Operations | Monitoring | ITSM | DevOps | Cloud

Elastic is prepping for Elasticsearch 8.0, but in the meantime is rolling out upgrades and features with Elasticsearch 7.7. The new version introduces asynchronous search as well as changes with Elasticsearch clusters, mapping, SQL enhancements, snapshots and machine learning. This post will cover just a few of the highlights of the new release. Besides asynchronous search, ES 7.7 also introduces multi-class classification, reduced heap usage, inference time features, and better password security.

At STRABAG, we are using Elastic Cloud Enterprise (ECE) for two main use cases within our on-premises web applications. One to power different kinds of search and a second for operations where we ship more than 25,000 log entries per minute to Elastic from our load balancers. The ECE platform runs in an air-gapped environment, and we would still like to be able to use our corporate logins for the ECE platform.

At Elastic, we love data. It’s the backbone of what we do: search. And that data takes many forms. A knowledge base article showing how to reset your cable box — data. Logs from your network — data. IP addresses accessing your secure network — data. A video tutorial on how adults can use TikTok — data. The list goes on. But behind each piece of data is a story. And a person. Or a customer.

Data ingestion and data analysis are the yin and yang of a time series platform. There are many resources to help you ingest data. Typical ingestions are agent-based, imports via CSVs, using client libraries, or via third-party technologies. Once your time series data arrives, analysis completes the circle and often leads to additional data collection, and so on and so forth.

If you are a software engineer, there's a good chance that deep learning will inevitably become part of your job in the future. Even if you're not building the models that directly use CNNs, you might have to collaborate with data scientists or help business partners better understand what is going on under the hood. In this article, Julie Kent dives into the world of convolutional neural networks and explains it all in a not-so-scary way.

This post is the second in our series on system metrics where we cover: In the previous post, we went through some built-in tools and methods for identifying key metrics and values on your systems. In this post, we'll provide a tutorial on how to use Metricbeat to consolidate metrics, store and analyze them in the long term, and discuss some of the benefits of a centralized metric store.