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.
Random forest is one of the most widely used machine learning algorithms in real production settings.
A North American telecom company struggled for years trying to react quickly enough to new categories and new levels of spam texts and calls. They also did not have a good way to know when and why they would need additional capacity on their own, or any other telecom company’s networks.
For enterprise organizations, managing and operationalizing increasingly complex data across the business has presented a significant challenge for staying competitive in analytic and data science driven markets.
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.
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.
