Amazon Web Services (AWS)
Building Modern Data Analytics Solutions on AWS
Four days across the core of AWS analytics: data lakes with Lake Formation, batch analytics on EMR, warehousing on Redshift and real-time streaming, aligned with the Data Engineer Associate certification.
4 daysIntermediate36 modulesAWS Certified Data Engineer - Associate
About this course
Four days across the core of AWS analytics: data lakes with Lake Formation, batch analytics on EMR, warehousing on Redshift and real-time streaming, aligned with the Data Engineer Associate certification.
Curriculum
36 modules
1Building Data Lakes on AWS - Day One
- 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
- 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
- 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
- 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
- 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
- Post course knowledge check
- Architecture review
- Course review
8Building Batch Data Analytics Solutions on AWS - Day Two
- Data analytics use cases
- Using the data pipeline for analytics
- Using Amazon EMR in analytics solutions
- Amazon EMR cluster architecture
- Interactive Demo 1: Launching an Amazon EMR cluster
- Cost management strategies
- Storage optimization with Amazon EMR
- Data ingestion techniques
- 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
- 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
- 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
- Securing EMR clusters
- Interactive Demo 3: Client-side encryption with EMRFS
- Monitoring and troubleshooting Amazon EMR clusters
- Demo: Reviewing Apache Spark cluster history
- Batch data analytics use cases
- Activity: Designing a batch data analytics workflow
- Modern data architectures
18Building Data Analytics Solutions Using Amazon Redshift - Day Three
- Data analytics use cases
- Using the data pipeline for analytics
- Why Amazon Redshift for data warehousing?
- Overview of Amazon Redshift
- Amazon Redshift architecture
- Interactive Demo 1: Touring the Amazon Redshift console
- Amazon Redshift features
- Practice Lab 1: Load and query data in an Amazon Redshift cluster
- 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
- 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
- Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster
- Securing the Amazon Redshift cluster
- Monitoring and troubleshooting Amazon Redshift clusters
- Data warehouse use case review
- Activity: Designing a data warehouse analytics workflow
- Modern data architectures
27Building Streaming Data Analytics Solutions on AWS - Day Four
- Data analytics use cases
- Using the data pipeline for analytics
- The importance of streaming data analytics
- The streaming data analytics pipeline
- Streaming concepts
- 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
- 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
- Optimize Amazon Kinesis to gain actionable business insights
- Security and monitoring best practices
- 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
- 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
- Use case review
- Class Exercise: Designing a streaming data analytics workflow
- Modern data architectures
Who it's for
Data engineers and analytics professionals preparing for the AWS Data Engineer Associate exam.
Before you start
- AWS Technical Essentials
- Architecting on AWS
Prepares for
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