Data Engineering on AWS: Data Cataloging, Processing, Analytics, and Visualization
2h 18mIntermediate2023-04-20
Authors

Dipali Kulshrestha
AWS-Certified Software Programmer and Cloud Architect
Course details
Businesses need data experts—now more than ever before. As data-driven decision-making has risen to boardroom prominence, the role of the data expert has become essential to understanding and scaling a business. In this second course of a two-part series, instructor Dipali Kulshrestha focuses on data catalog, processing, analytics, and visualization. Dipali provides useful hands-on exercises—along with some challenges and solutions— to help you master these AWS engineering skills, so join her in this course to level up your skills and give your data engineering career a boost.
Skills covered
Data EngineeringAmazon Web Services (AWS)AmazonCloud ServicesCloud PlatformsCloud ComputingData ScienceOne-Off
Concepts
0. Introduction
- 01 - Data engineering with AWS
1. Data Engineering
- 02 - Data engineering pipeline overview
2. Data Catalog
- 03 - Glue overview
- 04 - Glue crawler
- 05 - Glue jobs
- 06 - AWS Glue DataBrew
- 07 - AWS Glue Elastic Views
- 08 - Challenge - AWS Glue
- 09 - Solution - AWS Glue
3. Processing
- 10 - Serverless processing with Lambda - Demo
- 11 - AWS Lake Formation
- 12 - Elastic MapReduce and Hadoop overview
- 13 - EMR in action - Lab
4. Analytics
- 14 - Kinesis Analytics overview
- 15 - Kinesis Analytics demo
- 16 - Amazon Elasticsearch
- 17 - Elasticsearch demo
- 18 - Amazon Athena overview
- 19 - Amazon Redshift introduction and architecture
- 20 - Amazon Redshift Spectrum and performance tuning
- 21 - Demo - Amazon Redshift
- 22 - Challenge - Read catalog data using Athena
- 23 - Solution - Read catalog data using Athena
5. Visualization
- 24 - Visualization introduction
- 25 - Amazon QuickSight overview
- 26 - Visualization types with QuickSight
- 27 - Amazon QuickSight demo
Conclusion
- 28 - Next steps