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This course is part of a collection called 'Building Modern Data Analytics Solutions on AWS', which consists of 4 courses. You have the option to book any of the individual courses separately. If you prefer to attend all 4 courses, you can book the combined course called Building Modern Data Analytics Solutions on AWS.

Overview

In this course, you will learn to build streaming data analytics solutions using AWS services, including Amazon Kinesis and Amazon Managed Streaming for Apache Kafka (Amazon MSK). Amazon Kinesis is a massively scalable and durable real-time data streaming service. Amazon MSK offers a secure, fully

managed, and highly available Apache Kafka service. You will learn how Amazon Kinesis and Amazon MSK integrate with AWS services such as AWS Glue and AWS Lambda. The course addresses the streaming data ingestion, stream storage, and stream processing components of the data analytics pipeline. You will also learn to apply security, performance, and cost management best practices to the operation of Kinesis and Amazon MSK.

This course is part of the Building Modern Data Analytics Solutions on AWS collection of four, one-day, intermediate-level classroom training courses.

This course is intended for:

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

  • Students with a minimum one-year experience managing data analytics solutions or streaming data will benefit from this course
  • We suggest the Streaming Data Solutions on AWS whitepaper for those that need a refresher on streaming concepts

We recommend that attendees of this course have:

  • Completed either Architecting on AWS or Data Analytics Fundamentals
  • Completed Building Data Lakes on AWS
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Delegates will learn how 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
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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

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Special Notices

Labs - Please note: The labs for your AWS course will be delivered through AWS Builder labs. In order to access these labs you will need to have an Amazon BuilderID. You can set up your new Amazon account here. Please ensure that you have set up this Amazon BuilderID in advance of attending your class.

Courseware – Please note: In order to access your digital course materials you are required to set up a Gilmore account in advance of attending your course. To do this please follow this link.

Please also be aware that in order to access your materials and Labs it is important that your device and network should not restrict access to AWS or Vitalsource content. For that reason, AWS recommend NOT using a Corporate laptop with any security restrictions in place or the use of a VPN.

Dates & Locations

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