At Pike13, we strive to help our customers spend more time doing what they love by reducing the stress that comes from managing the things they don’t. From fitness studios, martial arts dojos and music schools, our customers leverage Pike13’s software to take care of business activities such as scheduling, client management, reports, and billing.

Over time, our customers have adopted and used our tool for detecting anomalies or discovering opportunities. We understand that the journey does not stop there. We still have to find the root cause of the incidents. To help our customers in their endeavor to investigate and reach closure, we now introduce Smart Insights on detected anomalies.

Provide fully-branded reports, performance alerts, log in access, and more to your team and/or clients.

In this tutorial, we’ll use the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm from scikit-learn with the ADTK (Anomaly Detection Tool Kit) package to detect anomalous CPU behavior. We’ll use the InfluxDB 2.0 Python Client to query our data in InfluxDB 2.0 and return it as a Pandas DataFrame. This tutorial assumes that you have InfluxDB and Telegraf installed and configured on your local machine to gather CPU stats.

Organizations are building data lakes and bringing data together from many systems in raw format into these data lakes, hoping to process and extract differentiated value out of this data. Anyone familiar with trying to get value out of operational data, whether on prem or in the cloud, understands the inherent risks and costs associated with moving data from one environment to another.

Isn’t all logging pretty much the same? Logs appear by default, like magic, without any further intervention by teams other than simply starting a system… right? While logging may seem like simple magic, there’s a lot to consider. Logs don’t just automatically appear for all levels of your architecture, and any logs that do automatically appear probably don’t have all of the details that you need to successfully understand what a system is doing.

This blogpost will cover how customers can migrate clusters and workloads to the new Cloudera Data Platform – Data Center 7.1 (CDP DC 7.1 onwards) plus highlights of this new release. CDP DC 7.1 is the on-premises version of Cloudera Data Platform.