Data Engineering With Snowflake And Aws
Data Engineering With Snowflake And Aws
Published 1/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.58 GB | Duration: 4h 24m
Learn the main tasks a Data Engineer perform on Snowflake with AWS
What you'll learn
Data Engineering
Snowflake
SQL
Extraction, Transformation and Data Loading
AWS
ETL
Requirements
Familiarity with SQL is recommended but not mandatory
Familiarity with AWS is recommended but not mandatory
Description
Snowflake course for data engineersThis comprehensive Snowflake course is designed for data engineers who want to improve their ability to efficiently and scalably manage data in the cloud. With a hands-on focus, participants will be guided from the basics to advanced concepts of the Snowflake platform, which provides a modern and fully managed data warehouse architecture.Benefits of using Snowflake for data engineering: Elastic scalability: one of the key benefits of Snowflake is its cloud data storage architecture, which allows for elastic scalability. This means that data engineers can easily scale resources on demand to efficiently handle variable workloads and ensure consistent performance regardless of data volume.Simplified data sharing: Snowflake offers a unique approach to sharing data across departments and teams. Using the concept of secure and controlled data sharing, data engineers can create a single data source that promotes efficient collaboration and consistent data analysis across the organisation.Seamless integration with analytics tools: Snowflake is designed to integrate seamlessly with a variety of data analytics tools, allowing data engineers to create complete ecosystems for advanced data analysis. Compatibility with standard SQL makes it easy to migrate to the platform, while interoperability with popular tools such as Tableau and Power BI expands options for data visualisation and exploration.In this course we deal with:Snowflake basicsPlatform architectureVirtual warehouses - the clustersWorking with semi-structured dataIntegrating Snowflake with AWSUsing Stages, Storage Integration, and SnowpipesUsing AWS S3, SQS, IAMAutomatic ingestion of data in near real time
Overview
Section 1: 1. Introduction
Lecture 1 1.1 Introduction - Check this before starting
Lecture 2 1.2 What is a Modern Data Platform?
Lecture 3 1.3 Introduction to Snowflake
Lecture 4 1.4 Snowflake Architecture
Lecture 5 1.5 Costs
Lecture 6 1.6 Instance Types
Section 2: 2. Understanding the Snowflake Environment
Lecture 7 2.1 Creating a free Account
Lecture 8 2.2 Exploring the Menus
Lecture 9 2.3 Exploring Worksheets
Lecture 10 2.4 Snowflake Clusters: Virtual Warehouses
Lecture 11 2.5 Default Roles
Section 3: 3. Data Ingestion 1: COPY command
Lecture 12 3.0 Snowflake Ingestion Methods
Lecture 13 3.1 Open Data for Ingestion
Lecture 14 3.2 Stage Types
Lecture 15 3.3 Configuring an External Stage
Lecture 16 3.4 Ingesting Data to Snowflake
Lecture 17 3.5 File Formats
Lecture 18 3.6 Using FLATTEN command
Section 4: 4. Integrate AWS and Snowflake
Lecture 19 4.1 Creating the Storage Integration
Lecture 20 4.2 Loading JSON to Snowflake
Lecture 21 4.3 Semi-Structured Data 1
Lecture 22 4.4 Semi-Structured Data 2
Lecture 23 4.5 Code Versioning
Lecture 24 4.6 Semi-Structured Data 3
Lecture 25 4.7 Inserting Data into a Table
Section 5: 5. Data Ingestion 2: SNOWPIPE - Microbatches
Lecture 26 5.1 Ingesting with SNOWPIPE
Lecture 27 5.2 Creating a PIPE
Lecture 28 5.3 Configuring SQS and Testing the PIPE
Lecture 29 5.4 How to check Erros in PIPES
Data Engineers,Data Analysts,Database Administrators,Analytics Engineers,Cloud Engineers,Software Engineers,Database Developers,Python Developers
HOMEPAGE
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