AmericanAncestors.org provides one of the leading online genealogical research sites in the industry. They offer users real-time search and access across more than 1.4 billion names to their 250,000 members around the world. Learn how the team at the New England Historic Genealogical Society have evolved from a very early on-premise Elastic implementation to taking full advantage of Elasticsearch Service. Hear about the challenges they encountered and design changes they made to maximize performance, as well as how they leveraged the skills of the Elastic support team to deliver a highly scalable service with minimal overhead.
This 20-minute practical session will give you an idea of how you can advance on the road to AIOps by automating root cause analysis with the Elastic Stack. How to save time and effort when dealing with multiple alerts, systems or applications, how to quickly identify false positives and ultimately address more precisely the causes of issues in your IT environment? By analyzing events from the Elastic Stack and applying a supervised Machine Learning algorithm, we can pinpoint root causes with higher accuracy and respond better and faster.
BAI Canada, a subsidiary of Australia-based BAI Communications, has built a public Wi-Fi offering in both the Toronto and NYC subway systems, where roughly 2 million unique devices totaling 14 million log-ons per month use the service. Their implementation of the Elastic Stack handles around 10 million new records per day and growing. Learn how BAI Canada has designed, architected, and run the Elastic Stack to help report on and monitor usage in one of the largest types of Wi-Fi deployments. See their plans for the future, including upcoming work in Australia.
The ongoing increases and disparate nature of big data sources make it difficult to collect, clean, analyze, and manage the distribution of security data in a unified manner. Learn how KPN is leveraging the Elastic Stack to power their security operations center and keep their organization secure.
Voxpopme are the market leader in video insight market research. See how they moved from Compose.io to AWS to Elasticsearch Service (formerly known as Elastic Cloud) in order to tackle their growing data ingest needs, up to nearly 1 million records this year alone. Learn how they revamped their entire system, including combining and indexing all of their data into a new Elasticsearch cluster, to improve speed by 10X and ensure continued success as they scale for exponential growth.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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?