Rightmove uses Elasticsearch geo capabilities to return more relevant search results for their users, how features like percolation allow them to alert users to properties they might be interested in, and how aggregations are used for reporting. Plus, see how Elasticsearch and Kibana have improved the overall developer support process for applications as they monitor 17 TB worth of log data on an ongoing basis and enabled the creation of innovative new tools like the Where I Live tool.

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Logs, Metrics, and APM: The Holy Trinity of Operations

The Elastic Stack is used for operational analytics in many environments today. However, we see many customers and users taking advantage of only a limited set of data sources in their Elastic deployments and continuing to rely on a set of siloed tools for the rest. Many users even build custom UIs and reports to bridge information from disparate systems, making such deployments complex and expensive.

Explore how the Elastic Stack can be used for a comprehensive operational analytics deployment, including infrastructure and application logging and metrics, as well as deep APM transaction analysis. See examples from users and customers, as well as Elastic's own deployments successfully doing this in practice.

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Kubernetes, Docker, and Containers at Elastic: Monitoring, Logging, and More

Tasks such as log collection and metrics aggregation can be daunting within Kubernetes and Docker deployments. Fortunately, the Elastic Stack provides effective methods to collect and store a wide variety of operational data to simplify these and similar problems.

Learn how leveraging the latest features in Beats (including “add metadata” processors, modules, and new autodiscovery features), Elasticsearch, and Kibana can make managing containerized, scheduled, and otherwise automated environments a great experience.

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