Amazon Redshift – Introduction Training
Level
BeginnerDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedAmazon Redshift – Introduction Training
The “Redshift – Modern Data Analytics in the 21st Century” training is designed for individuals who wish to learn Amazon Redshift, a managed data warehouse used by leading companies. The course covers deployment, cost optimization, security, and AI-driven analytics. It is suitable for both those with experience in AWS and beginners. The training is workshop-based, with lab exercises to facilitate learning.
What You Will Learn
- How Redshift operates and its architecture.
- The evolution of Redshift and its functionalities.
- The concept of decoupling storage and compute resources, exemplified by RA3.
- Scalability features of Redshift, including automatic adjustment of compute resources.
- Performance optimization techniques and data distribution strategies.
- Best practices for data security and access management.
- Creating and managing backups.
- Loading data into Redshift clusters and integrating with S3.
- Consuming data using Query Editor and JDBC/ODBC protocols.
- Monitoring and auditing Redshift environments.
- Building AI models using Redshift ML without prior ML tool knowledge.
- Cost optimization strategies for Redshift usage.
Who is this training for?
Individuals aiming to enhance analytical capabilities within their organization and derive insights from vast amounts of data collected daily.-
Those interested in mastering the world’s most popular data warehouse.
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Anyone eager to understand how Redshift operates and how to design modern Data Lake solutions securely and efficiently.
Training Program
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How Redshift Works
- Understanding Redshift cluster architecture
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Redshift Evolution
- Navigating the array of Redshift functionalities
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Decoupling in Action
- Why separating storage and compute has become the standard
- RA3 nodes as an example of modern data warehouse architecture
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Redshift Scalability
- Automatic adjustment of compute resources to workload needs
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Redshift Optimization
- Performance-related factors
- Data distribution and its impact on query execution
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Redshift in Production
- Ensuring data security and access management
- Creating and managing backups
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Loading Data into Redshift
- Best practices for data loading
- Integration with Amazon S3
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Data Consumption from Redshift
- Using the visual Query Editor
- Accessing data via JDBC and ODBC protocols
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Monitoring and Auditing Redshift
- Observability and auditing of the data warehouse
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Redshift ML
- Building machine learning models directly from Redshift data
- Using ML capabilities without deep ML tool knowledge
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Cost Optimization
- Controlling and optimizing Redshift-related costs
