Operations | Monitoring | ITSM | DevOps | Cloud

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.

In the first part of the blog series, we discussed how correlation analysis can be leveraged to reduce time to detection (TTD) and time to remediation (TTR) by guiding mitigation efforts early. Further, correlation analysis helps to reduce alert fatigue by filtering out irrelevant anomalies and grouping multiple anomalies stemming from a single incident into one alert. In this part, we throw light on the applicability of correlation analysis in the realm of eCommerce, specifically, promotions.

What exactly is the Data Age? Well, there is no single definition of what this means - but my interpretation is that it refers to the fact that data can now be used as a foundation for decision making in every department of every business. And with the volume of data generated forecast to continue to grow exponentially up until 2025 according to IDC, the possibilities for using data to drive informed decision making are only going to increase.

For public IPs, it is possible to create tables that will specify which city specific ranges of IPs belong to. However, a big portion of the internet is different. There are company private networks with IP addresses of the form 10.0.0.0/8, 172.16.0.0/12 or 192.168.0.0/16 scattered in every country in the world. These IP addresses tend to have no real information for the geographic locations.