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AWS Data Engineering/Data Analytics

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Data Warehousing on AWS

Quick Overview

  • Glue Deep Dive
  • AWS Step Functions
  • Lambda
  • Securing your Amazon deployment
  • Managing big data costs
  • Visualizing and orchestrating big data
  • Introduction to data in motion and streaming - Kinesis

Module 1: Introduction to Data Warehousing

  • Relational databases and data warehousing concepts
  • The intersection of data warehousing and big data
  • Overview of data management in AWS
  • Hands-on Lab 1: Introduction to Amazon Redshift

Module 2: Introduction to Amazon Redshift

  • Conceptual overview and real-world use cases
  • Hands-on Lab 2: Launching an Amazon Redshift cluster

Module 3: Launching Clusters

  • Building the cluster, connecting to it, and controlling access
  • Database security and loading data
  • Hands-on Lab 3: Optimizing database schemas

Module 4: Designing the Database Schema

  • Schemas, data types, columnar compression, data distribution styles, and data sorting methods

Module 5: Identifying Data Sources

  • Overview of data sources:
  • Amazon S3, Amazon DynamoDB, Amazon EMR, Amazon Kinesis Data Firehose, AWS Lambda Database Loader for Amazon Redshift
  • Hands-on Lab 4: Loading real-time data into an Amazon Redshift database

Module 6: Loading Data

  • Preparing and loading data using COPY
  • Maintaining tables and concurrent write operations
  • Troubleshooting load issues
  • Hands-on Lab 5: Loading data with the COPY command

Module 7: Writing Queries and Tuning for Performance

  • Amazon Redshift SQL, User-Defined Functions (UDFs)
  • Factors that affect query performance
  • The EXPLAIN command and query plans
  • Workload Management (WLM)
  • Hands-on Lab 6: Configuring workload management

Module 8: Amazon Redshift Spectrum

  • Configuring data for Amazon Redshift Spectrum
  • Amazon Redshift Spectrum queries
  • Hands-on Lab 7: Using Amazon Redshift Spectrum

Module 9: Maintaining Clusters

  • Audit logging, performance monitoring, events, and notifications
  • Resizing clusters, backing up and restoring clusters
  • Resource tagging and limits and constraints
  • Hands-on Lab 8: Auditing and monitoring clusters
  • Hands-on Lab 9: Backing up, restoring, and resizing clusters

Module 10: Analyzing and Visualizing Data

  • Power of visualizations and building dashboards
  • Amazon QuickSight editions and features

T. Sanjay

Tech Enthusiast | Seasoned Corporate EnterT(r)ainer

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