Suffering from severe headaches during meetings, feeling fatigued and lethargic due to lengthy powerpoints and monologues (you know, the ones that go on and on)? If that sounds oh so familiar to you, we have good news: it’s not you. And (usually) neither are your colleagues nor their presentations to blame. More often than not, the culprit for a “meeting hangover” is “bad”, stale air.
Machine learning in the Elastic Stack provides you with an intuitive way to detect anomalies in vast data sets. But even the most sophisticated anomaly detection job might not reveal the root cause of anomalous behavior. After an anomaly is detected, you may need to dive into further analysis, review multiple corresponding metrics, and investigate how they relate to the anomalous spike.
The next 10 years will redefine banking. What will differentiate top banks from their competitors? Data and derived insights.
Ingesting various events and documents into Elasticsearch is great for detailed analysis but when it comes to the common need to analyze data from a higher level, we need to aggregate the individual event data for more interesting insights. This is where Elasticsearch Data Frames come in. Aggregation queries do a lot of this heavy lifting, but sometimes we need to prebake the aggregations for better performance and more options for analysis and machine learning.
In this post we share how to use the InfluxDB CLI with InfluxDB Cloud. This TL;DR assumes that you have registered for an InfluxDB Cloud account – registering for a free account is the easiest way to get started with InfluxDB.
Event Hubs are a big data streaming PasS capability provided by Azure. Event Hubs can process data or telemetry produced from your Azure environment. They also provide us a scalable method to get your valuable Azure data into Splunk! Splunk add-ons like the Splunk Add-on for Microsoft Cloud Services and the Microsoft Azure Add-on for Splunk provide the ability to connect to, and ingest all kinds of data sources from your Azure environment.
Logs are one of the most valuable assets when it comes to IT system management and monitoring. As they record every action that took place on your network, logs provide the insight you need to spot issues that might impact performance, compliance, and security. That’s why log management should be part of any monitoring infrastructure.
In our previous two blogs, we provided an overview of the architecture and design of the Elasticsearch Go client and explored how to configure and customize the client. In doing so, we pointed to a number of examples available in the GitHub repository. The goal of these examples is to provide executable "scripts" for common operations, so it's a good idea to look there whenever you're trying to solve a specific problem with the client.
Evaluating a new, unknown technology is a complicated task. Although you can articulate the goals you’re trying to achieve, you’re probably faced with multiple solutions that approach the problem in different ways and highlight varying features. To cut through the clutter, you need to figure out what questions to ask in order to evaluate which technology has the optimal capabilities to get the job done in your unique setting.
