What you’ll learn
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Data Engineering leveraging Services under AWS Data Analytics
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AWS Essentials such as s3, IAM, EC2, etc
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Understanding AWS s3 for cloud based storage
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Understanding details related to virtual machines on AWS known as EC2
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Managing AWS IAM users, groups, roles and policies for RBAC (Role Based Access Control)
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Managing Tables using AWS Glue Catalog
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Engineering Batch Data Pipelines using AWS Glue Jobs
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Orchestrating Batch Data Pipelines using AWS Glue Workflows
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Running Queries using AWS Athena – Server less query engine service
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Using AWS Elastic Map Reduce (EMR) Clusters for building Data Pipelines
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Using AWS Elastic Map Reduce (EMR) Clusters for reports and dashboards
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Data Ingestion using AWS Lambda Functions
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Scheduling using AWS Events Bridge
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Engineering Streaming Pipelines using AWS Kinesis
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Streaming Web Server logs using AWS Kinesis Firehose
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Overview of data processing using AWS Athena
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Running AWS Athena queries or commands using CLI
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Running AWS Athena queries using Python boto3
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Creating AWS Redshift Cluster, Create tables and perform CRUD Operations
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Copy data from s3 to AWS Redshift Tables
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Understanding Distribution Styles and creating tables using Distkeys
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Running queries on external RDBMS Tables using AWS Redshift Federated Queries
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Running queries on Glue or Athena Catalog tables using AWS Redshift Spectrum
Who this course is for:
- Beginner or Intermediate Data Engineers who want to learn AWS Analytics Services for Data Engineering
- Intermediate Application Engineers who want to explore Data Engineering using AWS Analytics Services
- Data and Analytics Engineers who want to learn Data Engineering using AWS Analytics Services
- Testers who want to learn key skills to test Data Engineering applications built using AWS Analytics Services
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