Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

This article was written by InfluxDB Community member and InfluxAce David McKay. Eighteen hours ago, I was meeting with some colleagues to discuss our Kubernetes initiatives and grand plan for improving the integrations and support for InfluxDB running on Kubernetes. During this meeting, I laid out what I felt was missing for InfluxDB to really shine on Kubernetes.

Downsampling is the process of aggregating high-resolution time series within windows of time and then storing the lower resolution aggregation to a new bucket. For example, imagine that you have an IoT application that monitors the temperature. Your temperature sensor might collect temperature data. This data is collected at a minute interval. It’s really only useful to you during the day.

Whether you’re using InfluxDB Cloud or InfluxDB OSS, the InfluxDB API provides a simple way to interact with your InfluxDB instance. The InfluxDB v2 API offers a unified approach to querying, writing data to, and assessing the health of your InfluxDB instances. In today’s Tech Tips post, we’re learning about how to create and list buckets. Buckets are named locations in InfluxDB where time series data is written to.