Snowflake 101 – Build & Design Data Pipelines On Aws Cloud

What you'll learn
Everything needed for Snowpro Advanced Data engineering certification
Snowflake as a data-warehouse & automated pipelines within snowflake ecosystem
Use AWS Cloud with Snowflake as a data-warehouse
Integrating real time streaming data with Kafka and Snowflake
Requirements
Prior programming experience in SQL and python is a must.
Prior basic experience or understanding of cloud services like AWS is important.
Description
Get ready for Cloud Data Warehousing with Snowflake and AWS complete course. Gain familiarity with the course details and topics designed to help you succeed.This comprehensive course is designed to take you on a journey through the powerful combination of Snowflake and Amazon Web Services (AWS), two industry-leading solutions that together offer a cutting-edge cloud data warehousing platform. Snowflake is renowned for its performance, simplicity, and versatility, while AWS provides a robust and secure cloud infrastructure that supports an array of services.Learn about Snowflake on AWS with Hands-On LabsThe Cloud Data Warehousing with Snowflake and AWS is a hands-on practice course designed to familiarize you with the core functionality of Snowflake by connecting it with AWS. Through hands-on exercises, you'll gain a thorough understanding of Snowflake's architecture and how it revolutionizes data warehousing in the cloud. You'll explore the seamless integration of Snowflake with AWS services, such as Amazon S3 and Glue, unlocking a world of possibilities for managing and analyzing your data.The course comprises approximately 20 labs starting from the basics and moving to high levels in terms of complexity.Who should take this course?The course is intended for Data engineers responsible for designing, building, and maintaining data pipelines and data warehouses in the cloud. They will learn how to leverage Snowflake and AWS to create scalable and performant data warehousing solutions. Data analysts seeking to enhance their data manipulation and analytics skills using cloud-based tools will find value in this course. They will learn how to work with Snowflake and AWS services to query, transform, and analyze data effectively. IT professionals and cloud architects interested in understanding cloud data warehousing principles, architecture, and implementation using Snowflake and AWS will find this course beneficial.RequirementsBasic knowledge of SQL or writing queries in any languageScripting in Python (or any language )Willingness to explore, learn and put in the extra effort to succeedAn active AWS Account & know-how of basic cloud fundamentalsWho this course is for:Software engineers, aspiring data engineers, or data analysts & data scientistsAlso good for programmers and database administrators with experience in writing SQL queriesWhat you'll learnEverything needed for Snowpro Advanced Data Engineering certificationSnowflake as a data-warehouse & automated pipeline within Snowflake ecosystemUse AWS Cloud with Snowflake as a data-warehouseIntegrating real-time streaming data with Kafka and SnowflakeAre there any course requirements or prerequisites?Prior programming experience in SQL and Python is a must.Prior basic experience or understanding of cloud services like AWS is importantWho this course is for:Software engineers, aspiring data engineers, or data analysts & data scientistsAlso good for programmers and database administrators with experience in writing SQL queries
Overview
Section 1: Introduction
Lecture 1 Introduction to Snowflake
Lecture 2 Snowflake Architecture
Lecture 3 Demo: Setting Up Snowflake Account
Section 2: Snowflake Tables
Lecture 4 Types of Tables
Lecture 5 Demo: Creating Tables in Snowflake
Section 3: Snowflake Views
Lecture 6 Types of Views
Lecture 7 Demo: Creating Views in Snowflake
Section 4: Partitioning and Clustering
Lecture 8 Creating Micropartitions And Clustering Keys
Section 5: Data Loading, Ingestion and Extraction
Lecture 9 Creating an Integration Object to Connect Snowflake with AWS S3
Lecture 10 Ingesting CSV from S3 to Snowflake
Lecture 11 Ingesting JSON from S3 to Snowflake
Section 6: Roles in Snowflake
Lecture 12 Creating roles and granting access
Section 7: Streams in Snowflake
Lecture 13 Introduction to Streams
Lecture 14 Demo: Implementing Standard Streams
Lecture 15 Demo: Implementing Append-Only Streams
Section 8: Snowflake with Python and AWS Glue
Lecture 16 Connecting Python with Snowflake on Your Local Machine
Lecture 17 Deployment and Execution of Python Script on AWS Glue
Lecture 18 Parameterizing Your Python Script on AWS Glue
Lecture 19 Python Pandas with Snowflake on AWS Glue
Section 9: Instantaneous data streaming - Kafka with Snowflake
Lecture 20 Setting Up Kafka on Local Machine
Lecture 21 Setting up Kafka Snowflake Connector
Lecture 22 Setting Up Encryption Keys
Lecture 23 Real-time Data Streaming with Kafka
Section 10: Snowflake - Zero Copy Cloning
Lecture 24 Zero Copy Cloning in Snowflake
Lecture 25 Clone VS Snapshot
Lecture 26 Schema level cloneable Objects in Snowflake
Lecture 27 Demo: Simple Table Cloning
Lecture 28 Demo: Transient and temporary table cloning
Lecture 29 Demo: Object Dependency with clone
Lecture 30 Demo: Clone File Formats and sequences
Lecture 31 Demo: Clone Stream Objects
Section 11: Snowflake - Snowsight
Lecture 32 Creating Dashboard using Snowsight
Lecture 33 Dashboard Filters
Lecture 34 Dashboard for Covid-19 dataset
Section 12: Time Travel in Snowflake
Lecture 35 What is Time Travel?
Lecture 36 Time Travel vs FailSafe
Lecture 37 Demo: How to enable Time travel
Lecture 38 Demo: Drop & Undrop table, schema and database
Lecture 39 Demo: Clone with Time Travel
Section 13: Tasks in Snowflake
Lecture 40 Creating Standalone and Dependent Tree of Tasks
Software Engineers, Aspiring Data Engineers or Data Analyst & Data Scientists.,Also good for programmers and database administrators with experience in writing SQL queries.
Homepage

https://www.udemy.com/course/snowflake-101-build-design-data-pipelines-on-aws-cloud/