Building Streaming Data Analytics Solutions on AWS (E)

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.

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Dauer

Trainingssprache

Englisch

Trainingsart

Nicht verfügbar

Was werden Sie in diesem Training erlernen?

1. Understand the features and benefits of a modern data architecture. Learn how AWS streaming services fit into a modern data architecture. 2. Design and implement a streaming data analytics solution 3. Identify and apply appropriate techniques, such as compression, sharding, and partitioning, to optimize data storage 4. Select and deploy appropriate options to ingest, transform, and store real-time and near real-time data 5. Choose the appropriate streams, clusters, topics, scaling approach, and network topology for a particular business use case 6. Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights 7. Secure streaming data at rest and in transit 8. Monitor analytics workloads to identify and remediate problems 9. Apply cost management best practices

Agenda

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

Demonstration: Explore Zeppelin notebooks for Kinesis Data Analytics

Practice Lab: Streaming analytics with Amazon Kinesis Data Analytics and Apache Flink

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

Use case review

Class Exercise: Designing a streaming data analytics workflow

Developing Modern Data Architectures on AWS

Modern data architectures

REVIEWING ARCHITECTING CONCEPTS

SINGLE TO MULTIPLE ACCOUNTS

Group Exercise: Review Architecting on AWS core best practices
AWS Organizations for multi-account access and permissions
AWS SSO to simplify access and authentication across AWS accounts and third-party services
AWS Control Tower
Permissions, access, and authentication

HYBRID CONNECTIVITY

SPECIALIZED INFRASTRUCTURE

Zielgruppe

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

Voraussetzung für den Kurs

We recommend that attendees of this course have: 1. 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. 2. Completed either Architecting on AWS or Data Analytics Fundamentals 3. Completed Building Data Lakes on AWS

Partner

AWS