AWS Data Engineering with Data Analytics Online Training in Hyderabad
                     AWS Data Engineering with 
Data Analytics: Course 
Overview
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1. Introduction to AWS Data Engineering
• In this foundational section, students are introduced to the core concepts 
of data engineering within the AWS ecosystem. It begins with an overview 
of the role of a data engineer and the importance of data engineering in 
modern organizations. The section covers key AWS services that are 
central to data engineering, such as Amazon S3, AWS Glue, Amazon 
Redshift, and Amazon RDS. Students will gain an understanding of how 
these services fit into the data lifecycle, from ingestion and storage to 
processing and analysis.
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2. Data Ingestion and Storage
• This module dives into the various methods and services available for ingesting 
data into AWS. Topics include batch ingestion using AWS Data Pipeline, real-time 
ingestion using Amazon Kinesis, and file-based ingestion using AWS Transfer 
Family. Students will learn how to design and implement data ingestion pipelines 
that are scalable, reliable, and secure. The module also covers best practices for 
data storage, focusing on Amazon S3 as the primary data lake storage solution. 
Students will learn how to structure and manage data in S3, as well as how to 
optimize it for performance and cost.
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3. Data Transformation and Processing
• Once data is ingested and stored, it needs to be transformed and processed to be 
useful for analytics. This section covers data transformation techniques using AWS 
Glue and AWS Lambda. Students will learn how to use AWS Glue's ETL (Extract, 
Transform, Load) capabilities to clean, enrich, and format data. The course also 
explores serverless data processing with AWS Lambda, highlighting how it can be 
used to automate and scale data workflows. The use of Amazon EMR for big data 
processing with Hadoop and Spark is also covered, providing students with hands-
on experience in processing large datasets.
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4. Data Warehousing with Amazon Redshift
• Amazon Redshift is a key component in the AWS data engineering toolkit, 
designed for scalable and high-performance data warehousing. This module 
provides an in-depth look at how to design, implement, and manage a data 
warehouse using Redshift. Students will learn about data modeling, schema 
design, and query optimization. The course will also cover how to load data 
into Redshift from various sources, and how to integrate Redshift with other 
AWS analytics services such as Amazon QuickSight for data visualization.
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5. Data Analytics and Visualization
• Data engineering is closely tied to data analytics, and this 
module focuses on how to extract insights from data 
using AWS services. Students will explore Amazon Athena 
for ad-hoc querying of data stored in S3, and how to use 
Amazon QuickSight for creating interactive dashboards 
and visualizations. The course also covers machine 
learning integration, with an introduction to Amazon 
SageMaker for building predictive models based on the 
data processed through the engineering pipelines.
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6. Security and Compliance
• Security is a critical aspect of data engineering, especially 
when dealing with large volumes of sensitive data. This 
module emphasizes best practices for securing data at 
rest and in transit within the AWS environment. Topics 
include IAM roles and policies for fine-grained access 
control, encryption using AWS KMS, and setting up VPCs 
for network security. The course also covers compliance 
frameworks like GDPR and HIPAA, ensuring that students 
understand how to build compliant data pipelines.
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7. Monitoring and Optimization
• Effective data engineering requires continuous 
monitoring and optimization of data pipelines. In this 
section, students will learn how to use AWS CloudWatch 
for monitoring and alerting on the performance of data 
workflows. The course also covers cost management 
strategies, helping students understand how to optimize 
their use of AWS resources to minimize costs while 
maintaining high performance.
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8. Capstone Project
• The course culminates in a capstone project where 
students apply what they have learned to build a 
complete data engineering solution on AWS. This project 
involves designing and implementing a data pipeline that 
ingests, processes, and analyzes data, followed by 
creating a final report or dashboard. This hands-on 
experience solidifies the students' understanding and 
prepares them for real-world data engineering 
challenges.
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Conclusion
• The "AWS Data Engineering with Data Analytics" course provides a 
comprehensive guide to mastering data engineering on AWS. By 
covering the full data lifecycle—from ingestion and storage to 
transformation, analysis, and visualization—students will be equipped 
with the skills needed to build and manage robust data solutions in 
the cloud.
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CONTACT
For More Information About 
AWS Data Engineering Online Training 
                 Address:  Flat no: 205, 2nd Floor
                                                             Nilagiri Block, Aditya Enclave,
                                                             Ameerpet, Hyderabad-16
                                             Ph No:  +91-9989971070
                                                Visit:  www.visualpath.in
                                            E-Mail:  [email protected]
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