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Data Warehousing on AWS Course

  • Duration: 24 Hours
  • Exam Voucher: Yes
  • Language: English
  • Course Delivery : E - Learning Access
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Course Overview

The Data Warehousing on AWS Course delivers in‑depth training on cloud‑based data warehousing using Amazon Web Services. The curriculum emphasizes Amazon Redshift, offering learners a comprehensive understanding of how to launch and configure Redshift clusters, design efficient schemas, and load and prepare data for analysis. Through structured instruction and practical labs, participants gain the skills needed to support data‑driven decision‑making and analytics initiatives within their organizations.

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Course Details

  • Understand core data warehousing concepts and AWS cloud integration
  • Gain hands on experience with Amazon Redshift cluster setup and management
  • Learn to design efficient database schemas tailored for analytics
  • Master data ingestion and transformation workflows
  • Support scalable, high performance analytics workloads in AWS environments
  • Enhance professional capabilities in data engineering and analytics roles
  • Suitable for data engineers, database administrators, BI professionals, analytics architects, and data analysts
  • Foundational understanding of databases and data warehousing concepts enhances learning
  • Familiarity with AWS basics (e.g., AWS Technical Essentials) is recommended but not mandatory
  • Practical experience with SQL and cloud environments is beneficial
  • Explain data warehousing fundamentals and cloud data architecture
  • Deploy and configure Amazon Redshift clusters
  • Design effective schemas and optimize warehouse structures
  • Load, transform, and prepare data using AWS services in conjunction with Redshift
  • Implement performance tuning and data management strategies
  • Integrate data warehousing processes into analytics and reporting workflows
  • Introduction to data warehousing and its role in analytics
  • Amazon Redshift architecture and key features
  • Launching and configuring Redshift clusters
  • Schema design principles for analytics workloads
  • Data loading and transformation techniques
  • Performance optimization and resource management
  • Integration of AWS data services for analytics workflows
  • Hands on labs reinforcing real world data warehouse scenarios
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What Our Corporate Clients Say

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James Abot
★★★★★

Much obliged to you for this course. I get know understanding and information in utilizing various types of online apparatuses which are helpful and viable. I'll utilize some of them during my exercises. Also, heaps of much obliged.

Sayali Patil
★★★★★

This was a very immersive and interesting course from NovelVista a lot of self-learning to be done on your own to really understand and put together into practice the technology into your own course and workflow.

Amit Shrivastav
★★★★★

It was truly an amazing learning session. I did have my apprehensions before signing up, but trainer made me feel so comfortable from the time we started the session till the very end of it.Thanks for this amazing experience.

Frequently Asked Questions

What is included in this course?+

Structured modules, Amazon Redshift walkthroughs, hands on labs, and analytics oriented exercises focused on data warehousing best practices.

Is there an associated certification exam?+

This course builds practical expertise in AWS data warehousing. Specific AWS certifications aligned with analytics and database roles may be pursued independently.

Who should enroll?+

Data engineers, BI developers, analytics professionals, database administrators, and cloud architects aiming to implement data warehousing solutions on AWS.

How is the course delivered?+

Training is available through digital self paced, virtual instructor led, and classroom formats via Koenig Solutions.

Do I need AWS experience?+

Basic cloud and database knowledge enhances the learning experience but is not strictly required.

Is hands on practice included?+

Yes — applied labs and real world scenario exercises are central to the curriculum.