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Building Data Analytics Solutions Using Amazon Redshift Course Overview

The "Building Data Analytics Solutions Using Amazon Redshift" course equips professionals with essential skills to harness the power of data analytics in cloud environments. Ideal for data analysts, data engineers, and business intelligence professionals, this course emphasizes practical techniques for leveraging Amazon Redshift to derive insights, optimize performance, and drive data-driven decision-making in organizations.


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Course Introduction

Price:

USD 900

Duration:

8 Hrs.

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  • Introduction to Data Analytics and Amazon Redshift
    • Overview of data analytics concepts
    • Introduction to Amazon Redshift architecture
    • Key features and benefits of Amazon Redshift
  • Setting Up Amazon Redshift
    • Creating an Amazon Redshift cluster
    • Configuring cluster settings and security
    • Connecting to Amazon Redshift
  • Data Loading and Transformation
    • Methods for loading data into Amazon Redshift
    • Using Amazon S3 for data storage
    • Data transformation techniques with SQL and Redshift Spectrum
  • Querying Data in Amazon Redshift
    • Writing and optimizing SQL queries
    • Understanding query execution plans
    • Best practices for performance tuning
  • Data Modeling and Schema Design
    • Designing efficient data models for analytics
    • Understanding distribution styles and sort keys
    • Normalization vs. denormalization in Redshift
  • Integrating with Other AWS Services
    • Using AWS Glue for ETL processes
    • Integrating Amazon QuickSight for data visualization
    • Leveraging AWS Lambda for serverless analytics
  • Monitoring and Maintenance
    • Monitoring cluster performance and health
    • Managing workloads with concurrency scaling
    • Implementing backups and snapshots
  • Security and Compliance
    • Data encryption at rest and in transit
    • Managing user access and permissions
    • Compliance considerations for data analytics
  • Case Studies and Best Practices
    • Real-world applications of Amazon Redshift
    • Common pitfalls and how to avoid them
    • Key takeaways for building scalable analytics solutions

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