Data Driven Dashboards with Kibana and Elasticsearch
Data Driven Dashboards with Kibana and Elasticsearch by Philipp Krenn
Operations | Monitoring | ITSM | DevOps | Cloud
The Place Where Modern Operations & Technology Come Together
SEMplicity: Scaling Large ECE Deployments
From the trenches: what does it really take to scale up a large Elastic security log deployment? Elasticsearch for enterprise security log storage & management is a hot topic today. Specular gains in performance, functionality and cost are ready for harvest. But what exactly does it take to create a large Elastic log storage infrastructure? This talk will present war stories related to at 150,000 events per second Elastic log storage implementation with 2 month retention built at a large commercial client.
Virtual Meetup: Elastic Workplace Search-Finding Where That Document Went
Supporting employees in a work from a home environment can be challenging. Do they have access to these systems? Can they locate the documents they need? This would be easier if documents are centrally located. In most organizations, documents can live in services like Dropbox, Goole Drive, and or Github. In this virtual meetup, I will show you how to create a search box between these different services and finally solve the question.. "Where did that document go?"
Visualizing observability with Kibana: Event rates and rate of change in TSVB
When working with observability data, a good portion of it comes in as time series data — things like CPU or memory utilization, network transfer, even application trace data. And the Elastic Stack offers powerful tools within Kibana for time series analysis, including TSVB (formerly Time Series Visual Builder). In this blog post, I’m going to attempt to demystify rates in TSVB by walking through three different types: positive rates, rate of change, and event rates.
How to design your Elasticsearch data storage architecture for scale
Elasticsearch allows you to store, search, and analyze large amounts of structured and unstructured data. This speed, scale, and flexibility makes the Elastic Stack a powerful solution for a wide variety of use cases, like system observability, security (threat hunting and prevention), enterprise search, and more. Because of this flexibility, effectively architecting your deployment’s data storage for scale is incredibly important.
Virtual safety: How to teach your kids cybersecurity best practices
Confession: I am a security practitioner. I am also a mom. What I am not is a homeschool teacher. Earlier this year, I spoke to the 5th- and 6th-grade classes at my son’s Innovation Day about cybersecurity. I discussed what it means to be a cybersecurity practitioner and how the practice of cybersecurity affects everyday life.
Generating MITRE ATT&CK® signals in Elastic SIEM: Sysmon data
Many mature security teams look to the MITRE ATT&CK® matrix to help improve their understanding of attacker tactics, techniques, and procedures (TTPs) and to better understand their own capabilities relative to these common adversarial approaches. With the release of Elastic Security 7.6, Elastic SIEM saw 92 detection rules for threat hunting and security analytics aligned to ATT&CK.
Elastic Cloud Enterprise 2.5: Dedicated coordinating layer, snapshot lifecycle management, and more
We’re excited to announce the release of Elastic Cloud Enterprise (ECE) 2.5! This release improves the experience of managing your deployments with a dedicated coordinating layer, support for snapshot lifecycle management (SLM), and more.
Virtual Meetup: Elastic Community AMA Hour - Search
Ask Me Anything hour with Elastic Developer Advocates
Visualizing COVID-19 with Elastic
There are various publicly available data-sources that we may leverage to visualize the COVID-19 pandemic. We will use the materials created by SiScale (https://www.siscale.com/) as they have composed a rather elegant means of ingesting COVID-19 data. This document is designed to be an instructional guide on how to implement this solution locally or with the Elastic Cloud. We will first configure the necessary components, ingest the data source, then show how to create the additional Machine Learning jobs as well as creating a Canvas dashboard.