AWS-DA
Building Modern Data Analytics Solutions on AWS Training
The Building Modern Data Analytics Solutions on AWS collection of one-day, intermediate level instructor-led courses dives deep into Amazon Lake Formation, Amazon Glue, Amazon EMR, Amazon Kinesis, and Amazon Redshift and the current thinking in building and operating data analytics pipelines to turn data into insights.
Course Details
Duration
4 days
Target Audience
- Data warehouse engineers
- Data platform engineers
- Solutions architects
Skills Gained
In the four-course Building Modern Data Analytics Solutions on AWS collection, you'll receive comprehensive training for developing modern data skills, including:
- How to leverage AWS data Services to store, process, analyze, stream, and query data to make decisions with speed and agility at scale
- How to modernize data solutions end to end
- Skills to put your data to work to make better, more informed decisions, respond faster to the unexpected, and uncover new opportunities
Course Outline
- Building Data Lakes on AWS (1 day)
- 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
- 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
- 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
- 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
- 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
- Architecture and course review
- Introduction to data lakes
- Building Batch Data Analytics Solutions on AWS (1 day)
- Overview of Data Analytics and the Data Pipeline
- Data analytics use cases
- Using the data pipeline for analytics
- Using Streaming Services in the Data Analytics Pipeline
- The importance of streaming data analytics
- The streaming data analytics pipeline
- Streaming concepts
- Introduction to AWS Streaming Services
- Streaming data services in AWS
- Amazon Kinesis in analytics solutions
- Using Amazon Kinesis Data Analytics
- Introduction to Amazon MSK
- Overview of Spark Streaming
- Using Amazon Kinesis for Real-time Data Analytics
- Exploring Amazon Kinesis using a clickstream workload
- Creating Kinesis data and delivery streams
- Building stream producers
- Building stream consumers
- Building and deploying Flink applications in Kinesis Data Analytics
- Securing, Monitoring, and Optimizing Amazon Kinesis
- Optimize Amazon Kinesis to gain actionable business insights
- Security and monitoring best practices
- Using Amazon MSK in Streaming Data Analytics Solutions
- Use cases for Amazon MSK
- Creating MSK clusters
- Ingesting data into Amazon MSK
- Practice Lab: Introduction to access control with Amazon MSK
- Transforming and processing in Amazon MSK
- Securing, Monitoring, and Optimizing Amazon MSK
- Optimizing Amazon MSK
- Security and monitoring
- Designing Streaming Data Analytics Solutions
- Overview of Data Analytics and the Data Pipeline
- Building Streaming Data Analytics Solutions on AWS (1 day)
- Overview of Data Analytics and the Data Pipeline
- Data analytics use cases
- Using the data pipeline for analytics
- Using Streaming Services in the Data Analytics Pipeline
- The importance of streaming data analytics
- The streaming data analytics pipeline
- Streaming concepts
- 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
- 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
- Securing, Monitoring, and Optimizing Amazon Kinesis
- Optimize Amazon Kinesis to gain actionable business insights
- Security and monitoring best practices
- 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
- 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
- Designing Streaming Data Analytics Solutions
- Developing Modern Data Architectures on AWS
- Overview of Data Analytics and the Data Pipeline
- Building Data Analytics Solutions Using Amazon Redshift (1 day)
- Overview of Data Analytics and the Data Pipeline
- Data analytics use cases
- Using the data pipeline for analytics
- Using Amazon Redshift in the Data Analytics Pipeline
- Why Amazon Redshift for data warehousing?
- Overview of Amazon Redshift
- Introduction to Amazon Redshift
- Amazon Redshift architecture
- Interactive Demo 1: Touring the Amazon Redshift console
- Amazon Redshift features
- Ingestion and Storage
- Ingestion
- Data distribution and storage
- Querying data in Amazon Redshift
- Processing and Optimizing Data
- Data transformation
- Advanced querying
- Resource management
- Automation and optimization
- Security and Monitoring of Amazon Redshift Clusters
- Securing the Amazon Redshift cluster
- Monitoring and troubleshooting Amazon Redshift clusters
- Designing Data Warehouse Analytics Solutions
- Developing Modern Data Architectures on AWS
- Overview of Data Analytics and the Data Pipeline
