BSDASAAWAmazon Web ServicesAW-BSDASA1.0<p>In this course, you will learn to: </p> <ul> <li>Understand the features and benefits of a modern data architecture. Learn how AWS streaming services fit into a modern data architecture.</li><li>Design and implement a streaming data analytics solution</li><li>Identify and apply appropriate techniques, such as compression, sharding, and partitioning, to optimize data storage</li><li>Select and deploy appropriate options to ingest, transform, and store real-time and near real-time data</li><li>Choose the appropriate streams, clusters, topics, scaling approach, and network topology for a particular business use case</li><li>Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights</li><li>Secure streaming data at rest and in transit</li><li>Monitor analytics workloads to identify and remediate problems</li><li>Apply cost management best practices</li></ul><p>We recommend that attendees of this course have:</p> <ul> <li>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.</li><li>Completed either <span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/en/course/amazon-awsa"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Architecting on AWS <span class="fl-prod-pcode">(AWSA)</span></a></span> or Data Analytics Fundamentals</li><li>Completed <span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/en/course/amazon-bdla"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Building Data Lakes on AWS <span class="fl-prod-pcode">(BDLA)</span></a></span></li></ul><p>This course is intended for: </p> <ul> <li>Data engineers and architects</li><li>Developers who want to build and manage real-time applications and streaming data analytics solutions</li></ul><h4>Module A: Overview of Data Analytics and the Data Pipeline</h4><ul> <li>Data analytics use cases</li><li>Using the data pipeline for analytics</li></ul><h4>Module 1: Using Streaming Services in the Data Analytics Pipeline</h4><ul> <li>The importance of streaming data analytics</li><li>The streaming data analytics pipeline</li><li>Streaming concepts</li></ul><h4>Module 2: Introduction to AWS Streaming Services</h4><ul> <li>Streaming data services in AWS</li><li>Amazon Kinesis in analytics solutions</li><li>Demonstration: Explore Amazon Kinesis Data Streams</li><li>Practice Lab: Setting up a streaming delivery pipeline with Amazon Kinesis</li><li>Using Amazon Kinesis Data Analytics</li><li>Introduction to Amazon MSK</li><li>Overview of Spark Streaming</li></ul><h4>Module 3: Using Amazon Kinesis for Real-time Data Analytics</h4><ul> <li>Exploring Amazon Kinesis using a clickstream workload</li><li>Creating Kinesis data and delivery streams</li><li>Demonstration: Understanding producers and consumers</li><li>Building stream producers</li><li>Building stream consumers</li><li>Building and deploying Flink applications in Kinesis Data Analytics</li><li>Demonstration: Explore Zeppelin notebooks for Kinesis Data Analytics</li><li>Practice Lab: Streaming analytics with Amazon Kinesis Data Analytics and Apache Flink</li></ul><h4>Module 4: Securing, Monitoring, and Optimizing Amazon Kinesis</h4><ul> <li>Optimize Amazon Kinesis to gain actionable business insights</li><li>Security and monitoring best practices</li></ul><h4>Module 5: Using Amazon MSK in Streaming Data Analytics Solutions</h4><ul> <li>Use cases for Amazon MSK</li><li>Creating MSK clusters</li><li>Demonstration: Provisioning an MSK Cluster</li><li>Ingesting data into Amazon MSK</li><li>Practice Lab: Introduction to access control with Amazon MSK</li><li>Transforming and processing in Amazon MSK</li></ul><h4>Module 6: Securing, Monitoring, and Optimizing Amazon MSK</h4><ul> <li>Optimizing Amazon MSK</li><li>Demonstration: Scaling up Amazon MSK storage</li><li>Practice Lab: Amazon MSK streaming pipeline and application deployment</li><li>Security and monitoring</li><li>Demonstration: Monitoring an MSK cluster</li></ul><h4>Module 7: Designing Streaming Data Analytics Solutions</h4><ul> <li>Use case review</li><li>Class Exercise: Designing a streaming data analytics workflow</li></ul><h4>Module B: Developing Modern Data Architectures on AWS</h4><ul> <li>Modern data architectures</li></ul>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 practicesWe 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 (AWSA) or Data Analytics Fundamentals - Completed Building Data Lakes on AWS (BDLA)This course is intended for: - Data engineers and architects - Developers who want to build and manage real-time applications and streaming data analytics solutionsModule 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 architectures1 day590.00750.00750.00750.00900.00870.00750.00675.00930.00750.00