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Our Building Modern Data Analytics Solutions on AWS course spans 32 hours and is structured into four one-day, intermediate-level instructor-led courses. This comprehensive course dives deep into Amazon Lake Formation, Amazon Glue, Amazon EMR, Amazon Kinesis, and Amazon Redshift, covering all essential aspects of building and operating data analytics pipelines.
Day 1 focuses on Building Data Lakes on AWS, providing foundational skills for setting up scalable data lakes.
Day 2 dives into Building Batch Data Analytics Solutions on AWS with Amazon Glue and EMR.
Day 3 covers Building Data Analytics Solutions using Amazon Redshift, enabling high-performance analytics.
Day 4 explores Building Streaming Data Analytics Solutions on AWS, focusing on real-time data processing with Kinesis.
Learning Objectives: By the end of this course, participants will be proficient in creating and managing data pipelines, transforming data into actionable insights, and utilizing AWS services effectively.
Practical Application: You'll not only learn the theory but also engage in hands-on labs to apply your knowledge—preparing you to optimize data analytics in real-world scenarios.
Join us to advance your skills and stay ahead
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
Minimum Required Prerequisites for Building Modern Data Analytics Solutions on AWS
To ensure you get the most out of the Building Modern Data Analytics Solutions on AWS course, we recommend that participants have the following foundational knowledge and skills:
Basic Understanding of Database and Data Warehousing Concepts: Familiarity with relational databases and data warehousing principles will help you better understand the course material.
Proficiency in Data Analytics: Prior exposure to data analytics techniques and tools is recommended for getting the best out of this intermediate-level course.
Fundamental AWS Knowledge: Experience with core AWS services such as Amazon S3, Amazon EC2, and IAM is essential. You should understand how to navigate the AWS Management Console and have basic AWS architecture knowledge.
Programming Skills: Basic programming knowledge is a plus, especially in scripting languages such as Python, which is commonly used in AWS data analytics solutions.
Familiarity with ETL Processes: Understanding ETL (Extract, Transform, Load) processes and experience in data transformation and data preparation tasks will be advantageous.
Basic Networking Concepts: Knowing the fundamentals of networking, including VPC, subnets, and security groups, will help you follow along with the deployment and management of AWS resources.
By meeting these prerequisites,
1. Introduction: The Building Modern Data Analytics Solutions on AWS course is ideal for IT professionals seeking to advance their skills in data lakes, batch and streaming data analytics, and Amazon Redshift.
2. Job Roles and Audience:
The Building Modern Data Analytics Solutions on AWS course is a 32-hour, intermediate-level training designed to deepen your understanding of Amazon Lake Formation, Amazon Glue, Amazon EMR, Amazon Kinesis, and Amazon Redshift for constructing data analytics pipelines efficiently.
1. Introduction: The Building Modern Data Analytics Solutions on AWS course is ideal for IT professionals seeking to advance their skills in data lakes, batch and streaming data analytics, and Amazon Redshift.
2. Job Roles and Audience:
The Building Modern Data Analytics Solutions on AWS course is a 32-hour, intermediate-level training designed to deepen your understanding of Amazon Lake Formation, Amazon Glue, Amazon EMR, Amazon Kinesis, and Amazon Redshift for constructing data analytics pipelines efficiently.
