At Sumo Logic, we manage petabytes of unstructured log data as part of our core log search and analytics offering. Multiple terabytes of data are indexed every day and stored persistently in AWS S3. When a query is executed against this data via UI, API, scheduled search or pre-installed apps, the indexed files are retrieved from S3 and cached in a custom read-through cache for these AWS S3 objects. For the most part, the caching scheme for S3 objects works reasonably well.

From the first stay-at-home order, we have been working alongside our customers — from a distance, of course — and we helped them through a new and changing reality. As a customer success organization, we help customers leverage the tools and solutions they have, troubleshoot issues and optimize their business with the intent of helping customers be nimble and adjust to the changing conditions and uncertainties in the market.

Curbside pickup. Contactless payment. Voice commerce. In today’s world, the retail landscape is changing at record pace as consumers and the global economy embrace digitization like never before. I couldn’t be more excited about what’s to come for the industry.

AIOps is a term Gartner invented to describe a general trend of applying AI techniques to IT Operations data sources to provide additional insights and scale to the teams operating today’s complex software system. AIOps is essentially a feature or set of features to analyze, combine, and collect data. Unfortunately, the lack of AI in these solutions often turns many people off, but this promise is still possible.

When viewing and analysing data with Elasticsearch, it is not uncommon to see visualizations and monitoring and alerting solutions that make use of timestamps that have been generated on remote/monitored systems. However, using remote-generated timestamps may be risky.

At first glance, microservices logging may seem simple. You just take the same principles you’ve always followed for monoliths and apply them to each microservice in your application, right? Well, no. The differences between microservices and monolithic architecture amount to much more than a difference in the number of services involved.

Most of the IT industry is floating on the cloud these days. From hosting websites to storing data, organizations have started to use the cloud for various purposes, so why not for log monitoring? In this blog, I’ll explain how cloud-based log monitoring is beneficial for you and how you can use SolarWinds® Papertrail™ for it.

Logstash can parse CSV and JSON files easily, but that’s because data in those formats are perfectly organized and ready for Elasticsearch analysis. Sometimes, though, we need to work with unstructured data, like plain-text logs for example. In these cases, we’ll need to parse the data to make it structured data using Logstash Grok. This tutorial will enable you to take full advantage of Elasticsearch’s analysis and querying capabilities by parsing with Logstash Grok.

Serverless computing has taken off in recent years with more efficient cloud services. AWS Lambda is a great example of this, where provisioning and management of resources happens from the service’s end. You only have to deal with the code. This article will give a brief overview of AWS Lambda in contrast to EC2 instances, then walk through shipping AWS Lambda metrics to Logz.io.

TL;DR note: if you want the bzip2 -9 version of this post, scroll down to the very last section for some quick pointers. If you want to learn a bit about Linux system logs, please continue, as we’ll talk about all these and more.