AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL.
Course content
Overview of cloud and AWS
- Overview of AWS web services and Free tier Account
- Creating an AWS Account
- Exploring Web Console
- Overview of AWS CLI tool, SDKs and APIs
- Overview of EC2 instance
Data Storage on AWS
- Overview of AWS Storage Service
- Overview of S3, Glacier
- Creating S3 Bucket
- Properties of S3 bucket
- Working with RDS databases
- Overview of No-SQL database (DynamoDB)
Introduction To Glue
- Glue Basics
- Features of Glue
- Glue Components
- Securing with IAM
- Setting up environment
- Pointing to specific data stores and endpoints
Glue Data Catalogue
- What and why of Data Catalogue
- Crawlers
- Connecting to your data store
- Using Crawlers for Catalogue tables.
Working with Glue Jobs
- Overview and working of Glue Jobs
- Adding new jobs in Glue
- Editing scripts in Glue
- Triggering Jobs and their scheduling
Administering Glue
- Scheduling Jobs
- Scheduling Crawlers
- Logging and monitoring Glue
ETL scripts and Glue APIs
- ETL scripts in Python
- Various Glue APIs
- Common Data Types and Exceptions
- Use-cases and Benefits
Troubleshooting on Glue
- Where to look?
- Connection issues
- Most common issues
- Best Practices for Glue
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Course Prerequisites
The following prerequisites will be both useful and helpful for this course:
- A background in statistics or probability
- Basic understanding of data analytics
- General development and coding experience
- AWS VPC networking and IAM security experience (for the demonstrations)
Who can attend
The intended audience for this course includes:
- Data scientists and/or data analysts
- Anyone interested in learning and performing distributed machine learning, or machine learning at scale
- Anyone with an interest in Apache Spark and/or Amazon Elastic MapReduce
Number of Hours: 30hrs
Certification
NoneKey features
- One to One Training
- Online Training
- Fastrack & Normal Track
- Resume Modification
- Mock Interviews
- Video Tutorials
- Materials
- Real Time Projects
- Virtual Live Experience
- Preparing for Certification
FAQs
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- Preparing for Certification
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