CourseFlix

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

Andreas Kretz is a German data engineer and one of the most widely followed independent voices on data engineering as a career discipline. He runs the Plumbers of Data Science brand and has been publishing tutorial material continuously since the field consolidated around the modern lake-house stack (Spark, Kafka, Snowflake, Databricks, Airflow).

His CourseFlix listing is the largest single-author catalog under this source — over thirty courses spanning data-pipeline construction, streaming architectures, the cloud-native data stack on AWS / Azure / GCP, the Python and Scala tooling that dominates the field, and the soft-skills / career side of breaking into data engineering. Material is paid and aimed at engineers transitioning into data work or already-working data engineers picking up specific tools.

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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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Course content

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

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Frequently asked questions

What is Modern Data Warehouses & Data Lakes about?
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…
Who teaches Modern Data Warehouses & Data Lakes?
Modern Data Warehouses & Data Lakes is taught by Andreas Kretz. You can find more courses by this instructor on the corresponding source page.
How long is Modern Data Warehouses & Data Lakes?
Modern Data Warehouses & Data Lakes contains 14 lessons with a total runtime of 58 minutes. All lessons are available to watch online at your own pace.
Is Modern Data Warehouses & Data Lakes free to watch?
Modern Data Warehouses & Data Lakes is part of CourseFlix's premium catalog. A CourseFlix subscription unlocks the full video player; the course description, table of contents, and preview information are available to everyone.
Where can I watch Modern Data Warehouses & Data Lakes online?
Modern Data Warehouses & Data Lakes is available to watch online on CourseFlix at https://courseflix.net/course/modern-data-warehouses-data-lakes. The page hosts every lesson with the integrated video player; no download is required.