Modern Data Warehouses & Data Lakes

58m 9s
English
Paid
As a data engineer, you will regularly work with analytical platforms where companies store data in Data Lakes and Data Warehouses for building visualizations and creating machine learning models. Modern data warehouses, such as AWS Redshift, Google BigQuery, and Snowflake, allow you to load data directly from files in a Data Lake. This integration makes working with warehouses flexible and convenient for analytical tasks.

In this course you will learn:

  • How to use Data Lakes, Data Warehouses, and BI tools in a unified system
  • How to load data into Data Lakes and visualize it in reports
  • How to build integrations in Google Cloud Platform and AWS
  • How ETL/ELT architecture works and how to apply it in modern data warehouses

Basics of Data Warehouses and Data Lakes

  • The role of data warehouses in analytical platforms
  • How data is loaded into Data Warehouse through ETL/ELT
  • What Data Lakes are and how to use them
  • How to work with files directly in the data lake

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

  • Setting up Cloud Storage, creating a table in BigQuery
  • Data visualization in Data Studio
  • Understanding 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

Required knowledge

  • Basics of working with Data Warehouses (it is recommended to take the "Data Warehouses" course in the academy)
  • Basic knowledge of AWS Athena and Redshift (for the block with Redshift Spectrum, a prepared Data Catalog from the AWS project is used)

This course will help you master modern approaches to building data storage and processing systems and learn how to effectively use the capabilities of 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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