Shay Banon

Meet a CEO: Shay Banon from Elastic

We spoke with the CEO of digital game changers Elastic about the importance of fast, real-time search engine analytics in the ever-shifting landscape of modern business.

 

 

As CEO of Elastic and creator of Elasticsearch, you were there from its very beginning. What were the major milestones of Elasticsearch since its initial release in 2010?

When I first released Elasticsearch I had one goal: make it simple for a developer to get started, download and install Elasticsearch on their laptop, load data into it, and get really fast results in milliseconds or less. Today we have more than 140 million downloads of our software and our community has grown to more than 100,000 developers across 100 countries.

While there are lots of individual milestones for Elasticsearch, I’ll highlight a few company milestones that make us who we are today. Early on in 2013, Kibana and Logstash joined forces with Elasticsearch to create the de facto open source logging solution. Like how I built search based on frustration with existing tools, the creators of these projects – Rashid Khan and Jordan Sissel – also were frustrated with off the shelf products, and created these products to help them do their jobs as system and network administrators. A few years later in 2015, we changed the company name from Elasticsearch to Elastic as we were a multi-product and use case company beyond just search. In the same year, we acquired a Norwegian SaaS company (today Elastic Cloud) so that we could offer users a way to deploy our products in the cloud, and an open source project called Packetbeat, based in Berlin, decided to join us. Last year, as our products extended to Beats, we re-named the popular “ELK” to the Elastic Stack and introduced X-Pack, a single installation for all of our commercial features. Earlier this year, we formed a new partnership with Google to provide Elastic Cloud on GCP, launched Elastic Cloud Enterprise (ECE) for enterprises to deploy and manage multiple Elastic Stack environments on-premise or in a private cloud, and we just acquired a SaaS APM company based in Copenhagen.

 

Which roles do search engines such as Elasticsearch play for the analytics of business performance figures and metrics?

The strength of a search engine as a technology is that it exists to solve real problems. Whether it is a search box on a website or analysing billions of log events across hundreds of machines, search provides incredible value to end users. With Elasticsearch, users get fast, real-time insights and analytics from the data that is stored and continuously added to. This lets users act on the data in a meaningful way, for example, to help a visitor to a website find what they are looking for in milliseconds or less, or help a system admin troubleshoot errors within petabytes of log files.

 

Elastic is used by millions of people and a wide array of companies. What – except from the fact that it is open source – is it that makes Elasticsearch so popular?

Elasticsearch was created to put the power of data exploration in the hands of users. There are a lot of things about it that make it popular with developers: it’s easy to get started and one can download it on a laptop; it works great for both structured and unstructured data; Elasticsearch horizontally scales; ingesting data into Elasticsearch is easy with 200+ connectors; Kibana visualisations are intuitive, powerful and provide real-time exploration; and everything works on-premise or in the cloud.

You recently added the first functions of machine learning into the Elastic Stack. How do your customers benefit from this advancing technology?

Machine learning is a natural extension of the powerful search and analytics capabilities in Elasticsearch. As our users continue to store more and more data in Elasticsearch, machine learning will help them automatically detect and spot anomalies in their data without having to use third-party data science tools.

Today, our customers like BlaBlaCar, Expedia, Groupon, Uber, and Yelp use machine learning on top of the data stored in Elasticsearch to drive personalisation, offers and monetisation strategies, and to give their users the best online or mobile experience. In the future, like we’ve done with time series anomaly detection, we can give customers a way to expose machine learning with other use cases.

What does Elastic have in store for the rest of 2017 and next year?

Earlier this summer, we acquired Opbeat, an application performance management (APM) company, based in Copenhagen. We’re super excited they decided to join our team as they’ve built a wonderful SaaS APM solution for developers to instrument their applications and monitor their code. Like with machine learning, APM is yet another extension of the Elastic Stack. This will provide our users with the ability to have an end-to-end solution for search, logging, metrics, and application monitoring. APM will be part of our open source.

Our major 6.0 release is also coming soon this November. This has many new features across the entire Elastic Stack. We’ve created an entirely new upgrade experience for migrating applications to new versions, worked hard in Lucene 7 to make searches even faster and more efficient created a new Kibana query language called Kuery, developed many new alerting and security features in X-Pack, and will be delivering a user interface to manage Logstash pipelines.

Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data so you can discover the expected and uncover the unexpected. You can see more about it here.

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