Operations | Monitoring | ITSM | DevOps | Cloud

We are excited to announce the general availability of version 7.6 of the Elastic Stack. This release streamlines automated threat detection with the launch of a new SIEM detection engine and a curated set of detection rules aligned to the MITRE ATT&CK™ knowledge base, brings performance improvements to Elasticsearch, makes supervised machine learning more turnkey with inference-on-ingest features, and deepens cloud observability and security with the launch of new data integrations.

Today, most enterprises rightfully think about their business strategies by leveraging available data. Data-driven decisions certainly are more solid and reliable than those based upon mere instinct, intuition or just plain mysticism. Logs, in particular, are a fantastic source of information from which a company can draw to fuel its business intelligence (BI) strategies. However, there’s a big and sometimes unbridgeable gap between theory and practice.

LogDNA is releasing Custom Webhooks, enabling customers to easily integrate LogDNA alerts with 3rd-party services. Custom Webhooks allows you to configure a webhook’s header and body and add specific LogDNA tokens to your alerts going through that webhook.

We highly recommend that all App Search users keep their deployments up to date with the latest available version to have access to new features, security updates, and performance improvements. This guide is designed to help customers through the upgrade process, to minimize the impact of an upgrade on production environments, and to ensure data safety during an upgrade. Finally, the guide helps App Search users troubleshoot any issues that may occur during an upgrade.

Snyk is a developer-centric company whose raison d’être is to identify and patch vulnerabilities in open source security software. With about 50 engineers, Snyk VP Engineering Anton Drukh wants to maintain flexibility in how the team operates. The best way to ensure that is to give them as much insight into their own work as possible, and hence options. They also look at the state of open source security across the industry.

When Splunk was first released almost 20 years ago, it helped many organizations realize the power of logs to gain business insights with pricing based on the volume of data ingested per day. Over the last two decades, the volume, variety, and velocity of data generated by systems and users have grown exponentially. The demands of business and operations have quickly moved beyond compliance and basic reporting.

ClickHouse is an open source database management system, and was originally developed as a backend for Yandex’s Metrica analytics platform. ClickHouse is column oriented, meaning that it can quickly scan through ranges of values in a single column without touching irrelevant values in other columns. This makes ClickHouse well suited for online analytical processing (OLAP).

Jupyter Notebooks are wonderful because they provide a way to share code, explanations, and visualizations in the same place. Notebooks add narrative to computation. The cells compartmentalize steps and reduce the fear or hesitation associated with editing code. In this way, notebooks act as an invitation for experimentation. Today, I want to extend that invitation and apply it to InfluxDB. In this post, we’ll learn how to query our system stats data from InfluxDB v2.0 using Flux.