Operations | Monitoring | ITSM | DevOps | Cloud

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.

As data continues to explode across the enterprise, we are finding that it is becoming increasingly challenging for organizations to keep up. A recent Splunk report, "The Data Age is Here," found that 57% of companies interviewed expressed that the volume of data is growing faster than they can manage, with 47% bluntly saying they will fall behind when faced with rapid data volume growth.

At ServiceNow, we define analytics as using data to make better, faster decisions to run the company. We use analytics to not only spotlight every corner of our operations, but we also to spark growth, by giving our employees data-driven decision-making capabilities. That means they can take action every single day by using data and digital workflows. In order to drive data-driven decisions, we created a user-centric analytics program based on five major elements, which are listed below. 1.

With Elasticsearch, it's easy to hit the ground running. When I built my first Elasticsearch cluster, it was ready for indexing and search within a matter of minutes. And while I was pleasantly surprised at how quickly I was able to deploy it, my mind was already racing towards next steps. But then I remembered I needed to slow down (we all need that reminder sometimes!) and answer a few questions before I got ahead of myself.