The Building Modern Data Analytics Solutions on AWS course spans 32 hours and is structured into four one-day, intermediate-level, instructor-led modules. This comprehensive course dives deep into Amazon Lake Formation, Amazon Glue, Amazon EMR, Amazon Kinesis, and Amazon Redshift, covering essential aspects of building and operating data analytics pipelines.

Course Breakdown:

• Day 1: Building Data Lakes on AWS – Gain foundational skills for setting up scalable data lakes using Amazon Lake Formation.
• Day 2: Building Batch Data Analytics Solutions on AWS – Learn how to build batch data analytics pipelines using Amazon Glue and EMR.
• Day 3: Building Data Analytics Solutions using Amazon Redshift – Explore data warehousing with Amazon Redshift for high-performance analytics.
• Day 4: Building Streaming Data Analytics Solutions on AWS – Focus on real-time data processing with Amazon Kinesis.

By the end of this course, participants will be proficient in creating and managing data pipelines, transforming data into actionable insights, and effectively utilizing AWS services for modern data analytics.

Practical Application:

Throughout the course, you’ll engage in hands-on labs, enabling you to apply your knowledge and optimize data analytics solutions in real-world scenarios.

Course Prerequisites

This course assumes:

• Intermediate-level knowledge of cloud computing concepts.
• Familiarity with AWS services and basic data analytics principles.

Target Audience

This course is ideal for IT professionals seeking to advance their skills in data lakes, batch and streaming data analytics, and Amazon Redshift, including:

• Data Engineers
• Data Analysts
• Data Scientists
• Database Administrators
• IT Managers
• Cloud Architects
• Business Intelligence Specialists
• Solution Architects
• AWS Developers
• Systems Engineers
• Data Architects
• IT Consultants
• DevOps Engineers
• Enterprise Architects

Why Choose us

⭢ Live Online Training (Duration : 32 Hours)

⭢ Guaranteed to run classes

⭢ Experienced & certified trainers

⭢ Query Handling session

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Learning Objectives

By the end of this course, learners will be able to:

• Comprehensive Understanding of Data Lakes: Understand the concepts and architecture of data lakes and how to build them using Amazon Lake Formation.
• Data Transformation with Amazon Glue: Learn how to utilize Amazon Glue for data transformation and cataloging in a data lake environment.
• Batch Data Processing: Set up and manage batch data processing using Amazon EMR and other AWS services.
• Optimized Data Warehousing with Amazon Redshift: Implement data warehousing solutions, focusing on data loading, querying, and performance tuning in Amazon Redshift.
• Real-time Data Processing with Amazon Kinesis: Master techniques for streaming data analytics and real-time data processing using Amazon Kinesis.
• Data Security and Compliance: Apply best practices for securing data and ensuring compliance with industry standards across AWS data analytics services.
• Scalable Data Pipelines: Build and operate scalable data analytics pipelines that turn raw data into actionable insights.
• Integration of AWS Services: Effectively integrate AWS services like Amazon Glue, EMR, Kinesis, and Redshift to create end-to-end data analytics pipelines.