TrainingData & analytics / AWS Data AnalyticsAWS-BDASR
AWS-BDASRAWS · authorised training
Building Data Analytics Solutions Using Amazon Redshift
In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with 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 the operation of Amazon Redshift.
- Duration
- 1 day
- Level
- Intermediate
- Provider
- AWS
- Topic
- Data & analytics / AWS Data Analytics
Course outline
- Module A: Overview of data analytics and the data pipeline – use cases, using the data pipeline for analytics
- Module 1: Using Amazon Redshift in the data analytics pipeline – why Amazon Redshift, service overview
- Module 2: Introduction to Amazon Redshift – architecture, console, features; lab: loading and querying data
- Module 3: Ingestion and storage – Data API, data distribution, SUPER data type, Amazon Redshift Spectrum
- Module 4: Processing and optimising data – transformation, advanced querying, workload management, cluster resizing
- Module 5: Security and monitoring of Amazon Redshift clusters – securing, monitoring, troubleshooting
- Module 6: Designing data warehouse analytics solutions – use case review, workflow design activity
- Module B: Developing modern data architectures on AWS
Skills you will gain
- Compare the features and benefits of data warehouses, data lakes, and modern data architectures
- Design and implement a data warehouse analytics solution
- Identify and apply appropriate techniques, including compression, to optimize data storage
- Select and deploy appropriate options to ingest, transform, and store data
- Choose the appropriate instance and node types, clusters, auto scaling, 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 data at rest and in transit
- Monitor analytics workloads to identify and remediate problems
- Apply cost management best practices
Who should attend
- Data warehouse engineers
- Data platform engineers
- Architects who build and manage data analytics pipelines
- Operators who build and manage data analytics pipelines
Prerequisites
- At least one year of experience managing data warehouses
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed Building Data Lakes on AWS
2300 PLN
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