Modern Data Warehouses & Data Lakes

58m 9s
English
Paid

As a data engineer, being adept in working with analytical platforms is crucial. This course focuses on the use of Data Lakes and Data Warehouses, which are essential for building visualizations and creating machine learning models.

Course Overview

Modern data warehouses, such as AWS Redshift, Google BigQuery, and Snowflake, have revolutionized the way we handle data. They allow seamless integration by loading data directly from files in a Data Lake, offering flexibility and convenience for analytical tasks.

What You Will Learn

  • Utilization of Data Lakes, Data Warehouses, and BI tools within a unified system
  • Loading data into Data Lakes and visualizing it in reports
  • Building integrations in Google Cloud Platform and AWS
  • Understanding and applying ETL/ELT architecture in modern data warehouses

Course Modules

Basics of Data Warehouses and Data Lakes

  • The role of data warehouses in analytical platforms
  • Loading data into a Data Warehouse via ETL/ELT
  • Understanding Data Lakes and their utilization
  • Working with files directly within a Data Lake

Practice on GCP: Cloud Storage, BigQuery, and Data Studio

  • Setting up Cloud Storage and creating a table in BigQuery
  • Data visualization in Data Studio
  • Grasping the general principles of cloud platforms

Practice on AWS: S3, Athena, Glue, and Quicksight

  • Creating data integration through S3, Athena, and Quicksight
  • Setting up Glue Data Catalog for data management
  • Detailed setup and integration of Glue

Summary and Bonus Lesson: AWS Redshift Spectrum

  • Course summary
  • Additional module on working with Redshift Spectrum using the prepared Data Catalog from the AWS project

Prerequisites

To make the most of this course, you should have:

  • Basic experience with Data Warehouses (Completing the "Data Warehouses" course from our academy is recommended)
  • Basic knowledge of AWS Athena and Redshift (for the Redshift Spectrum module, a prepared Data Catalog from the AWS project will be utilized)

This course will enhance your proficiency in modern data storage and processing systems, teaching you how to effectively leverage Data Lakes and Data Warehouses for analytics.

About the Author: Andreas Kretz

Andreas Kretz thumbnail

I am a senior data engineer and trainer, a tech enthusiast, and a father. For more than ten years, I have been passionate about Data Engineering. Initially, I became a self-taught data engineer and then led a team of data engineers at a large company. When I realized the great demand for education in this field, I followed my passion and founded my own Data Engineering Academy. Since then, I have helped over 2,000 students achieve their goals.

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#1: Introduction
All Course Lessons (14)
#Lesson TitleDurationAccess
1
Introduction Demo
02:14
2
Data Science Platform
04:11
3
ETL & ELT Data Warehouse
06:23
4
Data Lake & Data Warehouse integration
03:30
5
GCP & AWS Piplines we build
03:15
6
GCP hands on Cloud Storage & BigQuery
08:36
7
GCP hands on create Data Studio dashboard
07:34
8
GCP Recap & AWS goals
02:13
9
AWS Setup & upload data to S3
02:13
10
Athena Data Lake manual table configuration
03:49
11
Creating a Quicksight dashboard
05:06
12
Athena configuration using AWS Glue data catalog
03:30
13
Course recap
02:37
14
BONUS Configure Redshift Spectrum table with S3
02:58
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