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Machine Learning Engineer
The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift into a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to Amazon Redshift operations.
Module A: Overview of Data Analytics and the Data Pipeline
- Use cases of data analysis
- Use of the data pipeline for data analysis
Module 1: Using Amazon Redshift in the data analysis pipeline
- Why Amazon Redshift for data warehousing?
- Overview of Amazon Redshift
Module 2: Introduction to Amazon Redshift
- Architecture of Amazon Redshift
- Interactive demo 1: Tour of the Amazon Redshift console
- Amazon Redshift functions
- Exercise 1: Loading and querying data in an Amazon Redshift cluster
Module 3: Ingestion and storage
- Data transfer
- Interactive demo 2: Connecting your Amazon Redshift cluster to a Jupyter notebook with data API
- Data distribution and storage
- Interactive demo 3: Analyzing semi-structured data with the SUPER data type
- Querying data in Amazon Redshift
- Exercise 2: Data analysis with Amazon Redshift Spectrum
Module 4: Processing and optimizing data
- Data transformation
- Advanced queries
- Practical exercise 3: Data transformation and retrieval in Amazon Redshift
- Management of resources
- Interactive demo 4: Applying mixed workload management on Amazon Redshift
- Automation and optimization
- Interactive demo 5: Resizing the Amazon Redshift cluster from dc2.large to ra3.xlplus cluster
Module 5: Security and monitoring of Amazon Redshift clusters
- Securing the Amazon Redshift cluster
- Monitoring and troubleshooting Amazon Redshift clusters
Module 6: Designing data warehouse analytics solutions
- Review of data warehouse use cases
- Activity: Designing a data warehouse analysis workflow
Module B: Developing modern data architectures on AWS
- Modern data architectures
- Comparison of the functions and advantages of data warehouses, data lakes and modern data architectures
- Designing and implementing a data warehouse analysis solution
- Identify and apply appropriate techniques, including compression, to optimize data storage
- Selecting and providing suitable options for recording, converting and storing data
- Selection of suitable instance and node types, clusters, auto-scaling and network topology for a specific business use case
- Understand how data storage and processing impacts the analytics and visualization mechanisms required to gain actionable business insights
- Backup of data at rest and during transfer
- Monitoring of analytics workloads to identify and resolve issues
- Application of best practices for cost management
This course includes presentations, interactive demos, hands-on exercises, discussions and class exercises.
This course is aimed at the following job roles:
- Data analytics
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