What you'll learn
- Data engineering concepts and AWS services
- Fetching data from external Rest Api
- Fetching data from SFTP server
- Ingesting data into a Database
- Creating Serverless Data Lake using S3 and Athena
- Creating Glue ETL jobs and Workflows
- Transforming data into Parquet format
- Learn how to store data in S3 Data lakes using Parquet columnar file formats
Requirements
- Hands on expertise on Python & Sql is a must
- An active AWS account
Description
A hands on course that covers majority of the typical data engineering / ETL scenarios.
In this course you will learn:
Different services and concepts of AWS data engineering
Creating serverless data lake using S3, Glue and Athena
Ingesting data using Rest Api
Ingesting data using Sftp server
Ingesting data into Database (AWS RDS - Postgre SQL)
Incremental data loading
Prerequisites:
An active AWS account
Python / SQL knowledge
Who this course is for:
- Beginner python developers curious for data science
- Data engineers
- BI/ ETL Developers
- Data Scientists / Analysts
- Anyone from technical background who wants to learn Data engineering in AWS
- Software developers who want to learn different ways to ingest data into AWS and build a datawarehouse
Instructor
- 3.7 Instructor Rating
- 119 Reviews
- 438 Students
- 3 Courses
An experienced data engineer having 5+ years of experience in data engineering on cloud platforms (Azure and AWS) as well as on-prem (SSIS, Talend, Informatica), business intelligence (BI), ETL, analytics, data warehouse, databases (SQL, Oracle, MySQL, PL/SQL) and data visualization (Power BI, Microstrategy, SSRS).
Worked on multiple Programming Languages: X++, Visual Basic, c#, Python, Ruby, R