elasticsearch-mapper-attachments VS kubernetes

Elasticsearch - www.elastic.co/

The increasing complexity of modern systems led to the rise of AIOps (Artificial Intelligence for IT Operations) and observability practices. AIOps leveraged machine learning algorithms to automate problem detection, analysis, and resolution. Observability focused on gaining insights into system behaviour through metrics, logs, and traces. As a result, tools like Prometheus, Grafana, and ELK stack (Elasticsearch, Logstash, Kibana) gained popularity.

My only experience with NetFlow collection is on my home firewall/router running pfSense Community Edition, which is free to download and can be installed on a wide assortment of X86 hardware. I installed the Softflowd package, which exports NetFlow data to a dedicated Elasticsearch/Logstash/Kibana (ELK) server on my LAN. I believe Security Onion and ElastiFlow also can be NetFlow collectors.

Elasticsearch, Logstash, and Kibana (ELK) Stack: An open source suite of tools for log management and analysis, providing real-time insights into security events.

Recently I have taken an interest in big data. https://neo4j.com/ , https://cassandra.apache.org/ , https://clickhouse.com/, https://www.elastic.co/ - are all databases I have experience with. Neo4j and Cassandra only as a hobby, but Clickhouse I have used in production, and Elasticsearch I have used for some 7 years now.

Buy an enterprise-class, wired router like the Negate 2100 ($349 USD), which runs pfSense, and configure the Deco AXE5400 device(s) to operate in Access Point Mode. Then install the Softflowd package through the pfSense web UI. Softflowd will collect and export NetFlow data to a NefFlow collector, which is the separate computer/VM/container referred to above, running software like Security Onion, ElastiFlow, or Elasticsearch/Logstash/Kibana (ELK).

If you’re unaware, elastic search is some like enterprise level search shit. They just put it in a theme. https://www.elastic.co

systemctl status kibana ● kibana.service - Kibana Loaded: loaded (/etc/systemd/system/kibana.service; enabled; vendor preset: enabled) Active: active (running) since Tue 2023-03-28 09:40:05 UTC; 33min ago Docs: https://www.elastic.co Main PID: 3168 (node) Tasks: 11 (limit: 9432) Memory: 303.3M CPU: 35.190s CGroup: /system.slice/kibana.service └─3168 /usr/share/kibana/bin/../node/bin/node /usr/share/kibana/bin/../src/cli/dist --logging.dest=/var/log/kibana/kibana.log --pid.file=/run/kibana/kibana.pid "--deprecation.skip_deprecated_settings[0]=logging.dest" Mar 28 09:40:05 wazuh systemd[1]: Started Kibana.

In the field of software development, efficiency and agility are always sought after. In the era of cloud-native apps, traditional deployment techniques—which are frequently laborious and prone to errors—are starting to become obstacles. This is when Kubernetes and GitOps come in handy.

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

Securing access to your Kubernetes clusters is crucial as inadequate security measures can lead to unauthorized access and potential data breaches. However, navigating the complexities of Kubernetes access security, especially when setting up strong authentication, authorization, and network policies, can be challenging.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.