Building Data Analytics Solutions Using Amazon Redshift
In this course, you will learn how to build an operational data lake that supports the analysis of structured and unstructured data. You will learn the components and features of the services involved in building a data lake. You will use AWS Lake Formation to create a data lake, AWS Glue to create a data catalog, and Amazon Athena to analyze the data. The course lectures and labs further your learning by exploring several common data lake architectures.
COD: AW-BDASAR
Categorie: AWS
Who should participate
This course is aimed at:
- Data platform engineers
- Solution architects
- IT professionals
Prerequisites
Necessary
Familiarity with combining AWS technologies to support data lakes or other data-driven workloads will benefit from this course.
Recommended
- Completion of AWS Technical Essentials or Architecting on AWS
- Completion of Building Data Lakes on AWS
In this course you will learn to:
- Compare the features and benefits of data warehouses, data lakes and modern data architectures.
- Design and implement a data warehouse analytical solution
- Identify and apply appropriate techniques, including compression, to optimize data storage.
- Select and implement the appropriate options for acquiring, transforming, and archiving data.
- Choose the appropriate node and instance types, clusters, autoscaling, and network topology for a particular business use case.
- Understand how data storage and processing affect the analysis and visualization mechanisms needed to obtain actionable business insights.
- Protect data at rest and in transit
- Monitor analytic workloads to identify and resolve issues.
- Apply cost management best practices
Module A: Data Analysis and Data Pipeline Overview
- Data analytics use cases
- Using the data pipeline for analytics
Module 1: Using Amazon Redshift in your data analysis pipeline
- Why Amazon Redshift for Data Warehousing?
- Amazon Redshift overview
Module 2: Introduction to Amazon Redshift
- Amazon Redshift architecture
- Interactive demo 1: visit the Amazon Redshift console
- Features of Amazon Redshift
- Hands-on Lab 1: Setting Up Your Data Warehouse with Amazon Redshift
Module 3: Ingestion and storage
- Ingestion
- Interactive Demo 2: Connect your Amazon Redshift cluster using a Jupyter notebook with
- Data API
- Data distribution and archiving
- Interactive demo 3: Analysis of semi-structured data using the SUPER data type
- Querying data in Amazon Redshift
- Hands-on Lab 2: Analyzing Data with Amazon Redshift Spectrum
Module 4: Data Processing and Optimization
- Data transformation
- Advanced query
- Hands-on Lab 3: Transforming and Querying Data in Amazon Redshift
- Resource management
- Interactive Demo 4: Applying Mixed Workload Management on Amazon Redshift
- Automation and optimization
Module 5: Securing and Monitoring Amazon Redshift Clusters
- Securing your Amazon Redshift cluster
- Monitor and troubleshoot Amazon Redshift clusters
Module 6: Designing Data Warehouse Analytical Solutions
- Analysis of data warehouse use cases
- Activity: Designing a data warehouse analytic workflow
Module B: Developing Modern Data Architectures on AWS
- Modern data architectures
Duration – 1 day
Delivery – in Classroom, On Site, Remote
PC and SW requirements:
- Internet connection
- Web browser, Google Chrome
- Zoom
Language
Instructor: English
Workshop: English
Slides: English
