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Machine Learning Engineer
The course delves into Amazon Kinesis and Amazon MSK through a mix of instructor-led presentations, hands-on labs, demonstrations, and class exercises so that by the end of the course you will know how to build a streaming data analytics solution on AWS. You'll also learn how to scale streaming applications with Amazon Kinesis, optimize data storage, select and deploy appropriate options for ingesting, transforming, storing, and analyzing data, and more.
Module A: Overview of data analysis and the data pipeline
- Use cases of data analysis
- Use of the data pipeline for data analysis
Module 1: Use of streaming services in the data analysis pipeline
- The importance of streaming data analytics
- The streaming data analysis pipeline
- Streaming concepts
Module 2: Introduction to AWS streaming services
- Streaming data services in AWS
- Amazon Kinesis in analytics solutions
- Demo: Exploring Amazon Kinesis Data Streams
- Hands-on lab: Setting up a streaming delivery pipeline with Amazon Kinesis
- Use of Amazon Kinesis Data Analytics
- Introduction to Amazon MSK
- Overview of Spark Streaming
Module 3: Using Amazon Kinesis for real-time data analysis
- Exploring Amazon Kinesis using a clickstream workload
- Creation of Kinesis data and supply streams
- Demo: Understanding producers and consumers
- Creating stream producers
- Creating stream consumers
- Create and deploy Flink applications in Kinesis Data Analytics
- Demo: Exploring Zeppelin notebooks for Kinesis Data Analytics
- Practice Lab: Streaming analyses with Amazon Kinesis Data
- Analytics and Apache Flink
Module 4: Securing, monitoring and optimizing Amazon Kinesis
- Optimize Amazon Kinesis to gain actionable business insights
- Proven procedures for security and monitoring
Module 5: Using Amazon MSK in streaming data analytics solutions
- Use cases for Amazon MSK
- Creating MSK clusters
- Demo: Deploying an MSK cluster
- Feeding data into Amazon MSK
- Practice Lab: Introduction to access control with Amazon MSK
- Transforming and processing in Amazon MSK
Module 6: Backing up, monitoring and optimizing Amazon MSK
- Optimization of Amazon MSK
- Demo: Scaling Amazon MSK storage
- Practice Lab: Amazon MSK streaming pipeline and application delivery
- Security and monitoring
- Demo: Monitoring an MSK cluster
Module 7: Designing streaming data analytics solutions
- Review of use cases
- Class exercise: Designing a workflow for streaming data analysis
Module B: Developing modern data architectures on AWS
- Modern data architectures
- Understand the features and benefits of a modern data architecture. Learn how AWS streaming services fit into a modern data architecture.
- Designing and implementing a streaming data analytics solution
- Identify and apply appropriate techniques, such as compression, sharding and partitioning, to optimize data storage
- Selection and use of suitable options for recording, converting and storing real-time and near-real-time data
- Selection of suitable streams, clusters, themes, scaling approaches and network topologies for a specific business use case
- Understand how data storage and processing impact the analytics and visualization mechanisms required to gain actionable business insights
- Backup of streaming data at rest and during transmission
- Monitoring of analysis workloads to detect and resolve problems
- Application of best practices for cost management
This course consists of training training and is led by a trainer who supervises the participants live. Theory and practice are taught with live demonstrations and practical exercises. The video conferencing software Zoom is used.
This course is aimed at the following job roles:
- Data analytics
We recommend that participants this course have the following prerequisites:
- At least one year of experience in managing data analytics solutions or data streams
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