Today, we are proud to announce that InfluxDB Open Source 2.0 is now generally available for everyone. It’s been a long road, and we couldn’t have done it without the amazing support and contributions of our community. This marks a new era for the InfluxDB platform, but it truly is just the beginning. Before we talk about the future, let’s take a look at some of the amazing new capabilities our team has been working on.
On November 12, 2013, I gave the first public talk about InfluxDB titled: InfluxDB, an open source distributed time series database. In that talk I introduced InfluxDB and outlined what I meant when I talked about time series: specifically, it was any data that you might ask questions about over time.
Kibana is for everyone. As the creators of the Elastic Stack, we get a lot of feedback when chatting with our users from all corners of the world during ElasticON events, in GitHub and forums, and while helping folks resolve their support cases. One of the things we've heard in the past is that Kibana is difficult to use. And we've listened to our community!
Whether you consume App Search from Elastic or from Swiftype, you’re getting a set of robust APIs and unprecedented relevance controls to deliver amazing search experiences. But what if you could have that same powerful set of search tools, only better, faster, more flexible, and still built on the powerful, scalable foundation of Elasticsearch? We’d like to invite you to migrate your Swiftype App Search deployment over to App Search on Elastic Cloud.
Elastic on Azure gives you the power of Elastic Enterprise Search, Elastic Observability, Elastic Security as well as the Elastic Stack. You can quickly and easily search your environment for information, analyze data to observe insights, and protect your technology investment. Elastic Cloud lets you deploy your way, whether as a managed service, or with orchestration tools you manage in Azure. You can easily get started with Elastic Cloud on Azure through our listing page on the Azure Marketplace.
Anodot CEO and Co-Founder David Drai joined Amazon Web Services and Xandr to discuss the shift to machine learning-based anomaly detection in business monitoring. Xandr Chief Technology Officer Ben John shared how their advertising marketplace is using Anodot platform to cut detection from “up to a week to less than a day”. You can watch the webinar at the link above or read on for the highlights of that talk.
In this lesson, we’ll learn how we can use Elasticsearch Hadoop to process very large amounts of data. For our exercise, we’ll use a simple Apache access log to represent our “big data”. We’ll learn how to write a MapReduce job to ingest the file with Hadoop and index it into Elasticsearch.
With the promise of unprecedented potential, artificial intelligence (AI) and predictive analytics have permeated into every field of business. Due to their ability to help retail staff serve customers better, personalize video recommendations based on users’ preferences, reduce employee churn, and detect fraud and security threats, AI and predictive analysis are rapidly being adapted across industry verticals.
