Zentrales logging mit dem Elastic Stack

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The document discusses centralized logging with the Elastic Stack. It describes the components of the Elastic Stack including Beats, Logstash, Elasticsearch, and Kibana. It explains how Beats collects log data and sends it to Logstash for ingestion into Elasticsearch for storage, search, and analysis. Kibana is used for visualizing and managing the logged data. The document provides an example of using the Elastic Stack with Filebeat, Logstash, and Elasticsearch for log ingestion and analysis of access logs from a web server. It discusses benefits like reduced costs using a hot-warm architecture with the Elastic Stack.

Technology

Zentrales Logging
mit dem Elastic Stack
Simon Schneider
Froscon 2019

Kibana
Elasticsearch
Beats Logstash
SaaS Self Managed
Elastic Stack
Visualize & Manage
Store, Search & Analyze
Ingest
DeploymentElastic Cloud Elastic Cloud
Enterprise
Standalone

Hot Node Warm NodeMaster Node
● Nur Management
● Keine Datenhaltung
● SSD (RAID 0)
● Query
● Ingest
● Letzten 1-3 Tage
● HDD
● Query
● > 3 Tage
Hot-Warm Architektur

Docker Nodes
Ingest Nodes
Data NodesKibana
Nodes
REWE Digital Ecom - Elastic Stack

REWE Digital Big Data - Elastic Stack
https://github.com/elastic/cloud-on-k8s

Web Server
● Static HTML
● Proxy Route
Such-Backend
● Such-Endpunkt
● Bleve Search
Filebeat Logstash Elasticsearch
● Access Log Aufbereitung
● Geo IP Parser
● User Agent Parser

➔ Großer Initialaufwand / Kosten
➔ Hot-Warm Architektur zur Kostensenkung
➔ JSON Logs sind einfach zu verarbeiten
➔ Filebeat + Elasticsearch kann ausreichend sein
➔ Sicherheit, Datenschutz und Schnelligkeit gewonnen
Wrap Up

Thank you
Special Thanks to:
- Peter Larsen
- Paul Puschmann
twitter.com/raynigon
github.com/raynigon

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Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place. Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects. Here’s what you’ll gain: - Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows. - Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy. - Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency. - Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity. We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic. Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.

LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...

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Do you want to learn how to model and simulate an electrical network from scratch in under an hour? Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)! During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook. PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides: - A fully editable and extendable library for grid component modelling; - Visualization tools to display your network; - Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses; The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well. What you will learn during the webinar: - For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills; - For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.

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💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™: See how to accelerate model training and optimize model performance with active learning Learn about the latest enhancements to out-of-the-box document processing – with little to no training required Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath. Speakers: 👨‍🏫 Andras Palfi, Senior Product Manager, UiPath 👩‍🏫 Lenka Dulovicova, Product Program Manager, UiPath

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Zentrales logging mit dem Elastic Stack

1. Zentrales Logging mit dem Elastic Stack Simon Schneider Froscon 2019

8. Simon Schneider

9. Zentrales vs Dezentrales Loggingi

10. Zentrales LoggingDezentrales Logging

11.

12.

13.

14.

15.

16.

17. Elastic Stacki

18. Beats

19. Logstash

20. Beats Logstash Ingest

21. Elasticsearch

22. Kibana Elasticsearch Beats Logstash SaaS Self Managed Elastic Stack Visualize & Manage Store, Search & Analyze Ingest DeploymentElastic Cloud Elastic Cloud Enterprise Standalone

23. Hot Node Warm NodeMaster Node ● Nur Management ● Keine Datenhaltung ● SSD (RAID 0) ● Query ● Ingest ● Letzten 1-3 Tage ● HDD ● Query ● > 3 Tage Hot-Warm Architektur

24.

25. Docker Nodes Ingest Nodes Data NodesKibana Nodes REWE Digital Ecom - Elastic Stack

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27. REWE Digital Big Data - Elastic Stack https://github.com/elastic/cloud-on-k8s

28. Live Coding

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30. Web Server ● Static HTML ● Proxy Route Such-Backend ● Such-Endpunkt ● Bleve Search Filebeat Logstash Elasticsearch ● Access Log Aufbereitung ● Geo IP Parser ● User Agent Parser

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32. ➔ Großer Initialaufwand / Kosten ➔ Hot-Warm Architektur zur Kostensenkung ➔ JSON Logs sind einfach zu verarbeiten ➔ Filebeat + Elasticsearch kann ausreichend sein ➔ Sicherheit, Datenschutz und Schnelligkeit gewonnen Wrap Up

33. Thank you Special Thanks to: - Peter Larsen - Paul Puschmann twitter.com/raynigon github.com/raynigon

34. ● Verschiedene Logos de.wikipedia.org ● RRZE Icon Set ● Morning Coffee Photo by David Mao on Unsplash ● Shock Photo by David Mao on Unsplash ● Work Photo by Glenn Carstens-Peters on Unsplash ● Office Photo by Tim van der Kuip on Unsplash ● Folders Photo by Samuel Zeller on Unsplash ● Light Building Photo by Lysander Yuen on Unsplash ● Building a House Photo by Randy Fath on Unsplash ● Trophy Photo by Fauzan Saari on Unsplash ● Ecommerce by Mein Deal on Unsplash ● Big Data by ev on Unsplash ● Burning Money by Jp Valery on Unsplash ● Rocket by Bill Jelen on Unsplash Credits

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