In any platform of sufficient complexity, multiple anomalies are likely to occur. For many organizations, NOC operators triage multiple anomalies based on their severity. There are internal, non-customer-facing issues that might affect only a small part of your workforce and one-time issues that affect only a small number of customers. Both of the issues get ticketed and sent to low-level support.
Graylog’s log aggregation features are useful for a lot of tasks, ranging from regular troubleshooting to detecting issues as soon as they become manifest. Optimizing log management by aggregating all meaningful data is a quick and efficient way to isolate any problem to root causes and solve it with minimal impact on services. Aggregated data is easier to parse and analyze – you can reduce the number of data points in a meaningful way and obtain the answer you need from them.
Let’s start with simple definitions. Time series data is largely what it sounds like – a stream of numerical data representing events that happen in sequence. One can analyze this data for any number of use cases, but here we will be focusing on two: forecasting and anomaly detection. First, you can use time series data to extrapolate the future.
You know what they say: you can’t fix what you can’t find. That’s what makes log management such a critical element in the DevOps process. Logging provides key information for software developers on the lookout for code errors. While working on their third startup in 2013, Chris Nguyen and Lee Liu realized that traditional log management was wholly inadequate for addressing data sprawl in the modern, cloud-native development stack.
In the first post of our three-part Amazon Redshift series, we covered what Redshift is and how it works. For the second installment, we’ll discuss how Amazon Redshift queries are analyzed and monitored. Before we go deep into gauging query performance on Redshift, let’s take a quick refresher on what Amazon Redshift is and what it does.
