AWS Data Analytics Course Collection Bundle (DACCB)

Day 1:

Module 1: Introduction to data lakes

• Describe the value of data lakes
• Compare data lakes and data warehouses
• Describe the components of a data lake
• Recognize common architectures built on data lakes

Module 2: Data ingestion, cataloging, and preparation

• Describe the relationship between data lake storage and data ingestion
• Describe AWS Glue crawlers and how they are used to create a data catalog
• Identify data formatting, partitioning, and compression for efficient storage and query
• Lab 1: Set up a simple data lake

Module 3: Data processing and analytics

• Recognize how data processing applies to a data lake
• Use AWS Glue to process data within a data lake
• Describe how to use Amazon Athena to analyze data in a data lake

Module 4: Building a data lake with AWS Lake Formation

• Describe the features and benefits of AWS Lake Formation
• Use AWS Lake Formation to create a data lake
• Understand the AWS Lake Formation security model
• Lab 2: Build a data lake using AWS Lake Formation

Module 5: Additional Lake Formation configurations

• Automate AWS Lake Formation using blueprints and workflows
• Apply security and access controls to AWS Lake Formation
• Match records with AWS Lake Formation FindMatches
• Visualize data with Amazon QuickSight
• Lab 3: Automate data lake creation using AWS Lake Formation blueprints
• Lab 4: Data visualization using Amazon QuickSight

Day 2:

Module A: Overview of Data Analytics and the Data Pipeline

• Data analytics use cases
• Using the data pipeline for analytics

Module 1: Introduction to Amazon EMR

• Using Amazon EMR in analytics solutions
• Amazon EMR cluster architecture
• Interactive Demo 1: Launching an Amazon EMR cluster
• Cost management strategies

Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage

• Storage optimization with Amazon EMR
• Data ingestion techniques

Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR

• Apache Spark on Amazon EMR use cases
• Why Apache Spark on Amazon EMR
• Spark concepts
• Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell
• Transformation, processing, and analytics
• Using notebooks with Amazon EMR
• Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR

Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive

• Using Amazon EMR with Hive to process batch data
• Transformation, processing, and analytics
• Practice Lab 2: Batch data processing using Amazon EMR with Hive
• Introduction to Apache HBase on Amazon EMR

Module 5: Serverless Data Processing

• Serverless data processing, transformation, and analytics
• Using AWS Glue with Amazon EMR workloads
• Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions

Module 6: Security and Monitoring of Amazon EMR Clusters

• Securing EMR clusters
• Interactive Demo 3: Client-side encryption with EMRFS
• Monitoring and troubleshooting Amazon EMR clusters
• Demo: Reviewing Apache Spark cluster history

Module 7: Designing Batch Data Analytics Solutions

• Batch data analytics use cases
• Activity: Designing a batch data analytics workflow
• Module B: Developing Modern Data Architectures on AWS
• Modern data architectures

Day 3:

Module A: Overview of Data Analytics and the Data Pipeline

• Data analytics use cases
• Using the data pipeline for analytics

Module 1: Using Amazon Redshift in the Data Analytics Pipeline

• Why Amazon Redshift for data warehousing?
• Overview of Amazon Redshift

Module 2: Introduction to Amazon Redshift

• Amazon Redshift architecture
• Interactive Demo 1: Touring the Amazon Redshift console
• Amazon Redshift features
• Practice Lab 1: Setting up your data warehouse using Amazon Redshift

Module 3: Ingestion and Storage

• Ingestion
• Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
• Data distribution and storage
• Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
• Querying data in Amazon Redshift
• Practice Lab 2: Data analytics using Amazon Redshift Spectrum

Module 4: Processing and Optimizing Data

• Data transformation
• Advanced querying
• Practice Lab 3: Data transformation and querying in Amazon Redshift
• Resource management
• Interactive Demo 4: Applying mixed workload management on Amazon Redshift
• Automation and optimization

Module 5: Security and Monitoring of Amazon Redshift Clusters

• Securing the Amazon Redshift cluster
• Monitoring and troubleshooting Amazon Redshift clusters

Module 6: Designing Data Warehouse Analytics Solutions

• Data warehouse use case review
• Activity: Designing a data warehouse analytics workflow

Module B: Developing Modern Data Architectures on AWS

• Modern data architectures

Day 4:

Module A: Overview of Data Analytics and the Data Pipeline

• Data analytics use cases
• Using the data pipeline for analytics

Module 1: Using Streaming Services in the Data Analytics Pipeline

• The importance of streaming data analytics
• The streaming data analytics pipeline
• Streaming concepts

Module 2: Introduction to AWS Streaming Services

• Streaming data services in AWS
• Amazon Kinesis in analytics solutions
• Demonstration: Explore Amazon Kinesis Data Streams
• Practice Lab: Setting up a streaming delivery pipeline with Amazon Kinesis
• Using Amazon Kinesis Data Analytics
• Introduction to Amazon MSK
• Overview of Spark Streaming

Module 3: Using Amazon Kinesis for Real-time Data Analytics

• Exploring Amazon Kinesis using a clickstream workload
• Creating Kinesis data and delivery streams
• Demonstration: Understanding producers and consumers
• Building stream producers
• Building stream consumers
• Building and deploying Flink applications in Kinesis Data Analytics
• Demonstration: Explore Zeppelin notebooks for Kinesis Data Analytics
• Practice Lab: Streaming analytics with Amazon Kinesis Data Analytics and Apache Flink

Module 4: Securing, Monitoring, and Optimizing Amazon Kinesis

• Optimize Amazon Kinesis to gain actionable business insights
• Security and monitoring best practices

Module 5: Using Amazon MSK in Streaming Data Analytics Solutions

• Use cases for Amazon MSK
• Creating MSK clusters
• Demonstration: Provisioning an MSK Cluster
• Ingesting data into Amazon MSK
• Practice Lab: Introduction to access control with Amazon MSK
• Transforming and processing in Amazon MSK

Module 6: Securing, Monitoring, and Optimizing Amazon MSK

• Optimizing Amazon MSK
• Demonstration: Scaling up Amazon MSK storage
• Practice Lab: Amazon MSK streaming pipeline and application deployment
• Security and monitoring
• Demonstration: Monitoring an MSK cluster

Module 7: Designing Streaming Data Analytics Solutions

• Use case review
• Class Exercise: Designing a streaming data analytics workflow

Module B: Developing Modern Data Architectures on AWS

• Modern data architectures