Updated getting started guide

Author: JPHaus

FAQ/FAQ.md

@@ -3,6 +3,7 @@ Aliases: [frequently asked questions]
 Tags: [seedling]
 publish: true
 ---
+## Frequently Asked Questions
 
 > [!faq]- ### What is Data Engineering?
 > ![[What is Data Engineering|What is data engineering?]]

Guides/Getting Started With Data Engineering.md

@@ -10,11 +10,15 @@ This guide is intended for people who are new to Data Engineering and aren't sur
 
 ## Step 1: Read the FAQ
 
-![[FAQ]]
+![[FAQ#Frequently Asked Questions]]
 
 ## Step 2: Learn Data Engineering core concepts
 
-![[Concepts#Core Concepts]]
+- Understand the basics of a [[Data Pipeline]]
+- [[Batch Data Processing]] vs [[Stream Data Processing]]
+- [[Online Transaction Processing|Online Transaction Processing (OLTP)]] vs [[Online Analytical Processing| Online Analytical Processing (OLAP)]]
+- [[Relational Database]] vs [[Non-relational Database]]
+- [[Data Modeling]]: Similar to OLTP vs OLAP, read about [[Relational Modeling]] vs [[Dimensional Modeling]]
 
 ## Step 3: Learn the core tools
 

Tools/Tools.md

@@ -7,8 +7,8 @@ publish: true
 ## Core Tools
 
 - Learn [[SQL]], the most widely used language for interacting with data.
-- Learn a scripting language like [[Python]], [[Java]], or [[Scala]] which are currently the most popular languages for DE.
-- Learn how to use a set of cloud tools like [[Amazon Web Services]], [[Microsoft Azure]], or [[Google Cloud Platform]].
+- Learn [[Python]] which is the most popular programming language in data engineering. [[Java]] and [[Scala]] are also used for more niche use cases.
+- Learn how to use a set of cloud tools like [[Amazon Web Services]], [[Microsoft Azure]], or [[Google Cloud Platform]]. Choose AWS if you're unsure because it has the most [marketshare](https://www.statista.com/chart/18819/worldwide-market-share-of-leading-cloud-infrastructure-service-providers/) and thus more job opportunities.
 
 ## Databases