Building Streaming Data Analytics Solutions on AWS

In this course, you will learn to:

• Understand the features and benefits of a modern data architecture. Learn how AWS streaming services fit into a modern data architecture.
• Design and implement a streaming data analytics solution
• Identify and apply appropriate techniques, such as compression, sharding, and partitioning, to optimize data storage
• Select and deploy appropriate options to ingest, transform, and store real-time and near real-time data
• Choose the appropriate streams, clusters, topics, scaling approach, and network topology for a particular business use case
• Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
• Secure streaming data at rest and in transit
• Monitor analytics workloads to identify and remediate problems
• Apply cost management best practices

This course is intended for:

• Data engineers and architects
• Developers who want to build and manage real-time applications and streaming data analytics solutions

We recommend that attendees of this course have:
• At least one year of data analytics experience or direct experience building real-time applications or streaming analytics solutions. We suggest the Streaming Data Solutions on AWS whitepaper for those that need a refresher on streaming concepts.
• Completed either Architecting on AWS or Data Analytics Fundamentals
• Completed Building Data Lakes on AWS

Course outline 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