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Linux Security

Securing User Accounts

This lesson focuses on foundational user account security configurations. We will start by hardening user home directories, analyzing the differing default permissions between Red Hat (700) and Debian (755) based systems, and configuring a secure UMASK of 077 in "/etc/login.defs". We will then transition to enforcing strong password policies, discussing the modern approach of passphrases over complex rules. The core of this section is a hands-on configuration of the "pam_pwquality" module via "/etc/security/pwquality.conf", where we will set parameters like "minlen", "minclass", and credit-based options (dcredit, ucredit) to enforce robust password creation. Practical exercise: Configure a policy requiring a minimum password length of 14 characters, containing at least 3 character classes (uppercase, lowercase, digits, special).

2 lessons•1h total
With Frank
Goal: The user will be able to audit and configure secure default home directory permissions and implement strong, modern password quality policies on both Red Hat and Debian-family Linux systems.
AWS Data Analytics

Data Storage

Introduction to AWS object storage with Amazon S3. Covers core concepts like buckets, objects, and keys. Detailed exploration of the S3 consistency model (read-after-write for new objects, eventual for updates/deletes). Deep dive into S3 Storage Classes: Standard, Intelligent-Tiering, Standard-IA, One Zone-IA, and the Glacier tiers. The session will focus on the role of S3 in modern data architecture as a scalable, durable, and cost-effective data lake storage layer, emphasizing the schema-on-read approach.

6 lessons•3h total
With Frank
Goal: Differentiate between S3 storage classes and select the most cost-effective option for various data access patterns. Articulate why S3 is the ideal foundation for a data lake.
AWS Data Analytics

Data Visualization

This lesson introduces the core concepts of data visualization within the AWS ecosystem, focusing on Amazon QuickSight. It begins by identifying different types of data consumers (from data experts to business users) and their needs. We will then dive into QuickSight as a cloud-native, serverless BI service, exploring its key differentiators, such as the SPICE in-memory engine for high performance. The session will cover how to connect to various AWS and third-party data sources, the distinction between a "Data Source" and a "Dataset", and the fundamental steps of data preparation within the QuickSight UI. You will learn the basics of the analysis workspace, including the concepts of dimensions and measures, and how to use AutoGraph to create your first visual.

2 lessons•1h total
With Joanna
Goal: By the end of this lesson, you will be able to explain the role of QuickSight in the AWS analytics stack, describe the function of the SPICE engine, and understand the process of connecting data and creating a basic data visualization.

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