CF

Snowflake for Data Engineers

2h 4m 8s
English
Paid

Embrace the future of data management and analysis with Snowflake, a revolutionary cloud-based data storage solution. With its fully cloud-operated platform, Snowflake offers scalable access to data, seamless integration of various data sources, comprehensive data sharing capabilities, and robust analytical task execution.

Why Snowflake is Essential for Data Engineers

The increasing adoption of Snowflake by major corporations emphasizes the growing demand for proficiency in this platform among data engineers and analysts. Mastering Snowflake involves data preparation and integration, internal data management, and interfacing with external tools and services, making it a vital skill not just for analysts, but also for data engineers.

Course Overview: What You Will Learn

Embark on a practical journey with this course designed to equip you with essential knowledge to start working with Snowflake right away.

Course Content

  • Introduction to Snowflake: Understand what Snowflake is, its user base, and its role in data processing platform architecture.
  • Hands-on Practice:
    • Explore an e-commerce dataset.
    • Set up Snowflake and SnowSQL.
    • Create tables, file formats, and load data using internal staging areas with CSV files from your computer.
  • Advanced Skills:
    • Create visual reports in Snowflake.
    • Connect Power BI to Snowflake for dashboard creation.
    • Execute SQL queries through Python.
    • Configure and run automated tasks.
    • Test a comprehensive ETL pipeline.

Working with AWS and Automation

Conclude the course by learning to integrate Snowflake with AWS S3:

  • Manual Data Import: Import data from external staging areas.
  • Automated Data Loading: Set up automatic loading via Snowpipe.

Course Inclusions

  • Source codes for all exercises.
  • Links to additional resources.
  • Prepared datasets for practice.

Requirements

  • Basic understanding of relational databases.
  • An AWS account for S3 integration.

Additional

https://github.com/team-data-science/snowflake-for-data-engineers

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 (21)
#Lesson TitleDurationAccess
1
Introduction Demo
02:07
2
Snowflake basics
04:17
3
Data Warehousing bascis
04:14
4
How Snowflake fits into data platforms
03:15
5
Snowflake Account setup
04:25
6
Creating your warehouse & UI overview
04:16
7
Our dataset & goals
03:02
8
Setup Snowflake database
10:30
9
Preparing the upload file
08:32
10
Using internal stages with snowsql
12:38
11
Splitting a data table into two tables
06:39
12
Creating a visualization worksheet
07:09
13
Creating of a dashboard
05:24
14
Connect PowerBI to Snowflake
06:04
15
Query data with Python
07:36
16
Create import task
09:19
17
Create table refresh task
03:41
18
Test our pipeline
03:15
19
Working with external stages for AWS S3
10:21
20
Implementing snowpipe with S3
06:20
21
Summary
01:04
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Frequently asked questions

What are the prerequisites for enrolling in this course?
Before taking this course, it is recommended that students have a basic understanding of data warehousing concepts and experience with SQL. Familiarity with cloud-based data platforms will also be beneficial, though not strictly necessary, as the course covers Snowflake setup and usage comprehensively from the basics.
What projects or tasks will I work on during the course?
Students will work on several practical tasks including setting up Snowflake and SnowSQL, creating tables and file formats, loading data using internal staging areas, and connecting Power BI to Snowflake for dashboard creation. Additionally, the course involves querying data with Python and implementing snowpipe with AWS S3 for automated data loading.
Who is the target audience for this course?
This course is designed for data engineers and analysts who want to gain proficiency in Snowflake. It is particularly suitable for those looking to understand how Snowflake fits into modern data processing platform architectures and who wish to enhance their skills in data preparation, integration, and analysis using cloud-based solutions.
How does this course compare to other courses on cloud data platforms?
Unlike other courses that may cover a broad range of cloud data platforms, this course focuses specifically on Snowflake. It provides hands-on experience in setting up and using Snowflake, creating visual reports, and integrating with tools like Power BI. This specialization allows for a deeper understanding of Snowflake's capabilities and applications in real-world scenarios.
What specific tools or platforms will I learn to use?
In this course, students will learn to use Snowflake and SnowSQL for data management, Power BI for creating dashboards, and Python for querying data. Additionally, the course covers working with external stages on AWS S3 and implementing snowpipe for data automation tasks.
What topics are not covered in this course?
This course does not cover other cloud data platforms such as AWS Redshift, Google BigQuery, or Microsoft Azure Synapse in detail. It also does not delve into advanced data science techniques or machine learning models, focusing instead on the practical aspects of data management and integration using Snowflake.
How much time should I expect to commit to this course?
The course consists of 21 lessons, each designed to be concise yet informative. While the total runtime is not specified, students should anticipate dedicating several hours per week to watching lessons, participating in hands-on exercises, and completing tasks to gain full proficiency in using Snowflake.