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Data Warehousing on AWS

  • 24/7 Support
  • 2 Months
  • 25 Sessions

Course Description

Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon S3. Additionally, this course demonstrates how to use business intelligence tools to perform analysis on your data. 

This course will be delivered through a mix of Instructor-led Training (ILT) and hands-on Labs. This course allows you to test new skills and apply knowledge to your working environment through a variety of practical exercises.

OBJECTIVES

 Discuss the core concepts of data warehousing.

 Evaluate the relationship between Amazon Redshift and other big data systems

 Evaluate use cases for data warehousing workloads and review case studies that demonstrate implementation of AWS data and analytic services as part of a data warehousing solution.

 Choose an appropriate Amazon Redshift node type and size for your data needs.

 Discuss security features as they pertain to Amazon Redshift, such as encryption, IAM permissions, and database permissions.

 Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud.

 Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon S3, to contribute to the data warehousing solution.

 Evaluate approaches and methodologies for designing data warehouses.

 Identify data sources and assess requirements that affect the data warehouse design.

 Design the data warehouse to make effective use of compression, data distribution, and sort methods.

 Load and unload data and perform data maintenance tasks.

 Write queries and evaluate query plans to optimize query performance.

 Configure the database to allocate resources such as memory to query queues and define criteria to route certain types of queries to your configured query queues for improved processing.

 Use features and services, such as Amazon Redshift database audit logging, Amazon CloudTrail, Amazon CloudWatch, and Amazon Simple Notification Service (Amazon SNS), to audit, monitor, and receive event notifications about activities in the data warehouse.

 Prepare for operational tasks, such as resizing Amazon Redshift clusters and using snapshots to back up and restore clusters.

• Use a business intelligence (BI) application to perform data analysis and visualization tasks against your data.

One to One personalized training Schedule for Data Warehousing on AWS

EkasCloud provides flexible training to all it's student. Here is our training schedule. Incase you find these timings difficult, please let us know. We will try to arrange appropriate timings based on your Convenience.

30-05-2024 Thursday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session
01-06-2024 Saturday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session
03-06-2024 Monday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session
04-06-2024 Tuesday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session

Course Detail

Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon S3. Additionally, this course demonstrates how to use business intelligence tools to perform analysis on your data. 

This course will be delivered through a mix of Instructor-led Training (ILT) and hands-on Labs. This course allows you to test new skills and apply knowledge to your working environment through a variety of practical exercises.

OBJECTIVES

 Discuss the core concepts of data warehousing.

 Evaluate the relationship between Amazon Redshift and other big data systems

 Evaluate use cases for data warehousing workloads and review case studies that demonstrate implementation of AWS data and analytic services as part of a data warehousing solution.

 Choose an appropriate Amazon Redshift node type and size for your data needs.

 Discuss security features as they pertain to Amazon Redshift, such as encryption, IAM permissions, and database permissions.

 Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud.

 Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon S3, to contribute to the data warehousing solution.

 Evaluate approaches and methodologies for designing data warehouses.

 Identify data sources and assess requirements that affect the data warehouse design.

 Design the data warehouse to make effective use of compression, data distribution, and sort methods.

 Load and unload data and perform data maintenance tasks.

 Write queries and evaluate query plans to optimize query performance.

 Configure the database to allocate resources such as memory to query queues and define criteria to route certain types of queries to your configured query queues for improved processing.

 Use features and services, such as Amazon Redshift database audit logging, Amazon CloudTrail, Amazon CloudWatch, and Amazon Simple Notification Service (Amazon SNS), to audit, monitor, and receive event notifications about activities in the data warehouse.

 Prepare for operational tasks, such as resizing Amazon Redshift clusters and using snapshots to back up and restore clusters.

• Use a business intelligence (BI) application to perform data analysis and visualization tasks against your data.

Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon S3. Additionally, this course demonstrates how to use business intelligence tools to perform analysis on your data. 

This course will be delivered through a mix of Instructor-led Training (ILT) and hands-on Labs. This course allows you to test new skills and apply knowledge to your working environment through a variety of practical exercises.

WHO CAN JOIN

 Database architects

 Database administrators

 Database developers

 Data analysts and scientists

PREREQUISITES

 Courses taken: AWS Technical Essentials (or equivalent experience with AWS)

 Familiarity with relational databases and database design concepts

• We check your knowledge before we start the session.

• We build foundational topics first and core topics next.

• Theory classes with real time case study.

• Demo on every topic.

•  You will learn how to design architecture diagrams on each service.

• Mock exam on every topic you learn.

•  Exam Preparation

• Interview Preparation

Data Warehousing on AWS Syllabus


2 Months Course 20% Theory 80% Lab Daily Home work 10 Real time Projects Unlimited Mock Exams Unlimited Mock Interview

Topics Covered

 Course Introduction

 Introduction to Data Warehousing

 Introduction to Amazon Redshift

 Understanding Amazon Redshift Components and Resources

 Launching an Amazon Redshift Cluster

 Reviewing Data Warehousing Approaches

 Identifying Data Sources and Requirements

 Designing the Data Warehouse

 Loading Data into the Data Warehouse

 Writing Queries and Tuning Performance

 Maintaining the Data Warehouse

 Analyzing and Visualizing Data

 Course Summary

Instructor

Vijilin Jerrish

jerrish has a Bachelors degree in Computer Science, He is based out of India and had trained more than 8000 students in last 9years on Networking, Linux, Automation and Cloud Technologies.

Jerrish is RHCSA, Ansible, AWS Solutions Architect Pro, Terraform and Kubernetes Certified

Frequently asked question

Q:  What if I miss a class?
A:  We will stop the course for you, because it is one-to-one or one-to-two students only.

Q: Will I get placement assistance?
A: Yes.  you will get it, once you Certified form the AWS.

Q: What if I have queries after I complete this course?
A: You can check our blog, or send your queries from social media like Facebook, Linkedin, Instagram, Twitter, and Youtube.

Q: How soon after signing up will I get access to the course
A: Once you join the course, our Counselor Manger book a slot with a our Trainer based on your and Trainer availableTime.

Q: Is the course material accessible to the students even after the course training is over?
A: Yes, you can access our course material. Not only material you can watch short and sweet videos from our Youtube Channel.

Q: What is the average salary of an AWS certified professional?
A: $129,868

Q: How will this course help me pass the AWS certification exam?
A: We provide a separate Training for Exam and Interviews. In this Training will get more experience to get pass Your AWS Exam.


Data Warehousing on AWS Fees