Nouveauté

Data Engineering for Cybersecurity. Build Secure Data Pipelines with Free and Open-Source Tools

Par : James Bonifield
Offrir maintenant
Ou planifier dans votre panier
Disponible dans votre compte client Decitre ou Furet du Nord dès validation de votre commande. Le format ePub est :
  • Compatible avec une lecture sur My Vivlio (smartphone, tablette, ordinateur)
  • Compatible avec une lecture sur liseuses Vivlio
  • Pour les liseuses autres que Vivlio, vous devez utiliser le logiciel Adobe Digital Edition. Non compatible avec la lecture sur les liseuses Kindle, Remarkable et Sony
Logo Vivlio, qui est-ce ?

Notre partenaire de plateforme de lecture numérique où vous retrouverez l'ensemble de vos ebooks gratuitement

Pour en savoir plus sur nos ebooks, consultez notre aide en ligne ici
C'est si simple ! Lisez votre ebook avec l'app Vivlio sur votre tablette, mobile ou ordinateur :
Google PlayApp Store
  • Nombre de pages344
  • FormatePub
  • ISBN978-1-7185-0403-5
  • EAN9781718504035
  • Date de parution26/08/2025
  • Protection num.pas de protection
  • Taille4 Mo
  • Infos supplémentairesepub
  • ÉditeurNo Starch Press

Résumé

Turn raw logs into real intelligence. Security teams rely on telemetry-the continuous stream of logs, events, metrics, and signals that reveal what's happening across systems, endpoints, and cloud services. But that data doesn't organize itself. It has to be collected, normalized, enriched, and secured before it becomes useful. That's where data engineering comes in. In this hands-on guide, cybersecurity engineer James Bonifield teaches you how to design and build scalable, secure data pipelines using free, open source tools such as Filebeat, Logstash, Redis, Kafka, and Elasticsearch and more.
You'll learn how to collect telemetry from Windows including Sysmon and PowerShell events, Linux files and syslog, and streaming data from network and security appliances. You'll then transform it into structured formats, secure it in transit, and automate your deployments using Ansible. You'll also learn how to: Encrypt and secure data in transit using TLS and SSH Centrally manage code and configuration files using Git Transform messy logs into structured events Enrich data with threat intelligence using Redis and Memcached Stream and centralize data at scale with Kafka Automate with Ansible for repeatable deployments Whether you're building a pipeline on a tight budget or deploying an enterprise-scale system, this book shows you how to centralize your security data, support real-time detection, and lay the groundwork for incident response and long-term forensics.
Turn raw logs into real intelligence. Security teams rely on telemetry-the continuous stream of logs, events, metrics, and signals that reveal what's happening across systems, endpoints, and cloud services. But that data doesn't organize itself. It has to be collected, normalized, enriched, and secured before it becomes useful. That's where data engineering comes in. In this hands-on guide, cybersecurity engineer James Bonifield teaches you how to design and build scalable, secure data pipelines using free, open source tools such as Filebeat, Logstash, Redis, Kafka, and Elasticsearch and more.
You'll learn how to collect telemetry from Windows including Sysmon and PowerShell events, Linux files and syslog, and streaming data from network and security appliances. You'll then transform it into structured formats, secure it in transit, and automate your deployments using Ansible. You'll also learn how to: Encrypt and secure data in transit using TLS and SSH Centrally manage code and configuration files using Git Transform messy logs into structured events Enrich data with threat intelligence using Redis and Memcached Stream and centralize data at scale with Kafka Automate with Ansible for repeatable deployments Whether you're building a pipeline on a tight budget or deploying an enterprise-scale system, this book shows you how to centralize your security data, support real-time detection, and lay the groundwork for incident response and long-term forensics.