Data Engineering using AWS Data Analytics


Title: Data Engineering using AWS Data Analytics

Course Description:

Welcome to the “Data Engineering using AWS Data Analytics” course – a comprehensive program designed to empower data engineers and professionals with the skills to design, build, and manage scalable data processing solutions using Amazon Web Services (AWS) data analytics services. Whether you’re a beginner looking to enter the field of data engineering or an experienced professional seeking to enhance your AWS skills, this course covers the entire spectrum of data engineering, focusing on AWS tools and services for effective data processing and analytics.



Module 1: Introduction to AWS Data Analytics (Week 1-2)

Embark on your AWS data engineering journey with an introduction to key concepts and services. Understand the AWS ecosystem and its role in data analytics.

Module 2: AWS Data Storage Services (Weeks 3-4)

Dive into AWS data storage services. Explore S3, DynamoDB, and other storage solutions, learning how to store and manage data effectively.

Module 3: Data Processing with AWS Glue (Weeks 5-6)

Master data processing using AWS Glue. Understand how to create and manage ETL (Extract, Transform, Load) jobs to prepare data for analysis.

Module 4: Real-time Data Streaming with Kinesis (Weeks 7-8)

Explore real-time data streaming using Amazon Kinesis. Learn to ingest, process, and analyze streaming data for immediate insights.

Module 5: Batch Processing with AWS Batch (Weeks 9-10)

Delve into batch processing using AWS Batch. Understand how to process large volumes of data efficiently and cost-effectively.

Module 6: Data Warehousing with Amazon Redshift (Weeks 11-12)

Master data warehousing with Amazon Redshift. Learn to design and manage a scalable data warehouse for analytics and reporting.

Module 7: Serverless Data Analytics with AWS Athena (Weeks 13-14)

Explore serverless data analytics using AWS Athena. Understand how to query data directly from S3 without the need for a traditional database.

Module 8: Building Data Lakes with AWS Lake Formation (Weeks 15-16)

Understand the principles of building data lakes with AWS Lake Formation. Learn to organize, secure, and manage data lakes for analytics.

Module 9: Data Orchestration with AWS Step Functions (Weeks 17-18)

Dive into data orchestration using AWS Step Functions. Explore how to design and coordinate workflows for complex data processing tasks.

Module 10: Machine Learning Integration with AWS (Weeks 19-20)

Introduce machine learning into your data engineering workflow with AWS machine learning services. Learn to integrate ML models for advanced analytics.

Module 11: Data Security and Compliance (Weeks 21-22)

Understand the importance of data security and compliance in AWS data analytics. Learn best practices for securing sensitive information.

Module 12: Monitoring and Optimization of AWS Data Analytics (Weeks 23-24)

Explore monitoring and optimization techniques for AWS data analytics solutions. Understand how to ensure optimal performance and cost-effectiveness.

Module 13: Real-world AWS Data Engineering Applications (Weeks 25-26)

Apply your AWS data engineering skills to real-world scenarios. Explore case studies and practical applications, gaining hands-on experience in solving complex problems.

Module 14: Capstone AWS Data Engineering Project and Certification (Weeks 27-30)

Cap off the course by working on a comprehensive capstone project. Apply all the skills acquired throughout the course, receive personalized feedback, and earn a certification validating your proficiency in Data Engineering using AWS Data Analytics.

Enroll now in “Data Engineering using AWS Data Analytics” and unlock the full potential of AWS services for designing and managing robust data processing solutions. Elevate your skills and become a proficient AWS data engineer ready to tackle complex data challenges in diverse industries!


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