![Data Engineering Master Class using AWS Analytics Services]()
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 10.0 GB | Duration: 26h 15m
What you'll learn
Data Eeering leveraging AWS Analytics features
AWS Essentials such as s3, IAM, EC2, etc
Understanding AWS s3 for cloud based storage
Understanding details related to virtual machines on AWS known as EC2
Managing AWS IAM users, groups, roles and policies for RBAC (Role Based Access Control)
Managing Tables using AWS Glue Catalog
Eeering Batch Data Pipelines using AWS Glue Jobs
Orchestrating Batch Data Pipelines using AWS Glue Workflows
Running Queries using AWS Athena - Server less query ee service
Using AWS Elastic Map Reduce (EMR) Clusters for building Data Pipelines
Using AWS Elastic Map Reduce (EMR) Clusters for reports and dashboards
Data Ingestion using AWS Lambda Functions
Scheduling using AWS Events Bridge
Eeering Streaming Pipelines using AWS Kinesis
Streaming Web Server logs using AWS Kinesis Firehose
Overview of data processing using AWS Athena
Running AWS Athena queries or commands using CLI
Running AWS Athena queries using Python boto3
Creating AWS Redshift Cluster, Create tables and perform CRUD Operations
Copy data from s3 to AWS Redshift Tables
Understanding Distribution Styles and creating tables using Distkeys
Running queries on external RDBMS Tables using AWS Redshift Federated Queries
Running queries on Glue or Athena Catalog tables using AWS Redshift Spectrum
Requirements
Programming experience using Python
Data Eeering experience using Spark
Ability to write and interpret SQL Queries
This course is ideal for experienced data eeers to add AWS Analytics Services as key skills to their profile
Description
Data Eeering is all about building Data Pipelines to get data from multiple sources into Data Lake or Data Warehouse and then from Data Lake or Data Warehouse to downstream systems.